DIGITAL IMAGE PROCESSING An Overview. Digital Image Processing An Introduction An image may be...
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Transcript of DIGITAL IMAGE PROCESSING An Overview. Digital Image Processing An Introduction An image may be...
DIGITAL IMAGE PROCESSING
An Overview
Digital Image Processing An IntroductionAn image may be defined as : Two-dim. function, f(x, y), where x and y are spatial
(plane) coordinates. The amplitude of f at any pair of coordinates (x, y) is
called the intensity or gray level of the image at that point.
A digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image
elements and Pixels
Digital Image Processing An Introduction
Digitization of a continuous image. The pixel at coordinates [m=10, n=3] has the integer brightness
value 110
An Example: A Monochrome Digital Image
The Pixel Values Of the Image
Changing Image Characteristics with Processing
Fundamental steps in Digital Image Processing
KNOWLEDGE BASE
ImageAcquisition
ImageEnhancement
Image Restoration
Color ImageProcessing
Wavelets &multiresolutionprocessing
Compression MorphologicalProcessing
Segmentation
Representation& Description
Object Recognition
Problem Domain
Image Acquisition
Involves Preprocessing Since the image is in the Digital form . It is the simplest step.
Image Enhancement
Enhancement for the specific applications. Image Enhancement approaches fall into
two broad categories:
1. Spatial Domain Methods
2. Frequency Domain Methods
Image Enhancement Spatial Domain Methods Spatial domain methods are procedures that
operate directly on the pixels Direct manipulation of the pixels in an image. Spatial Domain Processes will be
denoted by the expression: g(x, y)=T[f(x,y)] f(x,y)--- Input Image g(x,y)--- Processed Image T is an operator on f
Image Enhancement Frequency Domain Methods Discrete Fourier Transform (DFT) is the
foundation of most of the work of the image enhancement.
Enhancement in frequency domain is being applied for:
1.SMOOTHING FREQUENCY DOMAIN
2.SHARPENING FREQUENCY DOMAIN
Image Enhancement Filtering
Pre-Processing
Post-Processing
Fourier Transform
Filter Function H (u, v)
Inverse Fourier Transform
f (x, y))
F(u,v) G(u,v)
g (x,y)
INPUT IMAGE PROCESSED IMAGE
Image Enhancement Filtering(cont) Smoothing Frequency Domain Filters
Low pass filters
Sharpening Frequency Domain Filters
High pass filters
An Example
Image Restoration
Improvement in an image in some predefined sense.
Recover or reconstruct an image that has been degraded by using a priori knowledge of the degradation phenomenon.
Degradation Function
H
RestorationFunction
s+
Noiseη(x,y)
f(x,y) F(x,y)
g(x,y)
Color Image Processing
The use of color in image processing is motivated by two principal factors: Color is a powerful descriptor that often
simplifies object identification and extraction from the scene.
Humans can discern thousands of color shades and intensities, compared to about only two shades of gray. This factor is important in manual image analysis.
Color Image Processing (cont.)Color image processing is divide
into two major areas:
Full color processing Pseudo color processing
Wavelets and Multiresolution Processing The wavelet transform makes it even easier to compress, transmit
and analyze many images. Wavelet transforms are based on small
waves, called WAVELETS, of varying frequency and limited duration.
Image Compression
Removal of redundant data. Applications include tele-video conferencing,
FAX etc..
Compression techniques fall into two
categories:• Information Preserving• Lossy compression
Morphological Image Processing Extracting image components that are useful
in the representation and description of region shape ,such as boundaries etc..
Major morphological operations are: Dilation Erosion Open Close
Morphological Image Processing Dilation & Erosion
Image Erosion Dilation
Morphological Image Processing Open & Close
Image with structure element
Opening
Image Segmentation
Image segmentation algorithms generally are
based on one of the two basic properties of the
intensity values: Discontinuity
Line Detection, Edge Detection Similarity
Thresholding,region growing and merging
Representation and Description Representation of a region involves two
choices: External characteristics
Shape characteristics Internal characteristics
Color and Texture
Object or Pattern RecognitionTwo approaches are being developed for
pattern recognition:
Decision –theoretic Methods
1. Matching
2. Neural Networks Structural Methods
1. String Matching
Need Of Digital Image Processing Vision allows humans
to perceive and understand the world surrounding us .
A machine recognition system has real difficulties reading broken characters
APPLICATIONS
remote sensing via satellites and other spacecrafts
image transmission and storage for business applications
Medical Processing radar, sonar, and acoustic image processing robotics Automated inspection of industrial parts.etc.
Applications (contd.)
Thank You