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

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Presentation on theme: "Pattern Recognition Mrs. Andleeb Y. Khan Lecture 03 BCS-VII."— Presentation transcript:

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

2 Image enhancement using Point Processing 1.What is point processing? 2.Negative images 3.Thresholding 4.Logarithmic Transforms 5.Power Law Transform 6.Grey Level Slicing 7.Bit Plane Slicing

3 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.

4 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

5 Image enhancement

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9 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.

10 Histogram example

11 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)

12 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

13 From system point of view

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

15 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

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

17 Basic grey level transformation

18 Negative images

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20 Negative images...

21 Logarithmic transformation

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23 Power law transformations

24 Power law transformations...

25 Power law transformation

26 Power Law Example....

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33 Gamma Correction

34 Power Law Grey level transform


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