RIA: VLSI Architectures and Algorithms for Motion Estimation Using Hough Transform

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Chakrabarti In the past, real-time image processing has been restricted to low-level tasks such as filtering; the more complex medium and high-level tasks have been left to general-purpose computers which are unable to handle the large computation rates of these tasks. Real-time computation of these tasks thus necessitates development of task-specific algorithms and architectures. This project is developing efficient algorithms and architectures for an important and representative medium-level task: motion estimation of 2-dimensional and 3-dimensional objects. The approach reduces the number of computations in motion estimation by implementing it in the Hough space. Specifically, the objects are represented by peaks in the Hough space, and motion parameters are extracted from shifts of these peaks. To facilitate real-time computation of Hough transforms, efficient algorithms that reduce the size of the Hough transform parameter space are being developed. To keep the design costs low, these algorithms are targeted to customized versions of a "base" architecture: a highly pipelined linear array of processors, which has access to an array of memory banks. Apart from developing an efficient scheme for real-time motion estimation, this research will also provide design guidelines for algorithm and architecture development of other medium- and high-level image processing tasks.