Abstract
The Edge can be defined as discontinuities in image intensity from one pixel to another. Modem image processing applications demonstrate an increasing demand for computational power and memories space. Typically, edge detection algorithms are implemented using software. With advances in Very Large Scale Integration (VLSI) technology, their hardware implementation has become an attractive alternative, especially for real-time applications. The Canny algorithm computes the higher and lower thresholds for edge detection based on the entire image statistics, which prevents the processing of blocks independent of each other. Direct implementation of the canny algorithm has high latency and cannot be employed in real-time applications. To overcome these, an adaptive threshold selection algorithm may be used, which computes the high and low threshold for each block based on the type of block and the local distribution of pixel gradients in the block. Distributed Canny Edge Detection using FPGA reduces the latency significantly; also this allows the canny edge detector to be pipelined very easily. The canny edge detection technique is discussed in this paper.
Highlights
First step in many computer vision algorithms is the Edge detection
Canny algorithm performs hysteresis thresholding which requires computing high and low thresholds based on the entire image statistics and it has superior performance
The original Canny algorithm computes the higher and lower thresholds for edge detection based on the entire image statistics, which prevents the processing of blocks independent of each other [3]
Summary
First step in many computer vision algorithms is the Edge detection. It is used to identify changes in luminosity of the image, changes in the intensity due to changes in scene structure. Canny algorithm performs hysteresis thresholding which requires computing high and low thresholds based on the entire image statistics and it has superior performance. As canny algorithm depends on a correct setting of the threshold,it miss some edges or detect some spurious edges when the threshold is not set a proper value It is not suitable for mobile robot vision system in which all of the operation should be done by the robot controller and the environment changes constantly [5]. To overcome these shortcomings of traditional canny algorithm, an adaptive threshold selection algorithm is proposed in [2] which compute the high and low threshold for each block based on the type of block and the local distribution of pixel gradients in the block. Most importantly, conducted conformance evaluations and subjective tests show that, compared with the framebased canny edge detector, the proposed algorithm yields better edge detection results for both clean and noisy images
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