Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields

Cohen, Fernand S.; Cooper, David B.

doi:10.1109/tpami.1987.4767895

Cited by 208 publications

(68 citation statements)

References 22 publications

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“…We will assume that the edge is centered at x = 0. Then the response of the (a) (b) (c) (d) (e) filter to this edge at its center HG is given by a convolution integral: +w HG= J G(-x)f(x)dx -w ric, and that its derivatives of odd orders [which appear in the coefficients of even order in (5)] are zero at the origin. Equations (4) and (5) give (1) assuming the filter has a finite impulse response bounded by [-W, W].…”

Section: One-dimensional Formulationmentioning

confidence: 99%

“…Then the response of the (a) (b) (c) (d) (e) filter to this edge at its center HG is given by a convolution integral: +w HG= J G(-x)f(x)dx -w ric, and that its derivatives of odd orders [which appear in the coefficients of even order in (5)] are zero at the origin. Equations (4) and (5) give (1) assuming the filter has a finite impulse response bounded by [-W, W]. The root-mean-squared response to the noise n(x) only, will be HG(O)x0 = -H(XO) (6) Now Hx(xo) is a Gaussian random quantity whose variance is the mean-squared value of Hn(xo), and is given by H, = no Lw f2(x) dx] (2) where n2 is the mean-squared noise amplitude per unit length.…”

Section: One-dimensional Formulationmentioning

confidence: 99%

“…Making this replacement in each integral in the performance criteria that computes a noise response gives us the texture edge design problem. The generalization to other types of texture is not as easy, and for good discrimination between known texture types, a better approach would involve a Markov image model as in [5].…”

Section: Imentioning

confidence: 99%

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A Computational Approach to Edge Detection

Canny¹

1987

Readings in Computer Vision

3,805

4,735

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Abstract-This paper describes a computational approach to edge detection. The success of the approach depends on the definition of a comprehensive set of goals for the computation of edge points. These goals must be precise enough to delimit the desired behavior of the detector while making minimal assumptions about the form of the solution. We define detection and localization criteria for a class of edges, and present mathematical forms for these criteria as functionals on the operator impulse response. A third criterion is then added to ensure that the detector has only one response to-a single edge. We use the criteria in numerical optimization to derive detectors for several common image features, including step edges. On specializing the analysis to step edges, we find that there is a natural uncertainty principle between detection and localization performance, which are the two main goals. With this principle we derive a single operator shape which is optimal at any scale. The optimal detector has a simple approximate implementation in which edges are marked at maxima in gradient magnitude of a Gaussian-smoothed image. We extend this simple detector using operators of several widths to cope with different signal-to-noise ratios in the image. We present a general method, called feature synthesis, for the fine-to-coarse integration of information from operators at different scales. Finally we show that step edge detector performance improves considerably as the operator point spread function is extended along the edge. This detection scheme uses several elongated operators at each point, and the directional operator outputs are integrated with the gradient maximum detector.

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Section: One-dimensional Formulationmentioning

confidence: 99%

Section: One-dimensional Formulationmentioning

confidence: 99%

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A Computational Approach to Edge Detection

Canny¹

1987

Readings in Computer Vision

3,805

4,735

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“…Albeit robust, their wellknown drawback is their CPU consumption due to a large amount of computations, which led researchers to look for solution to speedup its execution time, using parallel machines or dedicated architectures [1,2,9,21,30].…”

Section: Markovian Relaxationmentioning

confidence: 99%

High performance motion detection: some trends toward new embedded architectures for vision systems

Lacassagne

Manzanera

Denoulet

et al. 2008

J Real-Time Image Proc

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The goal of this article is to compare some optimised implementations on current high performance platforms in order to highlight architectural trends in the field of embedded architectures and to get an estimation of what should be the components of a next generation vision system. We present some implementations of robust motion detection algorithms on three architectures: a general purpose RISC processor-the PowerPC G4-a parallel artificial retina dedicated to low level image processingPvlsar34-and the Associative Mesh, a specialized architecture based on associative net. To handle the different aspects and constraints of embedded systems, execution time and power consumption of these architectures are compared.

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“…Another variation is the analogue for constrained relaxation of "zero-temperature" sampling, which has been extensively studied by Besag [21 under the name ICM (for "iterated conditional modes"); see also [15] Then, during the kt h sweep, with A _ Ak, the energy U + AV is successively reduced, just as in ICM where A,, 0. Of course since there is no fixed (reference) energy, the algorithm cannot be conceived as one of iterative improvement.…”

Section: I!mentioning

confidence: 99%

Conference on Uncertainty in Engineering Design (May 10-11, 1988)

Singpurwalla¹

1988

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Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields

Cited by 208 publications

References 22 publications

A Computational Approach to Edge Detection

A Computational Approach to Edge Detection

High performance motion detection: some trends toward new embedded architectures for vision systems

Conference on Uncertainty in Engineering Design (May 10-11, 1988)

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