A wavelet based multiscale detection scheme of feature points

We present a scheme for feature points detection on a grey level image. The use of this algorithm does not imply the segmentation of the objects but only the detection of their edges. This task is achieve through the study of the behavior of wavelet coefficients across scales. Once the edges are detected, the high curvature points along them are localized. These points are extracted as the transition points of a gradient phase signal, with a wavelet based algorithm. Finally, our algorithm is able to select between the feature points a set of the most representative ones through the determination of the point type and the measurement of the local curvature. We prove the efficiency of our algorithm on three examples and we discuss the robustness of our algorithm versus classical ones