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Pattern Recognition Mrs. Andleeb Y. Khan Lecture 03 BCS-VII

Pattern Recognition Mrs. Andleeb Y. Khan Lecture 03 BCS-VII

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Pattern RecognitionMrs. Andleeb Y. KhanLecture 03BCS-VII

Image enhancement using

Point Processing1. What is point processing?2. Negative images3. Thresholding4. Logarithmic Transforms5. Power Law Transform6. Grey Level Slicing7. Bit Plane Slicing

Image enhancement• Goal: To modify an image so that its utilization on a particular

application is enhanced.• A set of ad hoc tools applicable based on viewer’s specific

needs.• No general theory on image enhancement exists.

Spatial & frequency domains• Two broad categories of image enhancement techniques:• Spatial Domain• Pixel Processing

• Grey Level Transformation (Data Independent)• Histogram Processing (Data Dependent)• Arithmetic Operations

• Mask-based Processing• Frequency Domain Filtering• Manipulation of Fourier transform or wavelet transform of an

image

Image enhancement

Image enhancement

Image enhancement

Image enhancement

Image histogram The histogram of an image shows us the distribution of

grey levels in the image. Massively useful in image processing, esp. in segmentation.

Histogram example

Spatial domain image enhancement• Most spatial domain

enhancement operations can be reduced to the form

• G(x,y) = T[f(x,y)]• Where f(x,y) is the input image,

g(x,y) is the processed image and T is some operator defined over some neighbourhood of (x,y)

Spatial domain• The operator T can be defined over the set of pixels(x,y) of the

image• The set of neighborhood N(x,y) of each pixel• A set of images f1,f2,f3

From system point of view

Point processing in spatial domain• Operation on the set of image-pixels

Mask processing or filter• Neighborhood is bigger than 1x1 pixel• Use a function of the values of f in a pre-defined

neighborhood of (x,y) to determine the value of g at (x,y)• The value of the mask coefficients determine the nature of the

process• Used in techniques:• Image Sharpening• Image Smoothing

Spatial domain Operation on the set of neighborhoods N(x,y) of each pixel.

Basic grey level transformation

Negative images

Negative images

Negative images...

Logarithmic transformation

Logarithmic transformation

Power law transformations

Power law transformations...

Power law transformation

Power Law Example....

Power Law Example....

Power Law Example....

Power Law Example....

Power Law Example....

Power Law Example....

Power Law Example....

Gamma Correction

Power Law Grey level transform