TY - GEN
T1 - A distributed psycho-visually motivated Canny edge detector
AU - Varadarajan, Srenivas
AU - Chakrabarti, Chaitali
AU - Karam, Lina
AU - Bauza, Judit Martinez
PY - 2010/11/8
Y1 - 2010/11/8
N2 - 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 512x512 image is processed in 64x64 blocks using the proposed scheme, the memory is reduced by a factor of 70 as compared to the original Canny edge detector.
AB - 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 512x512 image is processed in 64x64 blocks using the proposed scheme, the memory is reduced by a factor of 70 as compared to the original Canny edge detector.
KW - Canny edge detector
KW - Distributed processing
KW - Internal memory
KW - Sharpness metric
UR - http://www.scopus.com/inward/record.url?scp=78049376457&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78049376457&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2010.5494923
DO - 10.1109/ICASSP.2010.5494923
M3 - Conference contribution
AN - SCOPUS:78049376457
SN - 9781424442966
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 822
EP - 825
BT - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
T2 - 2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
Y2 - 14 March 2010 through 19 March 2010
ER -