Model-Based Recognition of Specular Objects Using Sensor Models

Abstract

The authors present a model-based object recognition system for specular objects. Objects with specular surfaces present a problem for computer vision. Simulating object appearances by using the sensor model, and the object model allows us to predict specular features, and to analyze the detectability and reliability of each feature. The system generates a set of aspects of the object. By precompiling the aspects with the feature detectability and the feature reliability, the system prepares adaptable matching templates. At the runtime, an input image is first classified into a few candidate aspects. A deformable template matching finds the best match among them. This method is applicable to multiple objects simply by changing object and sensor models. Experimental results using two kinds of objects and sensors are presented: a TV image of a shiny object and a synthetic aperture radar (SAR) image of an airplane. The results show the flexibility of the proposed model based approach.