Feature detection is presumed to be a major task of the early stages of the visual system.
This paper presents an algorithm for feature detection in complex, natural images, which explicitly uses common visual models' functions and structures. Horizontal and vertical orientations only are used and the filtered images are binarized to positive and negative contrast. Each pixel is tested at all scales at a given orientation and if the pixel maintains its polarity across all scales, it is marked as an edge with the corresponding polarity. The technique can easily and effectively detect the important features in complex natural images. While feature detection is good, images have a significant level of noise and the Hilbert transform imposes an arbitrary direction on the polarity of edges.