Semantic Models and Object Recognition in Computer Vision
Gerhard Sagerer, Franz Kümmert, Gudrun Socher · 1996
: 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 specific task. The main point of this paper is to show that both paradigms shall and can be incorporated to achieve efficient 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 sche...