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Showing results for tags 'convolution'.
Found 3 results
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Hello, I am trying to do some edge detection using a kernel. I need to do this using standard SOPs/VOPs. http://setosa.io/ev/image-kernels/ I saw a tutorial from Entagma (https://vimeo.com/169437381) that basically does the operation by hand splitting out the vectors and multiplying them but it's quite tedious and you dont really know what vectors you're multiplying (QUESTION: is there an order in which pcfind returns the array of points eg. clockwise closest to furthest ?) Is there a better way (using actual matrices / knowing exactly which points) ? thanks!
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Testing out an idea I had. Chop up A, Chop up B. Use the parts of B that are like A to rebuild A using only parts from B. 3D convolution. In audio the technique is called Convolution reverb. In Machine learning the technique is used to teach the computer to recognize objects. Here in my test it's used to glue a bunch of soggy corn flakes to the poor beleaguered Stanford terra cotta bunny. More explanation and demo videos to come. Questions? Comments? Has someone already done this but better?
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Volume convolution on the GPU using OpenCL. For 27M voxels using 100 iterations, OpenCL is 650 times faster than C++ and 12525 times faster than VEX.
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