ABSTRACT
In olden days it was chimerical and very surprising thing but over the decade the trend has
changed by technological feasts occurring around the world. Image processing has definitely
has its impact on communication devices such as cell phone, web camera.
By using Digital Image Processing we enhance the digital images and extracting
information and features from the image. Because of the computational load of dealing with
images containing millions of pixels, digital image processing was largely of academic
interest until the 1970s, when dedicated hardware became available that could process
images in real time, for some dedicated problems such as television standards conversion.
With the fast computers and signal processors available in the 2000s, digital image
processing has become the most common form of image processing, and is generally used
because it is not only the most versatile method, but also the cheapest.
There are many applications for Image Processing like surveillance, navigation, and robotics.
This technology is more useful in the investigation in Crime Branch. Digital Image
Processing has the advantages as a wider range of algorithm to be applied to the input data
and can avoid the problems such as build-up of noise and signal distortion during processing.
For this the NASA and U.S military have developed advanced computer software which
improves the clarity of and amount of detail visible in still and video images.
Interest in digital image processing methods stems from two principal application areas:
improvement pictorial information for human interpretation; and processing of image data
for storage, transmission, and representation for autonomous machine perception.
This paper mainly deals with digital image processing, different stages in image processing
and its profound applications in the present era. This also explains its applications found in
medical, military, Robotics fields. This paper stresses the importance and its impact in the
future.
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The main feature Digital Image Editing used for altering and improving images in an all
most endless number of time. The other features of this technology are Image Size
Alteration, Cropping on Image, Removal of Noise and unwanted elements, Image
Compression, merging of images and finally color adjustments and finally advantages and
disadvantages of digital image processing
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INTRODUCTION:
Digital Image Processing is concerned with acquiring and processing of an image. In simple
words an image is a representation of a real scene, either in black and white or in color, and
either in print form or in a digital form i.e., technically an image is a twodimensional light
intensity function. In other words it is a data intensity values arranged in a two-dimensional
form like an array, the required property of an image can be extracted from processing an
image. Image is typically by stochastic models. It is represented by AR model. Degradation
is represented by MA model.
Other form is orthogonal series expansion. Image processing system is typically noncasual
system. Image processing is two dimensional signal processing. Due to linearity Property, we
can operate on rows and columns separately. Image processing is vastly being implemented
by “Vision Systems” in robotics. Robots are designed, and meant to be controlled by a
computer or similar devices. While “Vision Systems” are most sophisticated sensors used in
Robotics. They relate the function of a robot to its environment as all other sensors do.
“Vision Systems” may be used for a variety of applications, including manufacturing,
navigation and surveillance. Some of the applications of Image Processing are:
1. Robotics.
3. Graphics and Animations.
2. Medical Field.
4. Satellite Imaging
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HISTORY:
Because of the computational load of dealing with images containing millions of pixels,
digital image processing was largely of academic interest until the 1970s, when dedicated
hardware became available that could process images in real time, for some dedicated
problems such as television standards conversion. As general-purpose computers became
faster, they started to take over the role of dedicated hardware for all but the most specialized
and compute-intensive operations.
With the fast computers and signal processors available in the 2000s, digital image
processing has become the most common form of image processing, and is generally used
because it is not only the most versatile method, but also the cheapest.
IMAGE PROCESSING:
First we see what digital image is
DIGITAL IMAGE:
A digital image is a representation of a two-dimensional as a finite set of values, called
picture elements or. Typically, the pixels are stored in computer memory or a two
dimensional array of small integers. These values are often transmitted or stored in a
form.
Digital images can be by a variety of input devices and techniques, such as, scanners,
coordinate-measuring machines, seismographic profiling, airborne radar, and more.
It is an image that was acquired through scanners or captured from digital cameras. The most
common kind of digital image processing is digital image editing
Image processing is enhancing image or extracting information or features from an image.
Any activity that transforms an input image into an output image. The manipulation and
alteration of images using computer software.
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DIGITAL IMAGE PROCESSING:
Digital image processing is the use of computer to perform on. Digital image processing has
the same advantages (over analog image processing) as has (over analog signal processing) -it allows a much wider range of algorithms to be applied to the input data, and can avoid
problems such as the build-up of noise and signal distortion during processing.
STAGES IN IMAGE PROCESSING
S a solution
P a problem
Image Acquisition
Segmentation
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1. IMAGE ACQUISITION:
An image is captured by a sensor (such as a monochrome or color TV camera) and
digitized. If the output of the camera or sensor is not already in digital form, an analog-to
digital converter digitizes it.
2. RECOGNITION AND INTERPRETATION:
Recognition is the process that assigns a label to an object based on the information
provided by its descriptors. Interpretation is assigning meaning to an ensemble of
recognized objects.
3. SEGMENTATION:
Segmentation is the generic name for a number of different techniques that divide the
image into segments of its constituents. The purpose of segmentation is to separate the
information contained in the image into smaller entities that can be used for other
purposes.
4. REPRESENTATION AND DESCRIPTION:
Representation and Description transforms raw data into a form suitable for the
Recognition processing.
5. KNOWLEDGE BASE:
A problem domain detailing the regions of an image where the information of interest is
known to be located is known as knowledge base. It helps to limit the search.
THRESHOLDING:
Threshold is the process of dividing an image into different portions by picking a certain
grayness level as a threshold, comparing each pixel value with the threshold, and then
assigning the pixel to the different portions, depending on whether the pixel’s grayness
level is below the threshold or above the threshold value. Threshold can be performed
either at a single level or at multiple levels, in which the image is processed by dividing it
into ” layers”，each with a selected threshold. Various techniques are available to choose
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an appropriate threshold ranging from simple routines for binary images to sophisticated
techniques for complicated images.
CONNECTIVITY:
Sometimes we need to decide whether neighboring pixels are somehow “connected” or
related to each other. Connectivity establishes whether they have the same property, such
as being of the same region, coming from the same object, having a similar texture, etc.
To establish the connectivity of neighboring pixels, we first have to decide upon a
connectivity path.
NOISE REDUCTION:
Like other signal processing mediums, Vision Systems contains noises. Some noises are
systematic and come from dirty lenses, faulty electronic components, bad memory chips
and low resolution. Others are random and are caused by environmental effects or bad
lighting. The net effect is a corrupted image that needs to be preprocessed to reduce or
eliminate the noise. In addition, sometimes images are not of good quality, due to both
hardware and software inadequacies; thus, they have to be enhanced and improved before
other analysis can be performed on them.
CONVOLUTION MASKS:
A mask may be used for many different purposes, including filtering operations and noise
reduction. Noise and Edges produces higher frequencies in the spectrum of a signal. It is
possible to create masks that behave like a low pass filter, such that higher frequencies of
an image are attenuated while the lower frequencies are not changed very much. There by
the noise is reduced.
EDGE DETECTION:
Edge Detection is a general name for a class of routines and techniques that operate on an
image and results in a line drawing of the image. The lines represented changes in values
such as cross sections of planes, intersections of planes, textures, lines, and colors, as well
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as differences in shading and textures. Some techniques are mathematically oriented,
some are heuristic, and some are descriptive. All generally operate on the differences
between the gray levels of pixels or groups of pixels through masks or thresholds. The
final result is a line drawing or similar representation that requires much less memory to
be stored, is much simpler to be processed, and saves in computation and storage costs.
Edge detection is also necessary in subsequent process, such as segmentation and object
recognition.
IMAGE DATA COMPRESSION:
Electronic images contain large amounts of information and thus require data transmission
lines with large bandwidth capacity. The requirements for the temporal and spatial
resolution of an image, the number of images per second, and the number of gray levels
are determined by the required quality of the image. Recent data transmission and storage
techniques have significantly improved image transmission capabilities, including
transmission over the Internet.
Importance of image data
According to one estimate, more than 75 percent of all the information received by man is
visual. Some researchers arguably consider this figure to be as high as 99 percent!
Even if we consider the conservative estimate, the remaining four senses contribute to
only 25 percent of the total share. And man has known this since ancient times. Probably
that’s the reason why the ancient Chinese coined the now popular proverb, “A picture
speaks a thousand words.” It is very evident that vision is a major source of information
for human beings, and thus if we could possibly provide similar visual faculties to
machines, we shall be able to achieve visual automation for a very broad range of
applications.
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Image Processing vs. Computer Graphics
There generally is a bit of confusion in recognizing the difference between the fields of
Image Processing and Computer Graphics, often even in the minds of tech-savvy
computer professionals. Actually, Image Processing and Computer Graphics are entirely
different, almost the opposite of each other. A computer graphics system is involved with
image synthesis, and not recognition or analysis, as in the case of Image Processing. The
input of a computer graphics system consists of an item list that describes a scene and its
purpose is to transform this list into a digital image, which could have been formed, if this
scene would really exist. Morphing used in advertisements could be said to be the most
commonly witnessed computer graphics technique. In contrast, input to an Image
Processing system is always a real image formed via some physical phenomenon such as
scanning, filming, etc. The main role of Image Processing is not to create information but
to extract it, integrate it, make it explicit and usable.
IMAGE EDITOR FEATURES:
Listed below are some of the most used capabilities of the better graphic manipulation
programs. The list is by no means all inclusive. There are a myriad of choices associated
with the application of most of these features.
♦ Image Size Alteration: Image editors can resize an image, making it larger, or
smaller. High image resolution cameras can produce large images which are
often reduced in size for Internet use. Image editor programs use a mathematical
process called re-sampling to calculate new pixel values whose spacing is larger
or smaller than the original pixel values..
♦ Noise Removal: Image editors may feature a number of algorithms which can add
or remove noise in an image. JPEG artifacts can be removed; dust & scratches can
be removed and an image can be despeckled. Noise tends to invade images when
pictures are taken in low light settings.
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♦ Removal of Unwanted Elements: Most image editors can be used to remove
unwanted branches, etc, using a "clone" tool.
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Original
Removal of branch at the top of image
♦ Selective Color Change: Image editors have the ability to selectively change the
color of specific items in an image.
An example of selective color change, the original is on the right.
♦ Merging Of Images: Many graphics applications are capable of merging one or more
individual images into a single picture. The orientation and placement of each image
can be controlled. The two images shown here were once individual studio portraits.
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John and Bertha Phillips - 1910
An example of some special effects that can be added to a picture.
♦ Change Color Depth: It is possible, using software, to change the color depth of
images. Common color depths are 2, 16, 256, and 16 million colors. The JPEG
and PNG image formats are capable of storing 16.7 million colors (equal to 256
luminance values per color channel). In addition, grayscale images of 8 bits or less
can be created, usually via conversion and down sampling from a full color image.
An example of converting an image from color to grayscale.
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APPLICATIONS
APPLICATION 1:
Image Processing is vastly being implemented in Vision Systems in Robotics. Robots
capture the real time images using cameras and process them to fulfill the desired
action. Here the usage of sensors/cameras and Edge Detection technique are related to
Image Processing and Vision Systems. By this technique the complexity of using
manual sensors is minimized to a great extent and thereby sophistication is increased.
Hence image processing is used here in the study of robotics.
APPLICATION 2:
In the field of Medicine this is highly applicable in areas like Medical imaging,
Scanning, Ultrasound and X-rays etc. Image Processing is rapidly used for MRI
SCAN (Magnetic Resonance Imaging) and CT SCAN (Computer Tomography).
Tomography is an imaging technique that generates an image of a thin cross sectional
slice of a test piece.
Bone Scan
Chest X-Ray and Baby Scan and MRI SCAN of
Aortic angiogram
Thyroids
Knee
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ADVANTAGES:
1. One of the biggest advantages of digital imaging is the ability of the operator
to manipulate the pixel shades to correct image density and contrast, is called
PostProcessing. And perform other processing functions that could result in
improved diagnosis and fewer repeated examinations.
2. Digital imaging allows the electronic transmission of images to third-party
providers, referring dentists, consultants, and insurance carriers via a modem.
3. Digital imaging is also environmentally friendly since it does not require
chemical processing. It is well known that used film processing chemicals
contaminate the water supply system with harmful metals such as the silver
found in used fixer solution.
4. Radiation dose reduction is also a benefit derived from the use of digital
systems. Some manufacturers have claimed a 90% decrease in radiation
exposure, but the real savings depend on comparisons.
DISADVANTAGES:
There are also disadvantages associated with the use of digital systems.
1. The initial cost can be high depending on the system used, the number of
detectors purchased, etc.
2. Competency using the software can take time to master depending on the
level of computer literacy of team members. Finally, since digital imaging in
dentistry is not standardized, professionals are unable to exchange information
without going through an intermediary process.
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CONCLUSION:
It’s a critical study, which plays a vital role in modern world as it is involved with
advanced use of science and technology. The advances in technology have created
tremendous opportunities for Vision System and Image Processing. There is no doubt
that the trend will continue into the future Over the next few years, the growth of
digital imageprocessing is going to be enormous with new products and technologies
coming out frequently. In order to get the most out of this period, it is going to be
important that imageprocessing planners and developers have a clear idea of what
they are looking for and then choose strategies and methods that will provide them
with performance today and flexibility for tomorrow. . From the above discussion we
can conclude that this field has relatively more advantages than disadvantages and
hence is very useful in varied branches.
REFERENCES:
1. Introduction To Robotics, Analysis, Systems, Applications - Saeed B. Niku
2. Introduction To Digital Image Processing - Anil K.Jain
3. Digital Mage Processing - Rafael C. Gonzalez And Richard E. Woods, Addison
Wesley 1993.
4. 4 wikipedia
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