Unsupervised multiscale image segmentation

In the early stages of visual information processing one of the most fundamental and complex task is segmentation. This process is used to divide images or image sequences into meaningful regions or objects. Such a representation is definitely needed to go from the first rough description of the scene (i.e. light intensity) up to the semantic level, where one is able to discriminate shapes and other high-level visual primitives. The biological visual system performs this extremely difficult task with such ease that is still a major challenge for computer vision to reach this level of speed and accuracy. It is by now widely believed that a method inspired by biological vision has the best chances of accomplishing this goal. Therefore, our main goal in this thesis is to step on biologically inspired schemes to propose new and efficient techniques to solve the image segmentation problem. The starting point and key concept in this research, is the multiresolution paradigm. Indeed, the fact that the Human Visual System (HVS) processes the information in a multiresolution fashion is now well established. Several techniques have been introduced to model this behaviour. Scale-space is one of them and we will thus focus on such a formulation of the segmentation problem. Such a representation is composed by a stack of successive versions of the original data set at coarser scales. It is assumed that, the bigger the scale, the less information referred to local characteristics of the input data will appear. We also impose that general information applying to large scales will last through scale. Taking that into account, it is reasonable to think that local and high resolution scale information can be related to general and low resolution information. This will enable us to extract image structures. We thus propose to link the multiresolution approach to a computational model based on the analysis of the entire range of significant information across scales. The inherent multiscale image structure of the non-linear scale-space guides a coarse to fine segmentation using different strategies. The first one might be termed extrinsic because the non-linear scale-space is analyzed by an external process of linking information through scales. This leads to a completely adaptive scheme that can then be coupled to a labeling procedure in order to define a genuine segmentation. The second one could be labeled intrinsic because it adapts to the image through its inner properties and incorporate this in the labeling step of the main algorithm. These two approaches are particular examples of multiscale image segmentation. We also apply the same scale-space concept to define a segmentation variational formulation that leads to a region merging segmentation. In order to illustrate the proposed segmentation schemes, we apply our algorithms to gray-scale, noisy, color and texture images. Finally, it should be mentioned that the main problem addressed in this thesis, genera