Construct Gabor Filters and Filter Image

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Thank you,

For Imgproc.getGaborKernel(ksize, sigma, theta, lambd, gamma), would setting theta to 8 and lambda 5 give me a kernel with 8 orientations and 5 scales?

For Imgproc.filter2D(src, dst, ddepth, kernel), is ddepth the down sampling factor or something else?

• no, each getGaborKernel returns a single filter, also, each filter2D call returns a single image of the original size, so i think, you have to construct 8x5=40 filters(with varying values for theta & lambda), and resize your images to 30x32. then push_back() the filter2D results to a single Mat (starting with an empty one), and apply reshape(1,1) to that, to get a 38400x1 output matrix
• ddepth is the resulting image type (preferably CV_32F). but imho, you should convert your input images to float, before applying the filter, so you would leave it at -1

(disclaimer: no, i did not read that paper !)

((sidenote: "manual" feature engeneering like this seems to be a thing of the past. the dnn folks have won that race 2 or 3 years ago))

@berak, even if dnn folks won the accuracy race, for day to day setups, dnn's are overkill. Not only do they require heaps of data, they take ages to train and run terribly slow on any decent resolution. Therefore hand crafted features for the win!

^^ apparently, you're not the only one, who is thinking like this

JEEJ for this link!