Generalization of Canny–Deriche filter for detection of noisy exponential edge

Bourennane, El-Bay; Gouton, Pierre; Paindavoine, Michel; Truchetet, Frédéric

doi:10.1016/s0165-1684(02)00283-9

Cited by 35 publications

(21 citation statements)

References 14 publications

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“…14, 15, and 16. These examples clearly show the complementarity between the information given by the various scales for the segmentation.…”

Section: Some Resultsmentioning

confidence: 73%

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Merging system for multiscale edge detection

Laligant¹,

Truchetet²,

Mitéran³

et al. 2005

Opt. Eng

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We present a multiscale edge detection algorithm whose aim is to detect edges whatever their slope. Our work is based on a generalization of the Canny-Deriche filter, characterized by a more realistic edge than the traditional step shape edge. The filter impulse response is used to generate a multiscale edge detection scheme. For the merging of the edge information, we use a geometrical classifier developed in our laboratory. The segmentation system thus set up, after the training phase, does not require any adjustment or depend on any parameter. The main original property of this algorithm is that it leads to a binary edge image without any threshold setting. The quality of the results is inferior to that for classical multiscale merging approaches; nevertheless, this system, studied for real-time functioning, presents satisfactory performance for well-contrasted images and excellent performance for noisy images.

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“…14, 15, and 16. These examples clearly show the complementarity between the information given by the various scales for the segmentation.…”

Section: Some Resultsmentioning

confidence: 73%

“…16 The scale parameter s must thus be chosen with respect to the content of the image and more particularly with respect to the finest edge. Table 1 shows the width of the edge with respect to scale s.…”

Section: Edge Model and Edge Detectormentioning

confidence: 99%

Merging system for multiscale edge detection

Laligant¹,

Truchetet²,

Mitéran³

et al. 2005

Opt. Eng

View full text Add to dashboard Cite

show abstract

“…Canny [29] set up an optimization problem for identifying a filter that would result in an edge with desirable properties. Other edge detection techniques in image processing also try to maximize edge robustness and invariably use iterative or numerical optimization techniques [29], [30], [31] in order to obtain the optimal filter. The current problem differs in its formulation and crucially yields a (noniterative) closed-form solution.…”

Section: Derivation For the Optimally Robust Filtermentioning

confidence: 99%

Robust Extrema Features for Time-Series Data Analysis

Vemulapalli

Monga

Brennan

2013

IEEE Trans. Pattern Anal. Mach. Intell.

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The extraction of robust features for comparing and analyzing time series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation, and their computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time series with an intuitively motivated filter (e.g., for smoothing), and subsequent thresholding to identify robust extrema. We define the properties of robustness, uniqueness, and cardinality as a means to identify the design choices available in each step of the feature generation process. Unlike existing methods, which utilize filters "inspired" from either domain knowledge or intuition, we explicitly optimize the filter based on training time series to optimize robustness of the extracted extrema features. We demonstrate further that the underlying filter optimization problem reduces to an eigenvalue problem and has a tractable solution. An encoding technique that enhances control over cardinality and uniqueness is also presented. Experimental results obtained for the problem of time series subsequence matching establish the merits of the proposed algorithm.

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“…Shen & Castan [8] and Canny-Deriche [9,10] filters correspond respectively to n = 0 and n = 1. Each filter is normalized with respect to unit step response:…”

Section: Edge Detectormentioning

confidence: 99%

Edge enhancement by local deconvolution

Laligant

Truchetet

Dupasquier

2005

Pattern Recognition

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International audienceIn this paper a new approach for blurred image restoration is presented. Our algorithm is based on human vision which zooms back and forth in the image in order to identify global structures or details. Deconvolution parameters are estimated by an edge detection and correspond to the ones of a chosen edge detection model. The segmentation is obtained by merging multiscale information provided by multiscale edge detection. The edge detection is achieved by using a derivative approach following a generalization of Canny-Deriche ﬁltering. This multiscale analysis performs an efﬁcient edge detection in noisy blurred images. The merging leads to the best local representation of edge information across scales. The algorithm deals with a mixed (coarse-to-ﬁne/ﬁne-to-coarse) approach and searches for candidate edge points through the scales. Edge characteristics are estimated by the merging algorithm for the chosen model. Scale, direction and amplitude informations allow a local deconvolution of the original image. The noise problem is not considered in this work since it does not disturb the process. Results show that this method allows non-uniformly blurred image restoration. An implementation of the whole algorithm in an intelligent camera (DSP) has been performed

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Generalization of Canny–Deriche filter for detection of noisy exponential edge

Cited by 35 publications

References 14 publications

Merging system for multiscale edge detection

Merging system for multiscale edge detection

Robust Extrema Features for Time-Series Data Analysis

Edge enhancement by local deconvolution

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