Andrea Selinger

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We present a comprehensive performance analysis of multiple appearance-based face recognition method-ologies, on visible and thermal infrared imagery. We compare algorithms within and between modalities in terms of recognition performance, false alarm rates and requirements to achieve specified performance levels. The effect of illumination conditions on(More)
We describe and analyze an appearance-based 3-D object recognition system that avoids some of the problems of previous appearance-based schemes. We describe various large-scale performance tests and report good performance for full-sphere/hemisphere recognition of up to 24 complex, curved objects, robustness against clutter and occlusion, and some(More)
In this report we describe a method for extracting curves from an image using directional pixel variances instead of gradient measures as low-level boundary evidence. The advantage of the variance over the image gradient is that we can accurately compute the direction of a local edge even if a sudden contrast change occurs in the background. This allows(More)
It has long been recognized that, in principle object recognition problems can be solved by simple, brute force methods. However, the approach has generally been held to be completely impractical. We argue that by combining a few more or less standard tricks with computational resources that are historically large, but completely feasible by recent(More)
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