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Memory-Efficient Architecture for Hysteresis Thresholding and Object Feature Extraction

Memory-Efficient Architecture for Hysteresis Thresholding and Object Feature Extraction,10.1109/TIP.2011.2147324,IEEE Transactions on Image Processing

Memory-Efficient Architecture for Hysteresis Thresholding and Object Feature Extraction  
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Hysteresis thresholding is a method that offers en- hanced object detection. Due to its recursive nature, it is time consuming and requires a lot of memory resources. This makes it avoided in streaming processors with limited memory. We propose two versions of a memory-efficient and fast architecture for hys- teresis thresholding: a high-accuracy pixel-based architecture and a faster block-based one at the expense of some loss in the accu- racy. Both designs couple thresholding with connected component analysis and feature extraction in a single pass over the image. Unlike queue-based techniques, the proposed scheme treats candi- date pixels almost as foreground until objects complete; a decision is then made to keep or discard these pixels. This allows processing on the fly, thus avoiding additional passes for handling candidate pixels and extracting object features. Moreover, labels are reused so only one row of compact labels is buffered. Both architectures are implemented in MATLAB and VHDL. Simulation results on a set of real and synthetic images show that the execution speed can attain an average increase up to for the pixel-based and for the block-based when compared to state-of-the-art techniques. The memory requirements are also drastically reduced by about 99%.
Journal: IEEE Transactions on Image Processing , vol. 20, no. 12, pp. 3566-3579, 2011
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