A New Objective Supervised Edge Detection Assessment Using Hysteresis Thresholds

Abdulrahman, Hasan; Magnier, Baptiste; Montesinos, Philippe

doi:10.1007/978-3-319-70742-6_1

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…HK and AGK are rotated each π/72 radian angles. Finally, after a non-maximum suppression [26], an objective assessment is performed by varying hysteresis thresholds on normalized thin edges until the Relative Distance Error (RDE) [7] evaluation obtains the minimum score [2]: Table 4. Parameters of the filters in function of their support size, i.e., the number of pixels under the range of the filter.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Image and Signal Processing

Bazzi

Mammass²,

Zaki³

et al. 2018

Lecture Notes in Computer Science

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Easy to use, oriented half kernels are reliable in image analysis. These thin filters, rotated in all the desired directions are useful to detect edges, or extract precisely their orientations, even concerning highly noisy images. Usually, the filtering process corresponds to convolutions with Gaussians and their derivatives. Other filters exist and can be implemented in order to build half kernels. However, functions used for the smoothing and derivative parts have not been studied in depth. The goal of this paper is to evaluate different types of half filters as a function of the noise level. The studied kernels have the same spatial support, enabling easier comparisons. To address the robustness of the studied filters against noise, the image quality is gradually worsened. Then, their performances are compared through objective evaluations of both segmentation and gradient direction.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Image and Signal Processing

Bazzi

Mammass²,

Zaki³

et al. 2018

Lecture Notes in Computer Science

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“…Other statistical measures are similar to P m [14,15,16] or worse in objective evaluation, see [17,18], so P m is the basis for the comparison in this paper. Also, statistical measures such as ROC [3] or PR [16] curves evaluate the comparison of two edge images, pixel per pixel, but do not detect when the Table 1.…”

Section: On Existing Normalized Measuresmentioning

confidence: 99%

“…Table 1 reviews the most relevant normalized criteria/measures involving distances. Alternative dissimilarity measures, inspired from the Hausdorff distance [23], but non-normalized, have been proposed in the literature, see [8,9,10,18,17,11]. In [11], a normalization for the measure of distances is proposed, but it is not really practical for real images.…”

Section: Error Measure Namementioning

confidence: 99%

Edge Detection Evaluation: A New Normalized Figure of Merit

Magnier

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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“…In [59], a measure of the edge detection assessment is developed: it is denoted λ and improves the segmentation measure Ψ (see formulas in Table 4). The λ measure penalizes highly FNs compared to FPs (as a function of their mistake distances), depending on the number of TPs.…”

Section: Influence Of the Penalization Of False Negative Points In Edmentioning

confidence: 99%

A Review of Supervised Edge Detection Evaluation Methods and an Objective Comparison of Filtering Gradient Computations Using Hysteresis Thresholds

Magnier¹,

Abdulrahman

Montesinos³

2018

J. Imaging

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Useful for human visual perception, edge detection remains a crucial stage in numerous image processing applications. One of the most challenging goals in contour detection is to operate algorithms that can process visual information as humans require. To ensure that an edge detection technique is reliable, it needs to be rigorously assessed before being used in a computer vision tool. This assessment corresponds to a supervised evaluation process to quantify differences between a reference edge map and a candidate, computed by a performance measure/criterion. To achieve this task, a supervised evaluation computes a score between a ground truth edge map and a candidate image. This paper presents a survey of supervised edge detection evaluation methods. Considering a ground truth edge map, various methods have been developed to assess a desired contour. Several techniques are based on the number of false positive, false negative, true positive and/or true negative points. Other methods strongly penalize misplaced points when they are outside a window centered on a true or false point. In addition, many approaches compute the distance from the position where a contour point should be located. Most of these edge detection assessment methods will be detailed, highlighting their drawbacks using several examples. In this study, a new supervised edge map quality measure is proposed. The new measure provides an overall evaluation of the quality of a contour map by taking into account the number of false positives and false negatives, and the degrees of shifting. Numerous examples and experiments show the importance of penalizing false negative points differently than false positive pixels because some false points may not necessarily disturb the visibility of desired objects, whereas false negative points can significantly change the aspect of an object. Finally, an objective assessment is performed by varying the hysteresis thresholds on contours of real images obtained by filtering techniques. Theoretically, by varying the hysteresis thresholds of the thin edges obtained by filtering gradient computations, the minimum score of the measure corresponds to the best edge map, compared to the ground truth. Twenty-eight measures are compared using different edge detectors that are robust or not robust regarding noise. The scores of the different measures and different edge detectors are recorded and plotted as a function of the noise level in the original image. The plotted curve of a reliable edge detection measure must increase monotonously with the noise level and a reliable edge detector must be less penalized than a poor detector. In addition, the obtained edge map tied to the minimum score of a considered measure exposes the reliability of an edge detection evaluation measure if the edge map obtained is visually closer to the ground truth or not. Hence, experiments illustrate that the desired objects are not always completely visible using ill-suited evaluation measure.

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A New Objective Supervised Edge Detection Assessment Using Hysteresis Thresholds

Cited by 8 publications

References 24 publications

Image and Signal Processing

Image and Signal Processing

Edge Detection Evaluation: A New Normalized Figure of Merit

A Review of Supervised Edge Detection Evaluation Methods and an Objective Comparison of Filtering Gradient Computations Using Hysteresis Thresholds

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