Multiresolution object detection and segmentation using top-down algorithms

Multiresolution approaches to image segmentation are being investigated widely as potential platforms on which to perform real-time operations on video sequences. A pyramid is a massively parallel computational platform on which variable resolution representations of an underlying image are used to obtain a segmentation. The paper is concerned with the use of computationally efficient hierarchical techniques for object detection and segmentation, and describes several such algorithms which exploit the pyramid structure using vertical interactions between levels. The algorithms use top-down approaches to achieve good performance at a lower cost relative to iterative techniques. The algorithms are discussed and their performance on both synthetic images and real infrared images is compared in terms of segmentation quality and computational cost. Results using iterative linking procedures are also presented, and are compared with the present algorithms in terms of cost and performance. The segmentation performance of the top-down algorithms is shown to be comparable to that of the more computationally expensive iterative algorithms