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Pattern Recognition and Image Processing
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Pattern Recognition and Image
Processing
By:
Nilu Singh
Lucknow, India
Table of Content
•Introduction
•Pattern recognition
•Pattern Class
•Features
•Features extraction
•Modeling
•Pattern recognition system
•Image Processing
•Digital Image Processing
•Applications
How Human Recognize Something
Ex.
How do we know that we know the person who is
standing in front of me?
Even if he/she is our Father/Mother and we have
lived with him/her for 20 years the question how we
recognize him/her is intriguing.
why are some differences minor while others are
Major?
How do we judge that?
How do we know which differences are essential?
Pattern
•It is the subset of similar objects in a larger set (a
class or a cluster).
•Also used for the entire similarity structure in a
collection of objects as well as for a
single object which is typical for a set
of similar objects.
•Ex. : A pattern could be an object or event.
Conti…
Biometric patterns
Conti…
Hand gesture patterns
Pattern Recognition
• Has a very long history (research work in this field started in
the 60s).
• Concerned with the recognition and classification of 2D
objects mainly from 2D images.
• Many classic approaches only worked under very constrained
views (not suitable for 3D objects).
• It has triggered much of the research which led to today‟s field
of computer vision.
• Many pattern recognition principles are used extensively in
computer vision.
Conti…
•It is the process or ability of finding patterns in a
set of objects.
•Pattern recognition is a branch of machine
learning
that
focuses
on
the
recognition of patterns and regularities in data
•Pattern recognition is related to but slightly
different
from
the
fields
of
artificial
intelligence and machine learning.
•It may be labeled as an art as well as a science.
Pattern Class
• A collection of “similar” objects.
Female
Male
Features
•A symbolic or numeric property of a real
world object that might be useful to determine
its class.
•The word „attribute„ is used for this as well.
•Different objects however may have different
numbers of attributes.
•while usually for all objects in the same problem
the same features can be measured.
•Thus objects may be represented by a feature
vector, or by a set of attributes.
Conti…
•A feature property stored in a dataset which refers
to the set of values the particular feature may have.
•During the addition of new objects to
a dataset the feature values may be checked for the
defined domain.
Feature extraction
•It is the process of determining good features for
a feature representation of objects.
•This may refer to raw data like images or time
signals.
•There are many techniques available for feature
extraction from image or any other pattern
recognition system.
How do we model a Pattern Class
• Typically, using a statistical model.
– probability density function (e.g.,
Gaussian)
Gender Classification
male
female
Pattern Recognition System
Test Phase
Training Phase
Pattern Recognition Applications
Software for Pattern Recognition
There are many software has been developed for
pattern recognition, such as:
•LIBSVM: A Library for Support Vector machines
•SVM light: Support Vector Machine software
•PRTools: Is a toolbox for pattern recognition
implemented in Matlab.
•Weka: Is an open source project in java intended
for data mining.
•MATLAB: Has a superfluity of tools which are
useful in implementing and testing Pattern
Recognition algorithms.
Image Processing
Digital Image Processing
Digital Image
f ( x, y)
- a two-dimensional function
- x and y are spatial coordinates
- The amplitude of f is called intensity or gray level at the point (x, y)
Digital Image Processing
- process digital images by means of computer, it covers low-, mid-, and
high-level processes
- low-level: inputs and outputs are images
- mid-level: outputs are attributes extracted from input images
- high-level: an ensemble of recognition of individual objects
Pixel
- the elements of a digital image
Image Processing
•Image processing is a method to convert an image
into digital form and perform some operations on
it, to get an enhanced image or to extract some
useful information from it.
•It is a type of signal dispensation in which input is
image, e.g. video frame or photograph and output
may be image or characteristics associated with
that image.
•The two types of methods used for Image
Processing are Analog and Digital Image
Processing.
Conti…
Image processing basically divided into 5 task set1. Visualization - Observe the objects that are not
visible.
2. Image sharpening and restoration - To create a
better image.
3. Image retrieval - Seek for the image of interest.
4. Measurement of pattern – Measures various
objects in an image.
5. Image Recognition – Distinguish the objects in
an image
Basic Component of Image Processing
Image fundamentals: A simple image formation model, sampling and
quantization, connectivity and adjacency relationships between pixels
Spatial domain filtering: Basic intensity transformations: negative, log, power-law
and piecewise linear transformations, bit-plane slicing, histogram equalization and
matching, smoothing and sharpening filtering in spatial domain, unsharp masking
and high-boost filtering
Frequency domain filtering: Fourier Series and Fourier transform, discrete and
fast Fourier transform, sampling theorem, aliasing, filtering in frequency domain,
lowpass and highpass filters, bandreject and bandpass filters, notch filters
Conti…
Image restoration: Introduction to various noise models, restoration in
presence of noise only, periodic noise reduction, linear and position invariant
degradation, estimation of degradation function
Image reconstruction: Principles of computed tomography, projections and
Radon transform, the Fourier slice theorem, reconstruction using parallelbeam and fan-beam by filtered back-projection methods
Mathematical morphology: Erosion and dilation, opening and closing, the
Hit-or-Miss transformation, various morphological algorithms for binary
images
Wavelets and multi-resolution processing: Image pyramids, sub-band
coding, multi-resolution expansions, the Haar transform, wavelet transform
in one and two dimensions, discrete wavelet transform
Image formation
• There are two parts to the image formation
process:
– The geometry of image formation, which
determines where in the image plane the
projection of a point in the scene will be
located.
– The physics of light, which determines the
brightness of a point in the image plane as a
function of illumination and surface properties.
Steps Involved in Image Processing
Image processing basically includes the following
three steps• Importing the image with optical scanner or by
digital photography.
• Analyzing and manipulating the image which
includes data compression and image enhancement
and spotting patterns that are not to human eyes
like satellite photographs.
•Output is the last stage in which result can be
altered image or report that is based on image
analysis.
Conti…
Image
Restoration
Morphological
Processing
Image
Enhancement
Segmentation
Image
Acquisition
Object
Recognition
Problem Domain
Representation
& Description
Colour Image
Processing
Image
Compression
Images as functions
Image - The vector representation
• Object-oriented representation
• Does not show information of individual pixel,
but information of an object (circle, line,
square, etc.)
Circle(100, 20, 20)
Line(xa1, ya1, xa2, ya2)
Line(xb1, yb1, xb2, yb2)
Line(xc1, yc1, xc2, yc2)
Line(xd1, yd1, xd2, yd)
Comparison
 Bitmap
• Can represent images with
complex variations in colors,
shades, shapes.
• Larger image size
• Fixed resolution
• Easier to implement
 Vector
• Can only represent simple line
drawings
(CAD),
shapes,
shadings, etc.
• Efficient
• Flexible
• Difficult to implement
29
Image digitization
• Sampling means measuring the value of an image at a finite number of
points.
• Quantization is the representation of the measured value at the sampled
point by an integer.
Conti…
Image Sampling and Quantization
Digitizing the
coordinate
values
Digitizing the
amplitude
values
Representation of Digital Image
• An image is represented by a rectangular array of integers.
• An integer represents the brightness or darkness of the image at
that point.
• N: # of rows, M: # of columns, Q: # of gray levels
– N = 2 n , M = 2 m , Q = 2 q (q is the # of bits/pixel)
– Storage requirements: N x M x Q (e.g., N=M=1024, q=8,
1MB)
f (0,0)
f (0,1)
...
f (0, M  1)
f (1,0)
f (1,1)
...
f (1, M  1)
...
f ( N  1,0)
...
...
f ( N  1,1) ...
...
f ( N  1, M  1)
Image acquisition
•
•
•
•
•
•
Video camera
Infrared camera
Range camera
Line-scan camera
Hyperspectral camera
Omni-directional camera etc.
Feature Extraction and Analysis
• The aim of feature extraction & image analysis
is to extract useful information for solving
application-based problems.
• The first step to this is to reduce the amount of
image data using methods that we have
discussed before:
– Image segmentation
– Filtering in frequency domain
Conti…
• The next step would be to extract features that
are useful in solving computer imaging
problems.
• What features to be extracted are application
dependent.
• After the features have been extracted, then
analysis can be done.
Conti…
 Shape Features - Binary Object Features
• Histogram Features
• Color Features
• Spectral Features
Feature Analysis
• Feature Vectors and Feature Spaces
• Distance and Similarity Measures
Verification / Identification
Applications Image Processing
Intelligent Transportation Systems:
This technique can be used in
Automatic number plate recognition and Traffic sign recognition.
Remote Sensing:
This application, sensors capture the pictures of the
earth‟s surface in remote sensing satellites or multi – spectral scanner which is
mounted on an aircraft.
Moving object tracking:
This application enables to measure motion
parameters and acquire visual record of the moving object.
Defense surveillance:
Aerial surveillance methods are used to
continuously keep an eye on the land and oceans.
Biomedical Imaging techniques:
For medical diagnosis, different
types of imaging tools such as X- ray, Ultrasound, computer aided tomography
(CT) etc are used.
Automatic Visual Inspection System: This application improves the
quality and productivity of the product in the industries.
Conti…
– Fingerprint retrieval
– Automatic target recognition
– Industrial inspection
– Medical imaging
Companies In this Field In India
– Sarnoff Corporation
– Kritikal Solutions
– National Instruments
– GE Laboratories
– Ittiam, Bangalore
– Interra Systems, Noida
– Yahoo India (Multimedia Searching)
– nVidia
Graphics,
Pune
(have
requirements)
– ADE Bangalore, DRDO
high
Image processing software
– CVIPtools (Computer Vision and
Image Processing tools)
– Intel Open Computer Vision Library
– Microsoft Vision SDL Library
– Matlab
– Khoros
Conclusion
• Feature Extraction
• Binary Object Features (Area, Center of Area, Axis of
Least Second Moment, Perimeter, Thinness Ratio,
Irregularity, Aspect Ratio, Euler Number, Projection)
• Histogram Features (Mean, Standard Deviation, Skew,
Energy, Entropy)
• Color Features
• Spectral Features
• Feature Analysis
• Feature Vectors and Feature Spaces
– Distance and Similarity Measures (Euclidean distance,
Range-normalized Euclidean distance, City block or
absolute value metric, Maximum value)
Reference
•
•
•
•
•
Yacov Hel-Or, “Image Processing”, Spring 2010
http://web.eecs.utk.edu/~qi/ece472-572
http://www.netnam.vn/unescocourse/computervision/computer.htm
Book: Digital Image Processing, 2nd Edition by Gonzalez and Woods,
Prentice Hall
Sanjeev
Kumar,
“Mathematical
Imaging
Techniques”, lecture notes.
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