Modelling Sensors: Toward Automatic Generation of Object Recognition Program

Abstract

One of the most important and systematic methods of building model-based vision systems is that of generating object recognition programs automatically from given geometric models. Automatic generation of object recognition programs requires several key components to be developed: object models to describe the geometric and photometric properties of the object to be recognized, sensor models to predict object appearances from the object model under a given sensor, strategy generation using the predicted appearances to produce a recognition strategy, and program generation converting the recognition strategy into an executable code. This paper concentrates on sensor modeling and its relationship to strategy generation, because we regard it as the bottleneck to automatic generation of object recognition programs. We consider two aspects of sensor characteristics: sensor detectability and sensor reliability. Sensor detectability specifies what kinds of featuers can be detected and under what conditions the features are detected; sensor reliability is a confidence level for the detected features. We define a configuration space to represent sensor characteristics. Then, we propose a representation method for sensor detectability and reliability in the configuration space. Finally, we investigate how to use the proposed sensor model in automatic generation of object recognition programs.