Hardware-oriented feature extraction based on compressive sensing

Feature extraction is used to reduce the amount of resources required to describe a large set of data. A given feature can be represented by a matrix having the same size as the original image but having relevant values only in some specific points. We can consider this sets as being sparse. Under this premise many algorithms have been generated to extract features from compressive samples. None of them though is easily described in hardware. We try to bridge the gap between compressive sensing and hardware design by presenting a sparsifying dictionary that allows compressive sensing reconstruction algorithms to recover features. The idea is to use this work as a starting point to the design of a smart imager capable of compressive feature extraction. To prove this concept we have devised a simulation by using the Harris corner detection and applied a standard reconstruction method, the Nesta algorithm, to retrieve corners instead of a full image.