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Edge Detection

Edge Detection. Lecture 2: Edge Detection Jeremy Wyatt. Visual pathway. The striate cortex. Eye-cortex mapping has certain properties Neighbouring areas in the retina are approximately mapped to neighbouring areas in the cortex Half the image in each half of the cortex

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Edge Detection

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  1. Edge Detection Lecture 2: Edge Detection Jeremy Wyatt

  2. Visual pathway

  3. The striate cortex Eye-cortex mapping has certain properties • Neighbouring areas in the retina are approximately mapped to neighbouring areas in the cortex • Half the image in each half of the cortex • Middle of retinal image on the outer edge of the relevant half of the cortex • Mapping is spatial distorted

  4. Hypercolumns & Hyperfields 0.5-1mm • Each hypercolumn processes information about one area of the retina, its hyperfield. • 400-600 columns in each hypercolumn. • Each column has its own receptive field. • All the cells in one column are excited by line stimuli of the same orientation. Surface 3-4mm Column

  5. Cells within a column • Light on the right and dark on the left of this cell causes excitation • The less the contrast the lower the excitation • Different cells in a single column respond to different patterns with the same orientation

  6. Orientation across columns • Different columns are tuned to different orientations • Adjacent columns are tuned to similar orientations • Cells can be excited to different degrees Less excited More excited

  7. Slabs and Hyperfields • Each hypercolumn is composed of about 20 slabs of columns • Each slab is tuned to one orientation • Each column in a slab is centred on a different portion of the hyperfield • But each column takes input from the whole hyperfield Slabs Columns in each slab

  8. Learning • We learn the orientation selectivity of cells in the early months of life • This has been shown by depriving animals of certain orientations of input Sole visual input Orientations present in cortex

  9. Edge detection in machines • How can we extract edges from images? • Edge detection is finding significant intensity changes in the image

  10. Images and intensity gradients • The image is a function mapping coordinates to intensity • The gradient of the intensity is a vector • We can think of the gradient as having an x and a y component direction magnitude a

  11. Approximating the gradient • Our image is discrete with pixels indexed by i and j • We want and to be estimated in the same place i i+1 j j+1

  12. Approximating the gradient • So we use 2x2 masks instead • For each mask of weights you multiply the corresponding pixel by the weight and sum over all pixels i i+1 j j+1

  13. Other edge detectors • Roberts • Sobel

  14. Convolution mask • This process is very general image

  15. What do these do? Original After Sobel Gx Threshold =30 Threshold=100 After Roberts Threshold=5 Threshold=20

  16. Noise • It turns out we will need to remove noise • There are many noise filters • We can implement most of them using the idea of convolution again • e.g. Mean filter

  17. Reading • RC Jain, Chapter 5, Edge Detection

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