Pyramid Vision Using Key Features to Integrate Image-Driven Bottom-Up and Model-Driven Top-Down Processes

Pyramidlike parallel hierarchical structures have been shown to be suitable for many computer vision tasks and have the potential for achieving the speeds needed for the real-time processing of real-world images. Algorithms are being developed to explore the pyramid's massively parallel and shallowly serial-hierarchical computing ability in an integrated system that combines both low-level and higher level vision tasks. Micromodular transforms are used to embody the program's knowledge of the different objects it must recognize. Pyramid vision programs are described that, starting with the image, use transforms that assess key features to dynamically imply other feature-detecting and characterizing transforms and additional top-down model-driven processes to apply. Program performance is presented for four real-world images of buildings. The use of key features in pyramid vision programs and the related search and control issues are discussed. To expedite the detection of various key features, feature-adaptable windows are developed. In addition to image-driven bottom-up and model-driven top-down processing, lateral search is used and is shown to be helpful, efficient, and feasible. The results indicate that with the use of key features and the combination of a variety of powerful search patterns, the pyramidlike structure is effective and efficient for supporting parallel and hierarchical object recognition algorithms.