For this assignment, my first was to filter video feedback through a convolutional matrix which could be altered by the user, allowing for variable effects, such as edge detection, blurring, and embossing, to be fed back on themselves. However, using common kernels for this system with the jit.convolve object yielded transformations too subtle to be fed back without being lost in noise. (The system I built for doing this is still in this patch, off to the right.)

My second attempt was to abandon user-defined transforms and instead utilize Max’s built-in implementation of the Sobel edge detection kernel to create the transform. However, applying the convolution to the feedback itself led to the edge detection being run on itself, causing values in the video to explode. This was solved by applying the edge detection on the input itself, and then adding the camera footage before the final output. (It looks maybe cooler without the original image added, depending on the light, so I included both outputs in the final patch.)