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Document Image Processing

Dr. V.N Manjunath Aradhya

DoS in Computer Science, UoM,Mysore

10/10/10

Dr. V.N Manjunath Aradhya Document Image Processing
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Vision

Definition

Vision ?

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Vision

Definition

Vision ?


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Understanding / Perception / Sight

The process of receiving and analyzing visual information by

the human species is referred to as sight, perception orunderstanding


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Computer Vision ?

Definition

Computer Vision aims to duplicate the effect of human vision byelectronically perceiving and understanding an image.


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Computer Vision ?

Definition

Computer Vision aims to duplicate the effect of human vision byelectronically perceiving and understanding an image.


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Physical Image and Digital Image?

Figure: A physical image and a corresponding digital image


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Image?

Definition

An Image may be defined as a 2D function, f(x, y), where x and y

are spatial (plane) coordinates and the amplitude of f at any pairof coordinates is called intensity or gray level of the image at thatpoint.


?

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Image?

Definition

An Image may be defined as a 2D function, f(x, y), where x and y

are spatial (plane) coordinates and the amplitude of f at any pairof coordinates is called intensity or gray level of the image at thatpoint.


P i

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Processing

Preparing or putting through a prescribed procedure

Deal with in a routine way (process a loan, process the

applicants)Perform mathematical and logical operations on (data)according to programmed instructions in order to obtain therequired information


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Processing





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Processing





Wh I P i ?

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Why Image Processing?

Interest in Image Processing methods stems from two principalapplication areas:

1 Improvement of Pictorial Information for Human interpretation2 Processing of image data for storage, transmission and

representation for autonomous machine perception


Digit l I g P ssi g?

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Digital Image Processing?

Manipulation of an image by means of a processor

The process of receiving and analyzing visual information by

digital computerWhen x, y and amplitude values of f are all finite, discretequantities, we call Digital Image. Processing Digital Imagesby means of digital computer


Examples: Contrast Enhancement

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Examples: Contrast Enhancement

Figure: (a) Car with unreadable Number Plate (b)Result of ContrastStretching


Examples: Removal Motion Blur

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Examples: Removal Motion Blur

Figure: (a) Image of jet degraded with motion blur (b) Undegraded Image


Examples: Image Warping

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Examples: Image Warping

Figure: (a) Input Image (b) Output Image


Applications I

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Applications I

The following are the major application areas:

Office Automation: OCR; Document Processing; CursiveScript Recognition; Logo and Icon Recognition; Identificationof address area on envelop; etc.

Industrial Automation: Automatic Inspection System;Automatic Assembling; Process related to VLSI

manufacturing; PCB checking; Robotics; Oil and Natural GasExploration; Process Control Applications; etc.

Bio-Medical: ECG, EEG, EMG Analysis; Cytological,Histological and Sterological Applications; Automated

Radiology and Pathology, X-ray image Analysis, Massscreening of medical images such as mammograms, cancersmears, CAT, MRI, PET, SPECT, and other tomographicimages, Routine screening of plant samples; 3-Dreconstruction and analysis; etc.


Applications II

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Applications II

Remote Sensing: Natural Resources survey and management;estimation related to agriculture, hydrology, forestry,

mineralogy; urban planning; environment and pollutioncontrol; Registration of satellite images; Monitoring trafficalong roads, docks and airfields; etc.

Scientific Applications: High energy physics; other forms oftrack analysis; etc.

Criminology: Fingerprint Identification; Human faceregistration and matching; forensic investigation; etc.

Astronomy and Space Applications: Restoration of imagessuffering from geometric and photometric distortions;

computing close up picture of planetary surfaces; etc.

Meteorology: Short term weather forecasting; long termclimatic change; change detection from satellite and otherremote sensing data; cloud pattern analysis; etc.


Applications III

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Applications III

Information Technology: Facsimile image transmission,videotex; video-conferencing and videophones; etc.

Entertainment and Consumer Electronics: HDTV; multimedia

and video-editing; etc.

Military Applications: Missile guidance and detection; targetidentification; navigation of pilotless vehicle; etc.


Fundamental Steps in DIP

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Fundamental Steps in DIP

Figure: Fundamental Steps in DIP


Contd... I

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Contd... I

Image Acquisition: is the first process in DIP. Ex of Image

Sensor could be Scanner and Camera.Image Enhancement: is among the simplest and mostappealing areas of DIP. Basically the idea behindenhancement technique is to bring out detail that is obscuredor simply to highlight certain features of interest in an image.

Image Restoration: is an area that also deals with improvingthe appearance of an image. However, unlike enhancement,which is subjective, image restoration is objective, in the sensethat restoration technique tend to be based on mathematical

/ probabilistic models of image degradation.Color Image Processing: Gaining more importance because ofthe significant increase in the use of digital images over theInternet.


Contd... II

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Contd... II

Wavelets: are the foundation for representing images invarious degrees of resolution.

Compression: as the name implies, deals with techniques forreducing the storage required to save an image.

Morphological Processing: deals with tools for extractingimage components that are useful in the representation and

description of shape.

Segmentation: procedures partition an image into itsconstituent parts or objects.

Representation and Description: almost always follow the

output of a segmentation stage, which is raw pixel data.Recognition: is the process that assigns a label to an objectbased on its descriptors.


Basic Concepts

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C p

Mathematical models are often used to describe images andother signals. A signal is a function depending on somevariable with physical meaning;

1 it can be one-dimensional (e.g., dependent on time),2 two-dimensional (e.g., an image dependent on two

co-ordinates in a plane),3 three-dimensional (e.g., describing a volumetric object in

space), or higher-dimensional.

Functions may be categorized as continuous, discrete, ordigital. A continuous function has continuous domain and

range; if the domain set is discrete, then we have a discretefunction; if the range set is also discrete, then we have adigital function.


The Continuous Image Function

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g

The (gray-scale) image function values correspond tobrightness at image points.

The image on the human eye retina or on a TV camera sensoris intrinsically two-dimensional (2D).

The 2D image on the imaging sensor is commonly the resultof projection of a three-dimensional (3D) scene. The simplestmathematical model for this is a pin-hole camera.

The 2D intensity image is the result of a perspectiveprojection of the 3D scene, which is modeled by the imagecaptured by a pin-hole camera illustrated in Figure.


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Figure: Perspective Projection Geometry


Contd...

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The image plane has been reflected with respect to the XYplane in order not to get a mirrored image with negativeco-ordinates.

The quantities X, Y, and Z are co-ordinates of the point X in

a 3D scene, and f is the distance from the pinhole to theimage plane.

The projected point has co-ordinates (X1, Y1) in the 2Dimage plane, which can easily be derived from similar

triangles: X1

= Xf/Z and Y1

= Yf/Z.


Contd...

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When 3D objects are mapped into the camera plane byperspective projection, a lot of information disappears becausesuch a transform is not one-to-one.

Recovering information lost by perspective projection is onlyone, mainly geometric, problem of computer visiona secondproblem is understanding image brightness.


Contd...

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Computerized image processing uses digital image functionswhich are usually represented by matrices, so co-ordinates arenatural numbers.

The domain of the image function is a region R in the plane

R = (x, y), 1 x xm, 1 y yn (1)

where xm, yn represent the maximal image co-ordinates.

The range of image function values is also limited; byconvention, in monochromatic images the lowest value

corresponds to black and the highest to white. Brightnessvalues bounded by these limits are gray-levels.


The Fourier Transform I

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An image is a function of two parameters in plane. One

possible way to investigate its properties is to decompose theimage function using a linear combination of orthonormalfunctions.

The Fourier Transform uses harmonic functions for the

decomposition. The 2D FT is defined by the integral

F(u, v) =

f(x, y)e2i(xu+yu)dxdy (2)

An inverse FT is defined by

f(x, y) =

F(u, v)e2i(xu+yu)dvdu (3)


The Fourier Transform II

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Parameters (x,y) denote image co-ordinates and co-ordinates(u,v) are called spatial frequencies. The function f(x,y) on theleft hand side of equation 3 can be interpreted as a linearcombination of simple periodic patterns e2i(xu+yu).

The real and imaginary components of the pattern are sineand cosine functions and the function F(u,v) is a weightfunction.


Image Digitization

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An image to be processed by computer must be representedusing an appropriate discrete data structure, for example, amatrix.

An image captured by a sensor is expressed as a continuousfunction f(x,y) of two co-ordinates in the plane.

To create digital image, we need to convert the continuoussensed data into digital form. This involves two process:Sampling and Quantization.


Contd...

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Contd...

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Figure shows a continuous image, f(x,y), that we want toconvert to digital image. To convert it to digital form, we haveto sample the function in both coordinates and in amplitude.

Digitizing coordinate values is called sampling

Digitizing amplitude values is called quantization


Contd... I

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The one dimensional function shown in figure (b) is a plot of

amplitude values of the continuous image along the linesegment.

To Sample this function we have to take equally spacedsamples along line AB as shown in Figure (c).

The location of each sample is given by a vertical tick mark inthe bottom part of the figure. The samples are shown assmall white squares superimposed on the function.

The set of these discrete locations gives the samples functions.

In order to form a digital function, the gray level values alsomust be converted into discrete quantities.

The right side of figure (c) shows the gray level scale dividedinto 8 discrete levels ranging from black to white.


Contd... II

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The continuous gray levels are quantized simply by assigningone of the eight discrete gray levels to each sample.

The digital samples resulting from both sampling and

quantization are shown in figure (d).Carrying out this procedure line by line produces a 2D digitalimage.


Color Images

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Color is a property of enormous importance to human visualperception.

Color display is of course the default in most computersystems. Since monochromatic image may not contain enough

information for many applications, while color can often helpHardware will generally deliver or display color via an RGBmodel; thusa particular pixel may have associated with it athree dimensional vector (r,g,b)

(0,0,0) is black, (k,k,k,) is white, (k,0,0) is pure red, and soon....


Contd...

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Other color models turn out to be equally important, thesimplest is CMY - Cyan, Magenta and Yellow

The YIQ Model is useful in color TV broadcasting. Ycomponents provide all that is necessary for a monochrome

display and exploit advantage to luminance, the perceivedenergy of a light source.

The alternative model of most relevance to image processingis HSI - Hue, Saturation and Intensity. Ex: Image

Enhancement Algorithms.


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