Recovering and characterizing image features using an efficient model based approach

The development of an efficient model-based approach to detect and characterize precisely important features such as edges, corners and vertices is discussed. The key is to propose some efficient models associated to each of these features directly from the image by searching the parameters of the model that best approximate the observed grey level image intensities. Due to the large amount of time required by a first approach that assumes the blur of the imaging acquisition system to be describable by a 2-D Gaussian filter, different solutions that drastically reduce this computational time are considered and developed. The problem of the initialization phase in the minimization process is considered, and an original and efficient solution is proposed. A large number of experiments involving real images are conducted in order to test and compare the reliability, the robustness, and the efficiency of the proposed approaches