To code image as edges, for instance in the
SparseEdges sparse coding scheme, we use a model of edges in images. A good model for these edges are bidimensional Log Gabor filter. This is implemented for instance in the
LogGabor library. The library was designed to be precise, but not particularly for efficiency. In order to improve its speed, we demonstrate here the use of a cache to avoid redundant computations.
Convolutions are essential components of any neural networks, image processing, computer vision ... but these are also a bottleneck in terms of computations... I will here benchmark different solutions using
tensorflow. This is work-in-progress, so that any suggestion is welcome (for instance on StackExchange!
Sometimes, you need to pick up the $N$-th extremal values in a mutli-dimensional matrix.
Let's suppose it is represented as a
nd-array (here, I further suppose you are using the numpy library from the python language). Finding extremal values is easy with
argsort but this function operated on 1d vectors... Juggling around indices is sometimes not such an easy task, but luckily, we have the
Let's unwrap an easy solution combining these functions: