Semantic Models and Object Recognition in Computer Vision

Object recognition has a long history in pattern recognition and computer vision. A major problem addressed is the development of models which are suitable for recognition and scene interpretation tasks. Two principal paradigms are emphasized. On the one hand side statistical and neural models making use of representative training samples optimizing parameters of decision functions. Contrarily, knowledge based techniques build explicit representations by modeling the structure and the constraints associated with a speciic task. The main point of this paper is to show that both paradigms shall and can be incorporated to achieve eecient problem solutions for complex problems. According to this goal the basic techniques for object recognition and scene interpretation will be presented and discussed. Based on this evaluation a hybrid system has been evolved which tries to combine the advantages of the fundamental paradigms. The system is derived from the knowledge representation scheme of procedural semantic networks integrating the advantages of neural network approaches for classiication and scoring purposes. Thus, explicit semantic models are combined with learning sample dependent analogous representations. One application of this environment the reconstruction of three dimensional scenes illustrates that this approach is appropriate for complex tasks. Furthermore, the accuracy of the results shows that hybrid and distributed modeling of objects and scenes is a powerful and eecient technique for scene interpretation tasks.