This paper proposes a distributed Canny edge detection algorithm which can be mapped onto multi-core architectures for high throughput applications. In contrast to the conventional Canny edge detection algorithm which makes use of the global image gradient histogram to determine the threshold for edge detection, the proposed algorithm adaptively computes the edge detection threshold based on the local distribution of the gradients in the considered image block. The efficacy of the distributed Canny in detecting psycho-visually important edges is validated using a visual sharpness metric. The proposed distributed Canny edge detection algorithm has the capacity to scale up the throughput adaptively, based on the number of computing engines. The algorithm achieves about 72 times speed up for a 16-core architecture, without any change in performance. Furthermore, the internal memory requirements are significantly reduced especially for smaller block sizes. For instance, if a 512×512 image is processed in 64×64 blocks using the proposed scheme, the memory is reduced by a factor of 70 as compared to the original Canny edge detector.
[1]
Lina J. Karam,et al.
A No-Reference Objective Image Sharpness Metric Based on the Notion of Just Noticeable Blur (JNB)
,
2009,
IEEE Transactions on Image Processing.
[2]
John F. Canny,et al.
A Computational Approach to Edge Detection
,
1986,
IEEE Transactions on Pattern Analysis and Machine Intelligence.
[3]
Marcel Worring,et al.
High-Performance Distributed Image and Video Content Analysis with Parallel-Horus
,
2007
.
[4]
Didier Demigny,et al.
Efficient ASIC and FPGA implementations of IIR filters for real time edge detection
,
1997,
Proceedings of International Conference on Image Processing.
[5]
Michel Paindavoine,et al.
Implementation of a recursive real time edge detector using retiming techniques
,
1995,
Proceedings of ASP-DAC'95/CHDL'95/VLSI'95 with EDA Technofair.
[6]
Marcel Worring,et al.
High-Performance Distributed Video Content Analysis with Parallel-Horus
,
2007,
IEEE MultiMedia.