A framework for spatiotemporal control in the tracking of visual contours
International Journal of Computer Vision
Active shape models—their training and application
Computer Vision and Image Understanding
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Active Shape Model Search
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Multi-template ASM Method for Feature Points Detection of Facial Image with Diverse Expressions
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A novel 3d statistical shape model for segmentation of medical images
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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Active Shape Model has been proven to be one of the most popular methods for recognizing non-rigid objects, which requires huge computation power for real time people tracking. After analyzing the parallel characteristics of the algorithm, we propose a deep pipelined structure for accelerating the Active Shape Model algorithm. The computing engine is organized into a deep pipeline network composing of multiple floating-point arithmetic units, including adders, multipliers, dividers and SQRT etc. A linear multiplication-accumulation (MAC) unit is designed to lower the complexity of the computing resources while keeping high pipeline throughput. In the optimization of the memory efficiency for loading random data in large images during the step of local search, we propose an on-chip buffer scheme to eliminate random accesses to off-chip memory. Experimental results show that our FPGA implementation achieves over 15 times of speedup compared with the sequentially-implemented software solution in Pentium 4 computer.