Comparative Study of Contour Detection Evaluation Criteria Based on Dissimilarity Measures

Chabrier, Sébastien; Laurent, Hélène; Rosenberger, Christophe; Emile, Bruno

doi:10.1155/2008/693053

Cited by 34 publications

(29 citation statements)

References 12 publications

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“…MSSIM measures the similarity between the simulated and the despeckled images with local statistics (mean, variance and covariance between the unfiltered and despeckled pixel values) [20,22]. This measure is bounded in (−1, 1), and a good similarity produces values close to 1.…”

Section: Simulation Resultsmentioning

confidence: 99%

Unassisted Quantitative Evaluation of Despeckling Filters

2017

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SAR (Synthetic Aperture Radar) imaging plays a central role in Remote Sensing due to, among other important features, its ability to provide high-resolution, day-and-night and almost weather-independent images. SAR images are affected from a granular contamination, speckle, that can be described by a multiplicative model. Many despeckling techniques have been proposed in the literature, as well as measures of the quality of the results they provide. Assuming the multiplicative model, the observed image Z is the product of two independent fields: the backscatter X and the speckle Y. The result of any speckle filter is X, an estimator of the backscatter X, based solely on the observed data Z. An ideal estimator would be the one for which the ratio of the observed image to the filtered one I = Z/ X is only speckle: a collection of independent identically distributed samples from Gamma variates. We, then, assess the quality of a filter by the closeness of I to the hypothesis that it is adherent to the statistical properties of pure speckle. We analyze filters through the ratio image they produce with regards to first-and second-order statistics: the former check marginal properties, while the latter verifies lack of structure. A new quantitative image-quality index is then defined, and applied to state-of-the-art despeckling filters. This new measure provides consistent results with commonly used quality measures (equivalent number of looks, PSNR, MSSIM, β edge correlation, and preservation of the mean), and ranks the filters results also in agreement with their visual analysis. We conclude our study showing that the proposed measure can be successfully used to optimize the (often many) parameters that define a speckle filter.

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Section: Simulation Resultsmentioning

confidence: 99%

Unassisted Quantitative Evaluation of Despeckling Filters

2017

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“…Finally, a wrong threshold of the segmentation could generate both FPs and FNs. Computing only FPs and FNs enables a segmentation assessment [6] [7], and a reliable edge detection should minimize the following indicators [3]: Additionally, the P erf ormance measure P * m presented in Table 1 considers directly at the same time the three entities T P , F P and F N to assess an a binary image. The obtained score reflects the percentage of statistical errors.…”

Section: Error Measures Involving Only the Confusion Matrixmentioning

confidence: 99%

“…Thus, some improvements have been developed as F and d 4 . Furthermore, as concluded in [3], a complete and optimum edge detection evaluation measure should combine assessments of both over-and under-segmentation, as in S k , ∆ k w and D p . As an example, inspired by f 2 d 6 [2], another way is to consider the combination of both F oM (G t , D c ) and F oM (D c , G t ), as the two following formulas:…”

Section: Assessment Involving Distances Of Misplaced Pixelsmentioning

confidence: 99%

A New Normalized Supervised Edge Detection Evaluation

Abdulrahman¹,

Magnier²,

Montesinos³

2017

Pattern Recognition and Image Analysis

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In digital images, edges characterize object boundaries, then their detection remains a crucial stage in numerous applications. To achieve this task, many edge detectors have been designed, producing different results, with different qualities. Evaluating the response obtained by these detectors has become a crucial task. In this paper, several referenced-based boundary detection evaluations are detailed, pointing their advantages and disadvantages through concrete examples of edge images. Then, a new supervised edge map quality measure is proposed, comparing a ground truth contour image, the candidate contour image and their associated spacial nearness. Compared to other boundary detection assessments, this new method has the advantage to be normalized and remains a more reliable edge map quality measure.

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“…A synthetic image has been contaminated with multiplicative noise that follows a gaussian distribution with μ=0 and σ = 0.125. The threshold has been computed considering k = 2.5 for the expression (6). From figures 1(e) and 1(h), it can be visually seen that the MCV has a better performance than the CV.…”

Section: A New Contour Detector For Images With Multiplicative Noisementioning

confidence: 99%

“…There are proposals which compare results from different detectors with a determined threshold [1,2], where the validity of results is highly opened to criticism because they can change dramatically by choosing different thresholds. Other works compute the PF with a small set of thresholds [3,4,5,6], which does not permit to figure out if it is in the set where the threshold that produces the grater approximation to the ground truth is found. There are other works like [7], which considering the dependence of the PF on the threshold, perform a search process of the optimal threshold, thus allowing a more objective comparison.…”

Section: Introductionmentioning

confidence: 99%

Objective Comparison of Contour Detection in Noisy Images

Pavez

Mora

González

2011

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. The constant appearance of new contour detection methods makes it necessary to have accurate ways of assessing the performance of these methods. This paper proposes an evaluation method of contour detectors for noisy images. The method considers the computation of the optimal threshold that produces a greater approximation to the ground truth and the effect produced by the noise. Both analyzed dimensions allow objective comparisons of the performance of contour detectors.

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Comparative Study of Contour Detection Evaluation Criteria Based on Dissimilarity Measures

Cited by 34 publications

References 12 publications

Unassisted Quantitative Evaluation of Despeckling Filters

Unassisted Quantitative Evaluation of Despeckling Filters

A New Normalized Supervised Edge Detection Evaluation

Objective Comparison of Contour Detection in Noisy Images

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