This paper illustrates three principles of a generic segmentation method, and their application to two particular cases of scene analysis. These cases refer to different applications, as the target tracking from infrared imagery described in [BPZ89], or the 3D reconstruction of indoor / outdoor scene, in man-made environments, from visible imagery described in [BBHZ89]. The first principle of the proposed segmentation methodology is in considering future implementation on parallel computers either of the MIMD (Multiple Instructions Multiple Data) or SIMD type (Single Instruction Multiple Data). The developped algorithms must exploit the possibilities of these computers. The second principle is the early introduction of an a priori knowledge. So the segmentation method is specific to a particular case of scene analysis. It relies on both a physical model related to the image formation (depending totally on the spectral domain, here IR or visible) , and a conceptual model related to the application, here tracking or 3D reconstruction. This introduction of knowledge allows the system to segment "finely" only certain interesting parts of the image. The third principle is in forcing cooperative or guided segmentation. For instance, robust segmentation algorithms often require simultaneous information about homogeneity and disparity properties of the image. The proposed method, which implies a great cooperation between edge and region detectors, enhancing these previous properties, satisfies this requirement.