CS 451 Quiz 32
Computer vision and convolution
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Which of the following is NOT a computer vision problem? *
If we used a 1-megapixel color image as input to a fully-connected neural net with 1000 hidden nodes in the first layer, how many parameters would the matrix W[1] have? *
The first edge detection example in the video uses a 6x6 image and a 3x3 filter. What is the size of the output image? *
Which 3x3 filter (in Octave/Matlab notation) could we use to detect horizontal edges? *
Convolution "places" a filter F on each pixel of the input image. Let R denote the region of the image "covered" by the current placement. How is the resulting output pixel computed (in Octave/Matlab notation)? *
Why is padding useful? *
For "same" convolutions with a 5x5 filter, how many pixels of padding do we need to add on each side? *
Suppose we convolve a large image with a 9x9 filter, with stride 1 or 2, and padding 0 or 3. Which option will result in the least amount of computation? *
If we use "same" convolution to convolve a NxNx3 color (RGB) image with a FxFx3 filter, what are the dimensions of the output? *
How clear is your understanding of convolution? (No wrong answer :) *
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