3D MORPHOLOGICAL SEGMENTATION AND MOTION ESTIMATION FOR IMAGE SEQUENCES

In this paper a method for segmenting image sequences and its application for motion estimation are presented, This method is based on a three-dimensional (3D) morphological segmentation. A 3D (i.e. two spatial dimensions plus time) approach,has advantages over a 2D one, as it produces a coherent segmentation along the time dimension. Mathematical morphology is a very attractive tool for segmentation purposes because it deals with geometric features, such as size, shape, contrast or connectivity, which can be considered as object-oriented, and therefore segmentation-oriented features. The method proposed follows a purely top-down procedure, i.e. it first produces a coarse segmentation in a first level and refines it in the following levels. The original image sequences are considered as functions defined on a 3D space. As a result, it will directly segment 3D regions. Furthermore, a time-recursive approach is introduced in order to deal with interactive applications, thus avoiding the drawbacks of purely 3D methods. Sequence segmentation has many applications in image sequence processing. In this paper, its application for motion analysis is discussed. As the segmentation is performed in a three-dimensional space, the produced regions are connected components in this space which can be related with moving objects. This implies a complete knowledge about the position and shape of every segmented object of the scene in every time section. From this information, an affine transformation is used within each connected component in order to estimate the parameters of motion of every region.