Applying Sensor Models To Automatic Generation Of Object Recognition Programs

Abstract

One of the most important and systematic methods to build modelbased vision systems is that to generate 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 an object to be recognized, sensor models to predict object appearances from the object model under a given sensor, strategy generation using the pred,icted appearances to produce an recognition strategy, and program generation converting the recognition strategy into an executable code. This paper concentrates on sensor modeling and its relationship with strategy generation, because we regard it as the bottle neck 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 features can be detected and in what condition the features are detected; sensor reliability is a confidence for the detected features. We define the configuration space to represent sensor characteristics. We propose a representation method for sensor detectability and rcliability in the configuration space. Finally, we investigate how to use the proposed sensor modcl in automatic generation of an objcct recognition program.