Postdoctoral Research Associateship: Computer Visual Analysis of Surface Reflection

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Visual measurement of surface reflectance properties is an important issue both in basic and in applied computer vision research. Surface reflectance gives a reliable cue for image segmentation and object recognition. The goal of the proposed research is to detect and separate specularity from Lambertian reflectance based on physical models. In order to provide enough information to extract various properties of reflectance, we generally need more than one frame of images. One direction is to use color information and another is to use multiple images with different views. An algorithm for color image segmentation and detection of specularity and inter-reflection is proposed. In addition to the use of color, the use of different views to differentiate specularity from Lamertian reflectance and to obtain object structure is proposed. the proposed algorithm is based on the fact that the appearance of specularity varies depending on viewing direction while that of Lambertian does not. In order to build a more robust and reliable vision system, information by color and multiple views will be integrated. For efficient processing of a large amount of spectral (color), temporal (multiple views) and spatial (image) data, parallel algorithms using a Connection Machine of 4K processors will be designed and implemented. The high degree of computational parallelism will bring the algorithms into practical reality.