We present a new 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 achieved 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 about the robustness of our algorithm versus classical ones.