Robustness of a multiscale scheme of feature points detection
We present a new scheme for feature points detection on a grey level image. Its principle is the study of the gradient phase signal along object edges and the characterization of the behavior across scales of the wavelet coefficients of this signal. The features points are determined as transition points of this signal. In the second part, we study the robustness of the detection scheme against changes of the acquisition parameters: the viewpoint and the zoom of the camera, the object rotation, the luminescence variation and noise. The results show the method efficiency: most of the points are still detected even if these parameters vary.
feature points acquisition parameter viewpoint method efficiency wavelet transform local curvature scale variation