As an <i>Edge</i> may be an interesting image feature to extract for robotic visual tasks such as the 3D modelization of the environment of robots by stereovision, we propose in this paper a methodology to implement edge segmentation, then we apply it to design a <i>Temporally Optimized Edge Segmentation for Mobile Robotics Applications</i>. Using our methodology, we show how it is possible to reduce the duration of an edge detection operator from 100.62ms for the slower case to 10.8ms for the faster one. This represents a gain of nearly 90ms for the processing time, so nearly a factor of 10 for the speed up.
Processing images involves large amount of both rich and complex information. Indeed, sets of localized pixels identify objects; however, the same pixels when contained on a larger set (the whole image for example), may also represent other types of information. They may have some semantics or represent a context and so on. Dealing with one type of information identifies problems particular to one grain level. At the low level are for example filtering problems. At the mid-level, one may consider segmentation techniques and at the high level, are interpretation problems. Independently of the algorithmic questions, a structure that allows capturing part or whole of the above granularity is of great interests. In this frame of mind, it is proposed here a structure based on the combinatorial maps' formalism. A combinatorial map is a topological representation, in term of darts, built to represent one object within the image. Permutations are then defined that operate on the darts. Their combinations allow exhaustive and easy circulations on the objects' edges. The combinations allow also representing relations among different objects; a feature one may use for complex (3D) objects' modeling. Furthermore, different information (texture, geometry...) may be attached to the maps. The proposed structure is demonstrated here at the mid-level, within a fusion scheme that combines edge and region segmentations. The first one is accurate in edges detection while the second detects regions which edges are less accurate. Combinatorial maps are then considered to highlight features mentioned above, but also to enhance region edges' representation.