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Calculate the entry in the FeatureMap(1,3) in the figure?

Calculate the entry in the FeatureMap(1,3) in the figure?

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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FeatureMap(m,n) = m n E E Image(m + i – 1,n+j- 1) Kernel(i,j) i- 1j-1 Calculate the entry in the FeatureMap(1,3) in the figure? 92 21 O 71
Transcribed Image Text:FeatureMap(m,n) = m n E E Image(m + i – 1,n+j- 1) Kernel(i,j) i- 1j-1 Calculate the entry in the FeatureMap(1,3) in the figure? 92 21 O 71
The method to compress an image in image processing is using convolution matrix where the number of outputs is reduced by replacing the matrix multiplication with much smaller kernel matrix. This is illustrated on the figure below. In this example a black and white photograph is scanned as a rectangular array of pixels and then stored as image matrix on the left by assigning each pixel a numerical value in accordance with its gray level, (0 = white to 255 = black), then the entries in the matrix are integers between O and 255.Then multiplication is employed with much smaller kernel matrix in the middle. 0|0 21 00 o 106 85 71 1 250 231 127 63 1 250 252 250 209 56 1 250 252 250 250 83 Kernel 0 0 0 Feature map Image Boxes in red and blue frames indicate how the upper left element is formed by applying the kernel to the corresponding upper left region of the input matrix. The elements of much smaller "feature map" matrix on the right can be calculated from: 3.
Transcribed Image Text:The method to compress an image in image processing is using convolution matrix where the number of outputs is reduced by replacing the matrix multiplication with much smaller kernel matrix. This is illustrated on the figure below. In this example a black and white photograph is scanned as a rectangular array of pixels and then stored as image matrix on the left by assigning each pixel a numerical value in accordance with its gray level, (0 = white to 255 = black), then the entries in the matrix are integers between O and 255.Then multiplication is employed with much smaller kernel matrix in the middle. 0|0 21 00 o 106 85 71 1 250 231 127 63 1 250 252 250 209 56 1 250 252 250 250 83 Kernel 0 0 0 Feature map Image Boxes in red and blue frames indicate how the upper left element is formed by applying the kernel to the corresponding upper left region of the input matrix. The elements of much smaller "feature map" matrix on the right can be calculated from: 3.
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