@conference {13978,
	title = {Canny edge detection on NVIDIA CUDA},
	booktitle = {Computer Vision and Pattern Recognition Workshops, 2008. CVPRW {\textquoteright}08. IEEE Computer Society Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {The Canny edge detector is a very popular and effective edge feature detector that is used as a pre-processing step in many computer vision algorithms. It is a multi-step detector which performs smoothing and filtering, non-maxima suppression, followed by a connected-component analysis stage to detect ldquotruerdquo edges, while suppressing ldquofalserdquo non edge filter responses. While there have been previous (partial) implementations of the Canny and other edge detectors on GPUs, they have been focussed on the old style GPGPU computing with programming using graphical application layers. Using the more programmer friendly CUDA framework, we are able to implement the entire Canny algorithm. Details are presented along with a comparison with CPU implementations. We also integrate our detector in to MATLAB, a popular interactive simulation package often used by researchers. The source code will be made available as open source.},
	keywords = {algorithms;connected-component, analysis, application, Canny, CUDA;computer, detection;feature, detection;NVIDIA, detector;filtering;graphical, detector;non, edge, extraction;smoothing, feature, filter, layers;multistep, methods;, responses;nonmaxima, stage;edge, suppression;smoothing;computer, VISION, vision;edge},
	doi = {10.1109/CVPRW.2008.4563088},
	author = {Luo,Yuancheng and Duraiswami, Ramani}
}
@article {14889,
	title = {Robust and efficient detection of salient convex groups},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {18},
	year = {1996},
	month = {1996/01//},
	pages = {23 - 37},
	abstract = {This paper describes an algorithm that robustly locates salient convex collections of line segments in an image. The algorithm is guaranteed to find all convex sets of line segments in which the length of the gaps between segments is smaller than some fixed proportion of the total length of the lines. This enables the algorithm to find convex groups whose contours are partially occluded or missing due to noise. We give an expected case analysis of the algorithm performance. This demonstrates that salient convexity is unlikely to occur at random, and hence is a strong clue that grouped line segments reflect underlying structure in the scene. We also show that our algorithm run time is O(n <sup>2</sup>log(n)+nm), when we wish to find the m most salient groups in an image with n line segments. We support this analysis with experiments on real data, and demonstrate the grouping system as part of a complete recognition system},
	keywords = {complexity;computer, complexity;contours;image, computational, convex, detection;feature, detection;object, extraction;object, groups;computational, organisation;proximity;salient, recognition;, recognition;line, recognition;perceptual, segment, vision;edge},
	isbn = {0162-8828},
	doi = {10.1109/34.476008},
	author = {Jacobs, David W.}
}
