Coarse-to-Fine Object Detection
Monday, November 13, 2000 - 9:30am - 10:30am
Object recognition is one of the primary goals of high-level computer vision, especially for real greyscale scenes and with the speed and precision of human vision. I will talk about a simpler but still vexing problem: detect and roughly localize all highly visible instances a small set of generic object classes, such as faces and cars - or even from only one class, measuring performance in terms of computation and false alarms. The approach, motivated by efficient computation, is sequential testing which is highly coarse-to-fine with respect to the representation of objects and the exploration of object classes and poses. At the beginning, the tests are universal, accommodating many objects and poses simultaneously, but the false alarm rate is relatively high. Eventually, the tests are more discriminating, but also more complex and dedicated to specific objects and poses. One result is that the spatial distribution of processing is highly skewed and detection is very rapid, but at the expense of (isolated) confusions. Presumably these could be eliminated with localized, more intensive, processing, perhaps involving global optimization.