UNIT I: DIGITAL IMAGE
FUNDAMENTALS
Elements of a digital image processing system – structure of the human eye – image
formation and contrast sensitivity – sampling and quantization – neighbors of pixel –
distance measure – photographic film structure and exposure – film characteristics – linear
scanner – video camera – image processing applications.
UNIT II: IMAGE
TRANSFORMS
Introduction to Fourier transform – DFT – properties of two-dimensional FT –
separability,
translation, periodicity, rotation, average value – FFT algorithm – Walsh transform –
Hadamard transform – discrete cosine transform.
UNIT III: IMAGE ENHANCEMENT
Definition – spatial domain methods – frequency domain methods – histogram –
modification techniques – neighborhood averaging – median filtering – low pass filtering
– averaging of multiple images – image sharpening by differentiation and high pass
filtering.
UNIT IV: IMAGE ENCODING
Objective and subjective fidelity criteria – basic encoding process – the mapping – the
quantizer– the coder – differential – encoding – contour encoding – run length encoding image encoding – relative to fidelity criterion – differential pulse code modulation.
UNIT V: IMAGE ANALYSIS AND COMPUTER VISION
Typical computer vision system – image analysis techniques – spatial feature
extraction – amplitude and histogram features - transforms features – edge detection –
gradient operators – boundary extraction – edge linking – boundary representation –
boundary matching – shape representation.
TEXT
BOOK
1. Rafael C. Gonzalez, Paul Wintz, “Digital Image Processing”, Addison-Westley
Publishing
Company, 1987
2. Rafael C. Gonzalez, Richard E Woods “Digital Image Processing”,
Pearson, 2001
UNIT 1
Elements of a Digital Image Processing System
This easy-to-follow textbook provides a modern, algorithmic introduction to digital image processing,
designed to be used both by learners desiring a firm foundation on which to build, and practitioners in
search of critical analysis and concrete implementations of the most important techniques. The text
compiles the key elements of digital image processing, starting from the basic concepts and elementary
properties of digital images through simple statistics and point operations, fundamental filtering
techniques, localization of edges and contours, and basic operations on color images. Mastering these
most commonly used techniques will enable the reader to start being productive straight away.
The Anatomy of The Eye
The anatomy and physiology of the human eye is an important part of many courses
(e.g. in biology, human biology, physics, and practical courses in medicine, nursing, and
therapies).
This page is a very basic introduction the subjects of "The Eye" and "Visual Optics" more
generally.
It includes a simple diagram of the eye together with definitions of the parts of the eye labelled in
the illustration.
Term
Definition / Description
Aqueous
Humour
The aqueous humour is a jelly-like substance located in the anterior chamber of the
eye.
... more about the Aqueous Humour
The choroid layer is located behind the retina and absorbs unused radiation.
... more about the Choroid
The ciliary muscle is a ring-shaped muscle attached to the iris.
It is important because contraction and relaxation of the ciliary muscle controls the
shape of the lens.
... more about the Ciliary Muscle
The cornea is a strong clear bulge located at the front of the eye (where it replaces
the sclera - that forms the outside surface of the rest of the eye).
The front surface of the adult cornea has a radius of approximately 8mm.
The cornea contributes to the image-forming process by refracting light entering the
eye.
... more about the Cornea
The fovea is a small depression (approx. 1.5 mm in diameter) in the retina.
This is the part of the retina in which high-resolution vision of fine detail is possible.
... more about the Fovea
The hyaloid diaphragm divides the aqueous humour from the vitreous humour.
Choroid
Ciliary
Muscle
Cornea
Fovea
Hyaloid
... more about the Hyaloid Membrane
The iris is a diaphragm of variable size whose function is to adjust the size of the
Iris
pupil to regulate the amount of light admitted into the eye.
The iris is the coloured part of the eye (illustrated in blue above but in nature may
be any of many shades of blue, green, brown, hazel, or grey).
... more about the Iris
The lens of the eye is a flexible unit that consists of layers of tissue enclosed in a
Lens
tough capsule. It is suspended from the ciliary muscles by the zonule fibers.
... more about the Lens
The optic nerve is the second cranial nerve and is responsible for vision.
Optic
Each nerve contains approx. one million fibres transmitting information from the
Nerve
rod and cone cells of the retina.
... more about the Optic Nerve
The papilla is also known as the "blind spot" and is located at the position from
Papilla
which the optic nerve leaves the retina.
... more about the Optic Papilla
The
pupil
is
the
aperture
through
which
light
and
hence
the images we "see" and
Pupil
"perceive" - enters the eye. This is formed by the iris. As the size of the iris
increases (or decreases) the size of the pupil decreases (or increases)
correspondingly.
... more about the Pupil
The retina may be described as the "screen" on which an image is formed by light
Retina
that has passed into the eye via the cornea, aqueous humour, pupil, lens, then the
hyaloid and finally the vitreous humour before reaching the retina.
The retina contains photosensitive elements (called rods and cones) that convert the
light they detect into nerve impulses that are then sent onto the brain along the optic
nerve.
... more about the Retina
The sclera is a tough white sheath around the outside of the eye-ball.
Sclera
This is the part of the eye that is referred to by the colloquial terms "white of the
eye".
... more about the Sclera
Visual Axis A simple definition of the "visual axis" is "a straight line that passes through both
the centre of the pupil and the centre of the fovea". However, there is also a stricter
definition (in terms of nodal points) which is important for specialists in optics and
related subjects.
... more about the Visual Axis
Vitreous The vitreous humour (also known as the "vitreous body") is a jelly-like substance.
Humour
... more about the Vitreous Humour
Zonules
The zonules (or "zonule fibers") attach the lens to the ciliary muscles.
... more about the Zonules
To learn more about the eye and how it works it is useful to understand some simple but
important aspects of ligh
The ultimate stage in most optical imaging systems is the
formation of an image on the retina, and the design of most
optical systems takes this important fact into account.
For example, the light output from optical systems is
often limited (or should be limited) to the portion of
the spectrum to which we are most sensitive (i.e., the
visible spectrum). The light level of the final image is
within a range that is not too dim or bright. Exit pupils
of microscopes and binoculars are matched to typical
pupil sizes, and images are often produced at a suitable
magnification, so that they are easily resolved. We even
incorporate focus adjustments that can adapt when the
user is near- or farsighted. Of course, it is understandable
that our man-made environment is designed to fit within
our sensory and physical capabilities. But this process
is not complete. There is still much to know about the
optical system of the human eye, and as we increase our
understanding of the eye, we learn better ways to present
visual stimuli, and to design instruments for which we are
the end users.
This article focuses on the way images are formed in
the eye and the factors in the optical system that influence
Types of connectivity
2-dimensional
4-connected
4-Connected pixels are neighbors to every pixel that touches one of their edges. These pixels are
connected horizontally and vertically. In terms of pixel coordinates, every pixel that has the
coordinates
or
is connected to the pixel at
.
6-connected
6-connected pixels are neighbors to every pixel that touches one of their corners (which includes
pixels that touch one of their edges) in a hexagonal grid or stretcher bond rectangular grid.
There are several ways to map hexagonal tiles to integer pixel coordinates. With one method, in
addition to the 4-connected pixels, the two pixels at coordinates
are connected to the pixel at
and
.
8-connected
8-connected pixels are neighbors to every pixel that touches one of their edges or corners. These
pixels are connected horizontally, vertically, and diagonally. In addition to 4-Connected pixels,
each pixel with coordinates
or
is connected to the pixel at
.
3-dimensional
6-connected
6-connected pixels are neighbors to every pixel that touches one of their faces. These pixels are
connected along one of the primary axes. Each pixel with coordinates
, or
is connected to the pixel at
,
.
18-connected
18-connected pixels are neighbors to every pixel that touches one of their faces or edges. These
pixels are connected along either one or two of the primary axes. In addition to 6-Connected
pixels, each pixel with coordinates
,
,
,
connected to the pixel at
,
, or
is
.
26-connected
26-connected pixels are neighbors to every pixel that touches one of their faces, edges, or
corners. These pixels are connected along either one, two, or all three of the primary axes. In
addition to 18-Connected pixels, each pixel with coordinates
,
,
,
,
,
,
, or
is connected to the pixel at
Distance Measure
Based on the digital image processing theory, a newmethod of measuring the leading vehicle
distance wasproposed. The input image using the method of edgeenhancement and
morphological transformation wasestablished, so the edges of objects were enhanced to
identify.The target vehicle was identified and calibrated in the imageby using the method of the
obstacle detection by segmentationand decision tree. The relationship between coordinates
valuein image space and the data of the real space plane wasestablished by applying the ray
angles. Thus, throughaccessing to image pixel coordinates of the vehicle, the vehicle'sactual
position in the plane can be calculated. At last, theleading vehicle distance based on the
calculating model ofinverse perspective mapping was measured. By using softwareVC++, an
experiment program was made. The experimentresults prove that the method of measuring the
leading vehicledistance is simple and effective. It can meet the requirement ofintelligent vehicle
technologies. It is an more available andmore advanced method to calculate the leading
vehicledistance.
Keywords-Active Safety; Leading Vehicle Distance; DigitalImage Processing; Obstacle
Detection; Monocular Ranging
Medical images are recorded either in digital format on some form of digital media or
on photographic film. Here we consider the process of recording on film. The active
component of film is an emulsion of radiation-sensitive crystals coated onto a
transparent base material. The production of an image requires two steps, as illustrated
below. First, the film is exposed to radiation, typically light, which activates the
emulsion material but produces no visible change. The exposure creates a so-called
latent image. Second, the exposed film is processed in a series of chemical solutions
that convert the invisible latent image into an image that is visible as different optical
densities or shades of gray. The darkness or density of the film increases as the
exposure is increased. This general relationship is shown in the second following
figure.
The Two Steps in the Formation of a Film Image
The General Relationship between Film Density (Shades of Gray) and Exposure
The specific relationship between the shades of gray or density and exposure
depends on the characteristics of the film emulsion and the processing conditions. The
basic principles of the photographic process and the factors that affect the sensitivity
of film are covered in this chapter.
FILM FUNCTIONS
CONTENTS
Film performs several functions in the medical imaging process. A
knowledge of these functions and how they are affected by the characteristics
of different types of film aids in selecting film for a specific clinical
procedure and in optimizing radiographic techniques.
Image Recording
CONTENTS
In principle, film is an image converter. It converts radiation, typically
light, into various shades of gray or optical density values. An important
characteristic of film is that it records, or retains, an image. An exposure of a
fraction of a second can create a permanent image. The amount of exposure
required to produce an image depends on the sensitivity, or speed, of the film
being used. Some films are more sensitive than others because of their design
or the way they are processed. The sensitivity of radiographic film is
generally selected to provide a compromise between two very important
factors: patient exposure and image quality, specifically image noise. A
highly sensitive film reduces patient exposure but decreases image quality
because of the increased quantum noise.
Image Display
CONTENTS
Most filmed medical images are recorded as transparencies. In this form
they can be easily viewed by trans-illumination on a viewbox. The overall
appearance and quality of a radiographic image depends on a combination of
factors, including the characteristics of the particular film used, the way in
which it was exposed, and the processing conditions. When a radiograph
emerges from the film processor, the image is permanent and cannot be
changed. It is, therefore, important that all factors associated with the
production of the image are adjusted to produce optimum image quality.
Image Storage
CONTENTS
Film has been the traditional medium for medical image storage and
archiving. If a film is properly processed it will have a lifetime of many years
and will, in most cases, outlast its clinical usefulness. The major
disadvantages of storing images on film are bulk and inaccessibility. Most
clinical facilities must devote considerable space to film storage. Retrieving
films from storage generally requires manual search and transportation of the
films to a viewing area.
Because film performs so many of the functions that make up the
radiographic examination, it will continue to be an important element in the
medical imaging process. Because of its limitations, however, it will be
replaced by digital imaging media in many clinical applications.
OPTICAL DENSITY
CONTENTS
Optical density is the darkness, or opaqueness, of a transparency film and is
produced by film exposure and chemical processing. An image contains areas
with different densities that are viewed as various shades of gray.
CONTENT
S
The optical density of film is assigned numerical values related to the amount of
light that penetrates the film. Increasing film density decreases light penetration. The
relationship between density values and light penetration is exponential, as shown
below.
Light Penetration
Relationship between Light Penetration and Film Density
A clear piece of film that allows 100% of the light to penetrate has a density value
of 0. Radiographic film is never completely clear. The minimum film density is
usually in the range of 0.1 to 0.2 density units. This is designated the base plus fog
density and is the density of the film base and any inherent fog not associated with
exposure.
Each unit of density decreases light penetration by a factor of 10. A film area with a
density value of 1 allows 10% of the light to penetrate and generally appears as a
medium gray when placed on a conventional viewbox. A film area with a density
value of 2 allows 10% of 10% (1.0%) light penetration and appears as a relatively
dark area when viewed in the usual manner. With normal viewbox illumination, it is
possible to see through areas of film with density values of up to approximately 2
units.
A density value of 3 corresponds to a light penetration of 0.1% (10% of 10% of
10%). A film with a density value of 3 appears essentially opaque when transilluminated with a conventional viewbox. It is possible, however, to see through such
a film using a bright "hot" light. Radiographic film generally has a maximum density
value of approximately 3 density units. This is designated the Dmax of the film. The
maximum density that can be produced within a specific film depends on the
characteristics of the film and processing conditions.
Measurement
CONTENTS
The density of film is measured with a densitometer. A light source passes
a small beam of light through the film area to be measured. On the other side
of the film, a light sensor (photocell) converts the penetrated light into an
electrical signal. A special circuit performs a logarithmic conversion on the
signal and displays the results in density units.
The primary use of densitometers in a clinical facility is to monitor the
performance of film processors.
CONTENT
S
Conventional film is layered, as illustrated in the following figure. The active
component is an emulsion layer coated onto a base material. Most film used in
radiography has an emulsion layer on each side of the base so that it can be used with
two intensifying screens simultaneously. Films used in cameras and in selected
radiographic procedures, such as mammography, have one emulsion layer and are
called single-emulsion films.
FILM STRUCTURE
Cross-Section of Typical Radiographic Film
Base
CONTENTS
The base of a typical radiographic film is made of a clear polyester material
about 150 µm thick. It provides the physical support for the other film
components and does not participate in the image-forming process. In some
films, the base contains a light blue dye to give the image a more pleasing
appearance when illuminated on a viewbox.
Emulsion
CONTENTS
The emulsion is the active component in which the image is formed and
consists of many small silver halide crystals suspended in gelatin. The gelatin
supports, separates, and protects the crystals. The typical emulsion is
approximately 10 µm thick.
Several different silver halides have photographic properties, but the one
typically used in medical imaging films is silver bromide. The silver bromide
is in the form of crystals, or grains, each containing on the order of 109
atoms.
Silver halide grains are irregularly shaped like pebbles, or grains of sand.
Two grain shapes are generally used in film emulsions. One form
approximates a cubic configuration with its three dimensions being
approximately equal. Another form is tabular-shaped grains. The tabular
grain is relatively thin in one direction, and its length and width are much
larger than its thickness, giving it a relatively large surface area. The primary
advantage of tabular grain film in comparison to cubic grain film is that
sensitizing dyes can be used more effectively to increase sensitivity and
reduce crossover exposure.
CONTENT
S
The production of film density and the formation of a visible image is a two step
process. The first step in this photographic process is the exposure of the film to light,
which forms an invisible latent image. The second step is the chemical process that
converts the latent image into a visible image with a range of densities, or shades of
gray.
THE PHOTOGRAPHIC PROCESS
Film density is produced by converting silver ions into metallic silver, which causes
each processed grain to become black. The process is rather complicated and is
illustrated by the sequence of events shown below.
Sequence of Events That Convert a Transparent Film Grain into Black Metallic
Silver
Each film grain contains a large number of both silver and bromide ions. The silver
ions have a one-electron deficit, which gives them a positive charge. On the other
hand, the bromide ions have a negative charge because they contain an extra electron.
Each grain has a structural "defect" known as a sensitive speck. A film grain in this
condition is relatively transparent.
CONTENT
S
The invisible latent image is converted into a visible image by the chemical process
of development. The developer solution supplies electrons that migrate into the
sensitized grains and convert the other silver ions into black metallic silver. This
causes the grains to become visible black specks in the emulsion.
Development
Radiographic film is generally developed in an automatic processor. A schematic of
a typical processor is shown below. The four components correspond to the four steps
in film processing. In a conventional processor, the film is in the developer for 20 to
25 seconds. All four steps require a total of 90 seconds.
A Film Processor
When a film is inserted into a processor, it is transported by means of a roller system
through the chemical developer. Although there are some differences in the chemistry
of developer solutions supplied by various manufacturers, most contain the same basic
chemicals. Each chemical has a specific function in the development process.
Reducer
Chemical reduction of the exposed silver bromide grains is the process that converts
them into visible metallic silver. This action is typically provided by two chemicals in
the solution: phenidone and hydroquinone. Phenidone is the more active and primarily
produces the mid to lower portion of the gray scale. Hydroquinone produces the very
dense, or dark, areas in an image.
Activator
The primary function of the activator, typically sodium carbonate, is to soften and
swell the emulsion so that the reducers can reach the exposed grains.
Restrainer
Potassium bromide is generally used as a restrainer. Its function is to moderate the
rate of development.
Preservative
Sodium sulfite, a typical preservative, helps protect the reducing agents from
oxidation because of their contact with air. It also reacts with oxidation products to
reduce their activity.
Hardener
Glutaraldehyde is used as a hardener to retard the swelling of the emulsion. This is
necessary in automatic processors in which the film is transported by a system of
rollers.
Fixing
CONTENTS
After leaving the developer the film is transported into a second tank,
which contains the fixer solution. The fixer is a mixture of several chemicals
that perform the following functions.
Neutralizer
When a film is removed from the developer solution, the development
continues because of the solution soaked up by the emulsion. It is necessary
to stop this action to prevent overdevelopment and fogging of the film. Acetic
acid is in the fixer solution for this purpose.
Clearing
The fixer solution also clears the undeveloped silver halide grains from the
film. Ammonium or sodium thiosulfate is used for this purpose. The
unexposed grains leave the film and dissolve in the fixer solution. The silver
that accumulates in the fixer during the clearing activity can be recovered; the
usual method is to electroplate it onto a metallic surface within the silver
recovery unit.
Preservative
Sodium sulfite is used in the fixer as a preservative.
Hardener
Aluminum chloride is typically used as a hardener. Its primary function is
to shrink and harden the emulsion.
Wash
CONTENTS
Film is next passed through a water bath to wash the fixer solution out of
the emulsion. It is especially important to remove the thiosulfate. If
thiosulfate (hypo) is retained in the emulsion, it will eventually react with the
silver nitrate and air to form silver sulfate, a yellowish brown stain. The
amount of thiosulfate retained in the emulsion determines the useful lifetime
of a processed film. The American National Standard Institute recommends a
maximum retention of 30 µg/in2.
Dry
CONTENTS
The final step in processing is to dry the film by passing it through a
chamber in which hot air is circulating.
CONTENT
S
One of the most important characteristics of film is its sensitivity, often referred to
as film speed. The sensitivity of a particular film determines the amount of exposure
required to produce an image. A film with a high sensitivity (speed) requires less
exposure than a film with a lower sensitivity (speed).
SENSITIVITY
The sensitivities of films are generally compared by the amount of exposure
required to produce an optical density of 1 unit above the base plus fog density. The
sensitivity of radiographic film is generally not described with numerical values but
rather with a variety of generic terms such as "half speed," "medium speed," and "high
speed." Radiographic films are usually considered in terms of their relative
sensitivities rather than their absolute sensitivity values. Although it is possible to
choose films with different sensitivities, the choice is limited to a range of not more
than four to one by most manufacturers.
The following figure compares two films with different sensitivities. Notice that a
specific exposure, indicated by the relative exposure step values, produces a higher
density in the high sensitivity film; therefore, the production of a specific density
value (i.e., 1 density unit) requires less exposure.
Comparison of Two Films with Different Sensitivities
High sensitivity (speed) films are chosen when the reduction of patient exposure and
heat loading of the x-ray equipment are important considerations.
Low sensitivity (speed) films are used to reduce image noise. The relationship of
film sensitivity to image noise is considered in the section titled, "Image Noise."
The sensitivity of film is determined by a number of factors, as shown in the
following figure, which include its design, the exposure conditions, and how it is
processed.
Factors That Affect Film Sensitivity
Composition
The basic sensitivity characteristic of a film is determined by the
composition of the emulsion. The size and shape of the silver halide grains
have some effect on film sensitivity. Increasing grain size generally increases
sensitivity. Tabular-shaped grains generally produce a higher sensitivity than
conventional grains. Although grain size may vary among the various types
of radiographic film, most of the difference in sensitivity is produced by
adding chemical sensitizers to the emulsion.
Processing
The effective sensitivity of film depends on several factors associated with
the development:
• the type of developer
• developer concentration
• developer replenishment rates
• developer contamination
• development time
• development temperature.
In most medical imaging applications, the objective is not to use these
factors to chance or vary film sensitivity, but rather to control them to
maintain a constant and predictable film sensitivity.
Developer Composition
The processing chemistry supplied by different manufacturers is not the
same. It is usually possible to process a film in a variety of developer
solutions, but they will not all produce the same film sensitivity. The
variation in sensitivity is usually relatively small, but must be considered
when changing from one brand of developer to another.
Developer Concentration
Developer chemistry is usually supplied to a clinical facility in the form of
a concentrate that must be diluted with water before it is pumped into the
processor. Mixing errors that result in an incorrect concentration can produce
undesirable changes in film sensitivity.
Developer Replenishment
The film development process consumes some of the developer solution
and causes the solution to become less active. Unless the solution is replaced,
film sensitivity will gradually decrease.
In radiographic film processors, the replenishment of the developer solution
is automatic. When a sheet of film enters the processor, it activates a switch
that causes fresh solution to be pumped into the development tank. The
replenishment rate can be monitored by means of flow meters mounted in the
processor. The appropriate replenishment rate depends on the size of the
films being processed. A processor used only for chest films generally
requires a higher replenishment rate than one used for smaller films.
Developer Contamination
If the developer solution becomes contaminated with another chemical,
such as the fixer solution, abrupt changes in film sensitivity can occur in the
form of either an increase or decrease in sensitivity, depending on the type
and amount of contamination. Developer contamination is most likely to
occur when the film transport rollers are removed or replaced.
Development Time
When an exposed film enters the developer solution, development is not
instantaneous. It is a gradual process during which more and more film grains
are developed, resulting in increased film density. The development process
is terminated by removing the film from the developer and placing it in the
fixer. To some extent, increasing development time increases film sensitivity,
since less exposure is required to produce a specific film density. In most
radiographic film processors, the development time is usually fixed and is
approximately 20-25 seconds. However, there are two exceptions. So-called
rapid access film is designed to be processed faster in special processors.
Some (but not all) mammographic films will produce a higher contrast when
developed for a longer time in an extended cycle processor.
Development Temperature
The activity of the developer changes with temperature. An increase in
temperature speeds up the development process and increases film sensitivity
because less exposure is required to produce a specific film density.
The temperature of the developer is thermostatically controlled in an
automatic processor. It is usually set within the range of 90-95°F. Specific
processing temperatures are usually specified by the film manufacturers.
Light Color (Wavelength)
Film is not equally sensitive to all wavelengths (colors) of light. The spectral
sensitivity is a characteristic of film that must be taken into account in selecting
film for use with specific intensifying screens and cameras. In general, the film
should be most sensitive to the color of the light that is emitted by the
intensifying screens, intensifier tubes, cathode ray tubes (CRTs), or lasers.
Blue Sensitivity
A basic silver bromide emulsion has its maximum sensitivity in the ultraviolet
and blue regions of the light spectrum. For many years most intensifying
screens contained calcium tungstate, which emits a blue light and is a good
match for blue sensitive film. Although calcium tungstate is no longer widely
used as a screen material, several contemporary screen materials emit blue light.
Green Sensitivity
Several image light sources, including image intensifier tubes, CRTs, and
some intensifying screens, emit most of their light in the green portion of the
spectrum. Film used with these devices must, therefore, be sensitive to green
light.
Silver bromide can be made sensitive to green light by adding sensitizing dyes
to the emulsion. Users must be careful not to use the wrong type of film with
intensifying screens. If a blue-sensitive film is used with a green-emitting
intensifying screen, the combination will have a drastically reduced sensitivity.
Red Sensitivity
Many lasers produce red light. Devices that transfer images to film by means
of a laser beam must, therefore, be supplied with a film that is sensitive to red
light.
Safelighting
Darkrooms in which film is loaded into cassettes and transferred to processors
are usually illuminated with a safelight. A safelight emits a color of light the eye
can see but that will not expose film. Although film has a relatively low
sensitivity to the light emitted by safelights, film fog can be produced with
safelight illumination under certain conditions. The safelight should provide
sufficient illumination for darkroom operations but not produce significant
exposure to the film being handled. This can usually be accomplished if certain
factors are controlled. These include safelight color, brightness, location, and
duration of film exposure.
The color of the safelight is controlled by the filter. The filter must be selected
in relationship to the spectral sensitivity of the film being used. An amberbrown safelight provides a relatively high level of working illumination and
adequate protection for blue-sensitive film; type 6B filters are used for this
application. However, this type of safelight produces some light that falls within
the sensitive range of green-sensitive film.
A red safelight is required when working with green-sensitive films. Type
GBX filters are used for this purpose.
Selecting the appropriate safelight filter does not absolutely protect film
because film has some sensitivity to the light emitted by most safelights.
Therefore, the brightness of the safelight (bulb size) and the distance between
the light and film work surfaces must be selected so as to minimize film
exposure.
Since exposure is an accumulative effect, handling the film as short a time as
possible minimizes exposure. The potential for safelight exposure can be
evaluated in a darkroom by placing a piece of film on the work surface,
covering most of its area with an opaque object, and then moving the object in
successive steps to expose more of the film surface. The time intervals should
be selected to produce exposures ranging from a few seconds to several
minutes. After the film is processed, the effect of the safelight exposure can be
observed. Film is most sensitive to safelight fogging after the latent image is
produced but before it is processed.
Exposure Time
In radiography it is usually possible to deliver a given exposure to film by
using many combinations of radiation intensity (exposure rate) and exposure
time. Since radiation intensity is proportional to x-ray tube MA, this is
equivalent to saying that a given exposure (in milliampere-seconds) can be
produced with many combinations of MA and time. This is known as the law
of reciprocity. In effect, it means that it is possible to swap radiation intensity
(in milliamperes) for exposure time and produce the same film exposure.
When a film is directly exposed to x-radiation, the reciprocity law holds true.
That is, 100 mAs will produce the same film density whether it is exposed at
1,000 mA and 0.1 seconds or 10 mA and 10 seconds. However, when a film
is exposed by light, such as from intensifying screens or image intensifiers,
the reciprocity law does not hold. With light exposure, as opposed to direct xray interactions, a single silver halide grain must absorb more than one
photon before it can be developed and can contribute to image density. This
causes the sensitivity of the film to be somewhat dependent on the intensity
of the exposing light. This loss of sensitivity varies to some extent from one
type of x-ray film to another. The clinical significance is that MAS values
that give the correct density with short exposure times might not do so with
long exposure times.
PROCESSING QUALITY CONTROL
There are many variables, such as temperature and chemical activity, that can affect
the level of processing that a film receives. Each type of film is designed and
manufactured to have specified sensitivity (speed) and contrast characteristics.
Under processing
If a film is under processed its sensitivity and contrast will be reduced below the
specified values. The loss of sensitivity can usually be compensated for by increasing
exposure but the loss of contrast cannot be recovered.
Over processing
Over processing can increase sensitivity. The contrast of some films might increase
with over processing, up to a point, and then decrease. A major problem with over
processing is that it increases fog (base plus fog density) which contributes to a
decrease in contrast.
Processing Accuracy
The first step in processing quality control is to set up the correct processing
conditions and then verify that the film is being correctly processed.
Processing Conditions
A specification of recommended processing conditions (temperature, time, type of
chemistry, replenishment rates, etc.) should be obtained from the manufacturers of the
film and chemistry.
Processing Verification
After the recommended processing conditions are established for each type of film,
a test should be performed to verify that the film is producing the design sensitivity
and contrast characteristics as specified by the manufacturer. These specifications are
usually provided in the form of a film characteristic curve that can be compared to one
produced by the processor being evaluated.
Processing Consistency
The second step in processing quality control is to reduce the variability over time in
the level of processing.
Variations in processing conditions can produce significant differences in film
sensitivity. One objective of a quality control program is to reduce exposure errors
that cause either underexposed or overexposed film. Processors should be checked
several times each week to detect changes in processing. This is done by exposing a
test film to a fixed amount of light exposure in a sensitometer, running the film
through the processor, and then measuring its density with a densitometer. It is not
necessary to measure the density of all exposure steps. Only a few exposure steps are
selected, as shown in the figure below, to give the information required for processor
quality control. The density values are recorded on a chart (see the second figure
below) so that fluctuations can be easily detected.
Density Values from a Sensitometer Exposed Film Strip Used for Processor
Quality Control
A Processor Quality Control Chart
Base Plus Fog Density
One density measurement is made in an area that receives no exposure. This is a
measure of the base plus fog density. A low density value is desirable. An increase in
the base plus fog density can be caused by over processing a film.
Speed
A single exposure step that produces a film density of about 1 density unit (above
the base plus fog value) is selected and designated the "speed step." The density of
this same step is measured each day and recorded on the chart. The density of this step
is a general indication of film sensitivity or speed. Abnormal variations can be caused
by any of the factors affecting the amount of development.
Contrast
Two other steps are selected, and the difference between them is used as a measure
of film contrast. This is the contrast index. If the two sensitometer steps that are
selected represent a two-to-one exposure ratio (50% exposure contrast), the contrast
index is the same as the contrast factor discussed earlier. This value is recorded on the
chart to detect abnormal changes in film contrast produced by processing conditions.
If abnormal variations in film density are observed, all possible causes, such as
developer temperature, solution replenishment rates, and contamination, should be
evaluated.
If more than one processor is used for films from the same imaging device, the level
of development by the different processes should be matched.
Artifacts
A variety of artifacts can be produced during the storage, handling, and processing
of film.
Bending unprocessed film can produce artifacts or "kink marks," which can appear
as either dark or light areas in the processed image. Handling film, especially in a dry
environment, can produce a build-up of static electricity; the discharge produces dark
spots and streaks.
Artifacts can be produced during processing by factors such as uneven roller
pressure or the accumulation of a substance on the rollers. This type of artifact is often
repeated at intervals corresponding to the circumference of the roller.
Application of Digital Image Processing
Image processing is any form of signal processing for which
the input is an image, such as photographs or frames of
video; the output of image processing can be either an image
or a set of characteristics or parameters related to the image.
Hence there are many ways in which it can be beneficial,
hence discuss.
UNIT II: IMAGE TRANSFORMS
Introduction to Fourier transform – DFT – properties of two-dimensional FT –
separability,
translation, periodicity, rotation, average value – FFT algorithm – Walsh transform –
Hadamard transform – discrete cosine transform.
Fourier transform spectroscopy is a measurement technique whereby spectra are collected
based on measurements of the coherence of a radiative source, using time-domain or spacedomain measurements of the electromagnetic radiation or other type of radiation. It can be
applied to a variety of types of spectroscopy including optical spectroscopy, infrared
spectroscopy (FTIR, FT-NIRS), nuclear magnetic resonance (NMR) and magnetic resonance
spectroscopic imaging (MRSI),[1] mass spectrometry and electron spin resonance spectroscopy.
There are several methods for measuring the temporal coherence of the light (see: fieldautocorrelation), including the continuous wave Michelson or Fourier transform spectrometer
and the pulsed Fourier transform spectrograph (which is more sensitive and has a much shorter
sampling time than conventional spectroscopic techniques, but is only applicable in a laboratory
environment).
The term Fourier transform spectroscopy reflects the fact that in all these techniques, a Fourier
transform is required to turn the raw data into the actual spectrum, and in many of the cases in
optics involving interferometers, is based on the Wiener–Khinchin theorem.
n mathematics, the discrete Fourier transform (DFT) is a specific kind of discrete transform,
used in Fourier analysis. It transforms one function into another, which is called the frequency
domain representation, or simply the DFT, of the original function (which is often a function in
the time domain). The DFT requires an input function that is discrete. Such inputs are often
created by sampling a continuous function, such as a person's voice. The discrete input function
must also have a limited (finite) duration, such as one period of a periodic sequence or a
windowed segment of a longer sequence. Unlike the discrete-time Fourier transform (DTFT), the
DFT only evaluates enough frequency components to reconstruct the finite segment that was
analyzed. The inverse DFT cannot reproduce the entire time domain, unless the input happens to
be periodic. Therefore it is often said that the DFT is a transform for Fourier analysis of finitedomain discrete-time functions.
The input to the DFT is a finite sequence of real or complex numbers (with more abstract
generalizations discussed below), making the DFT ideal for processing information stored in
computers. In particular, the DFT is widely employed in signal processing and related fields to
analyze the frequencies contained in a sampled signal, to solve partial differential equations, and
to perform other operations such as convolutions or multiplying large integers. A key enabling
factor for these applications is the fact that the DFT can be computed efficiently in practice using
a fast Fourier transform (FFT) algorithm.
Relationship between the (continuous) Fourier transform and the discrete Fourier transform. Left
column: A continuous function (top) and its Fourier transform (bottom). Center-left column: Periodic
summation of the original function (top). Fourier transform (bottom) is zero except at discrete points.
The inverse transform is a sum of sinusoids called Fourier series. Center-right column: Original function
is discretized (multiplied by a Dirac comb) (top). Its Fourier transform (bottom) is a periodic summation
(DTFT) of the original transform. Right column: The DFT (bottom) computes discrete samples of the
continuous DTFT. The inverse DFT (top) is a periodic summation of the original samples. The FFT
algorithm computes one cycle of the DFT and its inverse is one cycle of the DFT inverse.
Illustration of using Dirac comb functions and the convolution theorem to model the effects of sampling
and/or periodic summation. The graphs on the right side depict the (finite) coefficients that modulate
the infinite amplitudes of a comb function whose teeth are spaced at the reciprocal of the time-domain
periodicity. The coefficients in the upper figure are computed by the Fourier series integral. The DFT
computes the coefficients in the lower figure. Its similarities to the original transform, S(f), and its
relative computational ease are often the motivation for computing a DFT. The lower left graph
represents a discrete-time Fourier transform (DTFT).
FFT algorithms are so commonly employed to compute DFTs that the term "FFT" is often used
to mean "DFT" in colloquial settings. Formally, there is a clear distinction: "DFT" refers to a
mathematical transformation or function, regardless of how it is computed, whereas "FFT" refers
to a specific family of algorithms for computing DFTs. The terminology is further blurred by the
(now rare) synonym finite Fourier transform for the DFT, which apparently predates the term
"fast Fourier transform" (Cooley et al., 1969) but has the same initialism.
The Fourier transform is a mathematical operation with many applications in physics and
engineering that expresses a mathematical function of time as a function of frequency, known as
its frequency spectrum; Fourier's theorem guarantees that this can always be done. For instance,
the transform of a musical chord made up of pure notes (without overtones) expressed as
amplitude as a function of time, is a mathematical representation of the amplitudes and phases of
the individual notes that make it up. The function of time is often called the time domain
representation, and the frequency spectrum the frequency domain representation. The inverse
Fourier transform expresses a frequency domain function in the time domain. Each value of the
function is usually expressed as a complex number (called complex amplitude) that can be
interpreted as a magnitude and a phase component. The term "Fourier transform" refers to both
the transform operation and to the complex-valued function it produces.
In the case of a periodic function, such as a continuous, but not necessarily sinusoidal, musical
tone, the Fourier transform can be simplified to the calculation of a discrete set of complex
amplitudes, called Fourier series coefficients. Also, when a time-domain function is sampled to
facilitate storage or computer-processing, it is still possible to recreate a version of the original
Fourier transform according to the Poisson summation formula, also known as discrete-time
Fourier transform. These topics are addressed in separate articles. For an overview of those and
other related operations, refer to Fourier analysis or List of Fourier-related transforms.
A discrete cosine transform (DCT) expresses a sequence of finitely many data points in terms
of a sum of cosine functions oscillating at different frequencies. DCTs are important to numerous
applications in science and engineering, from lossy compression of audio (e.g. MP3) and images
(e.g. JPEG) (where small high-frequency components can be discarded), to spectral methods for
the numerical solution of partial differential equations. The use of cosine rather than sine
functions is critical in these applications: for compression, it turns out that cosine functions are
much more efficient (as described below, fewer functions are needed to approximate a typical
signal), whereas for differential equations the cosines express a particular choice of boundary
conditions.
In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform
(DFT), but using only real numbers. DCTs are equivalent to DFTs of roughly twice the length,
operating on real data with even symmetry (since the Fourier transform of a real and even
function is real and even), where in some variants the input and/or output data are shifted by half
a sample. There are eight standard DCT variants, of which four are common.
The most common variant of discrete cosine transform is the type-II DCT, which is often called
simply "the DCT"; its inverse, the type-III DCT, is correspondingly often called simply "the
inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform (DST),
which is equivalent to a DFT of real and odd functions, and the modified discrete cosine
transform (MDCT), which is based on a DCT of overlapping data.
UNIT III: IMAGE ENHANCEMENT
Definition – spatial domain methods – frequency domain methods – histogram –
modification techniques – neighborhood averaging – median filtering – low pass filtering
– averaging of multiple images – image sharpening by differentiation and high pass
filtering.
Image Enhancement
Image enhancement is the improvement of digital image quality (wanted e.g. for visual
inspection or for machine analysis), without knowledge about the source of degradation. If the
source of degradation is known, one calls the process image restoration. Both are iconical
processes, viz. input and output are images.
Many different, often elementary and heuristic methods are used to improve images in some
sense. The problem is, of course, not well defined, as there is no objective measure for image
quality. Here, we discuss a few recipes that have shown to be useful both for the human observer
and/or for machine recognition. These methods are very problem-oriented: a method that works
fine in one case may be completely inadequate for another problem.
Apart from geometrical transformations some preliminary greylevel adjustments may be
indicated, to take into account imperfections in the acquisition system. This can be done pixel by
pixel, calibrating with the output of an image with constant brightness. Frequently spaceinvariant greyvalue transformations are also done for contrast stretching, range compression, etc.
The critical distribution is the relative frequency of each greyvalue, the greyvalue histogram .
Examples of simple greylevel transformations in this domain are:
Greyvalues can also be modified such that their histogram has any desired shape, e.g flat (every
greyvalue has the same probability). All examples assume point processing, viz. each output
pixel is the function of one input pixel; usually, the transformation is implemented with a lookup table:
Physiological experiments have shown that very small changes in luminance are recognized by
the human visual system in regions of continuous greyvalue, and not at all seen in regions of
some discontinuities. Therefore, a design goal for image enhancement often is to smooth images
in more uniform regions, but to preserve edges. On the other hand, it has also been shown that
somehow degraded images with enhancement of certain features, e.g. edges, can simplify image
interpretation both for a human observer and for machine recognition. A second design goal,
therefore, is image sharpening. All these operations need neighbourhood processing, viz. the
output pixel is a function of some neighbourhood of the input pixels:
These operations could be performed using linear operations in either the frequency or the spatial
domain. We could, e.g. design, in the frequency domain, one-dimensional low or high pass filters
( Filtering), and transform them according to McClellan's algorithm ([McClellan73] to the
two-dimensional case.
Unfortunately, linear filter operations do not really satisfy the above two design goals; in this
book, we limit ourselves to discussing separately only (and superficially) Smoothing and
Sharpening.
Here is a trick that can speed up operations substantially, and serves as an example for both point
and neighbourhood processing in a binary image: we number the pixels in a
neighbourhood like:
and denote the binary values (0,1) by bi (i = 0,8); we then concatenate the bits into a 9-bit word,
like b8b7b6b5b4b3b2b1b0. This leaves us with a 9-bit greyvalue for each pixel, hence a new image
(an 8-bit image with b8 taken from the original binary image will also do). The new image
corresponds to the result of a convolution of the binary image, with a
matrix containing as
coefficients the powers of two. This neighbour image can then be passed through a look-up table
to perform erosions, dilations, noise cleaning, skeletonization, etc.
Apart from point and neighbourhood processing, there are also global processing techniques, i.e.
methods where every pixel depends on all pixels of the whole image. Histogram methods are
usually global, but they can also be used in a neighbourhood.
Spatial domain methods
The value of a pixel with coordinates (x,y) in the enhanced image is the result of performing
some operation on the pixels in the neighbourhood of (x,y) in the input image, F.
Neighbourhoods can be any shape, but usually they are rectangular.
Grey scale manipulation
The simplest form of operation is when the operator T only acts on a
in the input image, that is
transformation or mapping.
pixel neighbourhood
only depends on the value of F at (x,y). This is a grey scale
The simplest case is thresholding where the intensity profile is replaced by a step function, active
at a chosen threshold value. In this case any pixel with a grey level below the threshold in the
input image gets mapped to 0 in the output image. Other pixels are mapped to 255.
Other grey scale transformations are outlined in figure 1 below.
Figure 1: Tone-scale adjustments.
Histogram Equalization
Histogram equalization is a common technique for enhancing the appearance of images. Suppose we
have an image which is predominantly dark. Then its histogram would be skewed towards the lower end
of the grey scale and all the image detail is compressed into the dark end of the histogram. If we could
`stretch out' the grey levels at the dark end to produce a more uniformly distributed histogram then the
image would become much clearer.
Figure 2: The original image and its histogram, and the equalized versions. Both images are quantized to
64 grey levels.
Histogram equalization involves finding a grey scale transformation function that creates an output
image with a uniform histogram (or nearly so).
How do we determine this grey scale transformation function? Assume our grey levels are
continuous and have been normalized to lie between 0 and 1.
We must find a transformation T that maps grey values r in the input image F to grey values s =
T(r) in the transformed image
.
It is assumed that


T is single valued and monotonically increasing, and
for
.
The inverse transformation from s to r is given by
r = T-1(s).
If one takes the histogram for the input image and normalizes it so that the area under the
histogram is 1, we have a probability distribution for grey levels in the input image Pr(r).
If we transform the input image to get s = T(r) what is the probability distribution Ps(s) ?
From probability theory it turns out that
where r = T-1(s).
Consider the transformation
This is the cumulative distribution function of r. Using this definition of T we see that the derivative of s
with respect to r is
Substituting this back into the expression for Ps, we get
for all
want.
.Thus, Ps(s) is now a uniform distribution function, which is what we
Discrete Formulation
We first need to determine the probability distribution of grey levels in the input image. Now
where nk is the number of pixels having grey level k, and N is the total number of pixels in the image.
The transformation now becomes
Note that
, the index
,and
.
The values of sk will have to be scaled up by 255 and rounded to the nearest integer so that the
output values of this transformation will range from 0 to 255. Thus the discretization and
rounding of sk to the nearest integer will mean that the transformed image will not have a
perfectly uniform histogram.
Image Smoothing
The aim of image smoothing is to diminish the effects of camera noise, spurious pixel values,
missing pixel values etc. There are many different techniques for image smoothing; we will
consider neighbourhood averaging and edge-preserving smoothing.
Neighbourhood Averaging
Each point in the smoothed image,
is obtained from the average pixel value in a
neighbourhood of (x,y) in the input image.
For example, if we use a
neighbourhood around each pixel we would use the mask
1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
Each pixel value is multiplied by , summed, and then the result placed in the output image.
This mask is successively moved across the image until every pixel has been covered. That is,
the image is convolved with this smoothing mask (also known as a spatial filter or kernel).
However, one usually expects the value of a pixel to be more closely related to the values of
pixels close to it than to those further away. This is because most points in an image are spatially
coherent with their neighbours; indeed it is generally only at edge or feature points where this
hypothesis is not valid. Accordingly it is usual to weight the pixels near the centre of the mask
more strongly than those at the edge.
Some common weighting functions include the rectangular weighting function above (which just
takes the average over the window), a triangular weighting function, or a Gaussian.
In practice one doesn't notice much difference between different weighting functions, although
Gaussian smoothing is the most commonly used. Gaussian smoothing has the attribute that the
frequency components of the image are modified in a smooth manner.
Smoothing reduces or attenuates the higher frequencies in the image. Mask shapes other than the
Gaussian can do odd things to the frequency spectrum, but as far as the appearance of the image
is concerned we usually don't notice much.
Edge preserving smoothing
Neighbourhood averaging or Gaussian smoothing will tend to blur edges because the high
frequencies in the image are attenuated. An alternative approach is to use median filtering. Here
we set the grey level to be the median of the pixel values in the neighbourhood of that pixel.
The median m of a set of values is such that half the values in the set are less than m and half are
greater. For example, suppose the pixel values in a
neighbourhood are (10, 20, 20, 15, 20,
20, 20, 25, 100). If we sort the values we get (10, 15, 20, 20, |20|, 20, 20, 25, 100) and the
median here is 20.
The outcome of median filtering is that pixels with outlying values are forced to become more
like their neighbours, but at the same time edges are preserved. Of course, median filters are nonlinear.
Median filtering is in fact a morphological operation. When we erode an image, pixel values are
replaced with the smallest value in the neighbourhood. Dilating an image corresponds to
replacing pixel values with the largest value in the neighbourhood. Median filtering replaces
pixels with the median value in the neighbourhood. It is the rank of the value of the pixel used in
the neighbourhood that determines the type of morphological operation.
Figure 3: Image of Genevieve; with salt and pepper noise; the result of averaging; and the result of
median filtering.
Image sharpening
The main aim in image sharpening is to highlight fine detail in the image, or to enhance detail that has
been blurred (perhaps due to noise or other effects, such as motion). With image sharpening, we want
to enhance the high-frequency components; this implies a spatial filter shape that has a high positive
component at the centre (see figure 4 below).
Figure 4: Frequency domain filters (top) and their corresponding spatial domain counterparts (bottom).
A simple spatial filter that achieves image sharpening is given by
-1/9 -1/9 -1/9
-1/9 8/9 -1/9
-1/9 -1/9 -1/9
Since the sum of all the weights is zero, the resulting signal will have a zero DC value (that is,
the average signal value, or the coefficient of the zero frequency term in the Fourier expansion).
For display purposes, we might want to add an offset to keep the result in the
range.
High boost filtering
We can think of high pass filtering in terms of subtracting a low pass image from the original image, that
is,
High pass = Original - Low pass.
However, in many cases where a high pass image is required, we also want to retain some of the low
frequency components to aid in the interpretation of the image. Thus, if we multiply the original image
by an amplification factor A before subtracting the low pass image, we will get a high boost or high
frequency emphasis filter. Thus,
Now, if A = 1 we have a simple high pass filter. When A > 1 part of the original image is retained
in the output.
A simple filter for high boost filtering is given by
-1/9 -1/9 -1/9
-1/9
/9 -1/9
-1/9 -1/9 -1/9
where
.
UNIT IV: IMAGE ENCODING
Objective and subjective fidelity criteria – basic encoding process – the mapping – the
quantizer– the coder – differential – encoding – contour encoding – run length encoding image encoding – relative to fidelity criterion – differential pulse code modulation.
The objective fidelity criteria used in comparing images do not always agree with the subjective fidelity
criteria. A new objective fidelity criterion, which is called derivative SNR, is developed in this study and it
is compared with a known objective criterion called a.c. SNR. The sensitivity of the human eye to the
edges in an image is considered while developing the new criterion. Subjective tests performed on a
group of observers show that the subjective decisions agree more with the new and less with the old
objective criteria
Memory has the ability to encode, store and recall information. Memories give an organism the
capability to learn and adapt from previous experiences as well as build relationships. Encoding allows
the perceived item of use or interest to be converted into a construct that can be stored within the brain
and recalled later from short term or long term memory. Working memory stores information for
immediate use or manipulation which is aided through hooking onto previously archived items already
present in the long-term memory of an individual.
olutions to your tough image processing and geospatial challenges require flexible tools for data
exploration and analysis, a library of standard techniques, and a high-level language for
algorithm development. Join us for this free seminar to discover how MATLAB provides a
complete environment for image and video processing, mapping, geospatial data analysis, and
parallel computing.
During this half-day seminar, MathWorks engineers will deliver an introduction to MATLAB
and related tools for image, video, and mapping applications. Specific demonstrations with realworld examples will show you how to:

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Analyze digital elevation models with image processing functionality
Drape aerial imagery on a 3D map display
Access data through Web Map Service (WMS) servers
Create mosaics using video processing techniques for registration
Process arbitrarily large images using block processing workflows
Improve performance with Parallel Computing Toolbox
Differential pulse-code modulation (DPCM) is a signal encoder that uses the baseline of pulsecode modulation (PCM) but adds some functionalities based on the prediction of the samples of
the signal. The input can be an analog signal or a digital signal.
If the input is a continuous-time analog signal, it needs to be sampled first so that a discrete-time
signal is the input to the DPCM encoder.


Option 1: take the values of two consecutive samples; if they are analog samples, quantize
them; calculate the difference between the first one and the next; the output is the difference,
and it can be further entropy coded.
Option 2: instead of taking a difference relative to a previous input sample, take the difference
relative to the output of a local model of the decoder process; in this option, the difference can
be quantized, which allows a good way to incorporate a controlled loss in the encoding.
Applying one of these two processes, short-term redundancy (positive correlation of nearby
values) of the signal is eliminated; compression ratios on the order of 2 to 4 can be achieved if
differences are subsequently entropy coded, because the entropy of the difference signal is much
smaller than that of the original discrete signal treated as independent samples.
DPCM was invented by C. Chapin Cutler at Bell Labs in 1950; his patent includes both
methods.[1]

Shannon's rate-distortion function provides a potentially useful lower bound against which to
compare the rate-versus-distortion performance of practical encoding-transmission systems.
However, this bound is not applicable unless one can arrive at a numerically-valued measure of
distortion which is in reasonable correspondence with the subjective evaluation of the observer
or interpreter. We have attempted to investigate this choice of distortion measure for
monochrome still images. This investigation has considered a class of distortion measures for
which it is possible to simulate the optimum (in a rate-distortion sense) encoding. Such
simulation was performed at a fixed rate for various measures in the class and the results
compared subjectively by observers. For several choices of transmission rate and original
images, one distortion measure was fairly consistently rated as yielding the most satisfactory
appearing encoded images.
We describe novel methods of feature extraction for recognition of single isolated character images. Our
approach is flexible in that the same algorithms can be used, without modification, for feature extraction
in a variety of OCR problems. These include handwritten, machine-print, grayscale, binary and lowresolution character recognition. We use the gradient representation as the basis for extraction of lowlevel, structural and stroke-type features. These algorithms require a few simple arithmetic operations
per image pixel which makes them suitable for real-time applications. A description of the algorithms
and experiments with several data sets are presented in this paper. Experimental results using artificial
neural networks are presented. Our results demonstrate high performance of these features when
tested on data sets distinct from the training data.
UNIT V: IMAGE ANALYSIS AND COMPUTER VISION
Typical computer vision system – image analysis techniques – spatial feature
extraction – amplitude and histogram features - transforms features – edge detection –
gradient operators – boundary extraction – edge linking – boundary representation –
boundary matching – shape representation.
Computer vision is a field that includes methods for acquiring, processing, analysing, and
understanding images and, in general, high-dimensional data from the real world in order to
produce numerical or symbolic information, e.g., in the forms of decisions.[1][2][3] A theme in the
development of this field has been to duplicate the abilities of human vision by electronically
perceiving and understanding an image.[4] This image understanding can be seen as the
disentangling of symbolic information from image data using models constructed with the aid of
geometry, physics, statistics, and learning theory.[5] Computer vision has also been described as
the enterprise of automating and integrating a wide range of processes and representations for
vision perception.[6]
Applications range from tasks such as industrial machine vision systems which, say, inspect
bottles speeding by on a production line, to research into artificial intelligence and computers or
robots that can comprehend the world around them. The computer vision and machine vision
fields have significant overlap. Computer vision covers the core technology of automated image
analysis which is used in many fields. Machine vision usually refers to a process of combining
automated image analysis with other methods and technologies to provide automated inspection
and robot guidance in industrial applications.
As a scientific discipline, computer vision is concerned with the theory behind artificial systems
that extract information from images. The image data can take many forms, such as video
sequences, views from multiple cameras, or multi-dimensional data from a medical scanner.
As a technological discipline, computer vision seeks to apply its theories and models to the
construction of computer vision systems. Examples of applications of computer vision include
systems for:







Controlling processes, e.g., an industrial robot;
Navigation, e.g., by an autonomous vehicle or mobile robot;
Detecting events, e.g., for visual surveillance or people counting;
Organizing information, e.g., for indexing databases of images and image sequences;
Modeling objects or environments, e.g., medical image analysis or topographical
modeling;
Interaction, e.g., as the input to a device for computer-human interaction, and
Automatic inspection, e.g., in manufacturing applications.
Sub-domains of computer vision include scene reconstruction, event detection, video tracking,
object recognition, learning, indexing, motion estimation, and image restoration.
In most practical computer vision applications, the computers are pre-programmed to solve a
particular task, but methods based on learning are now becoming increasingly common
Image analysis is the extraction of meaningful information from images; mainly from digital
images by means of digital image processing techniques. Image analysis tasks can be as simple
as reading bar coded tags or as sophisticated as identifying a person from their face.
Computers are indispensable for the analysis of large amounts of data, for tasks that require
complex computation, or for the extraction of quantitative information. On the other hand, the
human visual cortex is an excellent image analysis apparatus, especially for extracting higherlevel information, and for many applications — including medicine, security, and remote sensing
— human analysts still cannot be replaced by computers. For this reason, many important image
analysis tools such as edge detectors and neural networks are inspired by human visual
perception models.
In this paper, we present an improved method for detecting LSB matching steganography in gray-scale
image. Our improvements focus on three aspects: (1) instead of using the amplitude of local extrema of
the image's histogram in the previous work, we turn to considering the sum of the amplitude of each
point in the histogram; (2) incorporating the calibration (downsample) technique with the current
method; (3) the sum/difference image (which is defined as the sum or difference of two adjacent pixels
in the original image) is taken into consideration to provide additional statistical features. Extensive
experimental results show that the novel steganalyzer out-performs the previous ones.