A novel method to look for the hysteresis thresholds for the Canny edge detector

Medina-Carnicer, R.; Muñoz-Salinas, Rafael; Yeguas-Bolivar, Enrique; Diaz-Mas, L.

doi:10.1016/j.patcog.2010.12.008

Cited by 90 publications

(47 citation statements)

References 24 publications

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“…Then, we applied the automatic threshold selection and hysteresis thresholding technique [45] on the edge strength maps to generate the binary edges. We investigated edge detection results with both subjective and quantitative evaluations.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

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Gravitation-Based Edge Detection for Hyperspectral Images

Sun¹,

Zhang²,

Ren³

et al. 2017

Preprint

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Abstract:Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

“…With Φ and θ , non-maximum suppression and automatic hysteresis thresholding [45] can be applied to obtain the exact edge. As shown in Figure 1 (d), there is no spurious edge in the final result, and the detected edges can fully depict the main structure within the image.…”

Section: Spe Responsementioning

confidence: 99%

Gravitation-Based Edge Detection for Hyperspectral Images

Sun¹,

Zhang²,

Ren³

et al. 2017

Preprint

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“…Finally, to binarize the image, we have implemented an automatic non-supervised hysteresis based on the determination of the instability zone of the histogram to find the threshold values [15]. Hysteresis allows to choose which pixels are relevant in order to be selected as edges, using their membership values.…”

Section: Edge Detectormentioning

confidence: 99%

“…. , 0.25} has been introduced, the same one which is used in [15]. In Figure 1, we display the block diagram of the edge detector algorithm proposed in this section and in Figure 2 …”

Section: Edge Detectormentioning

confidence: 99%

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On the Generalization of the Fuzzy Morphological Operators for Edge Detection

González-Hidalgo¹,

Massanet²,

Mir³

et al. 2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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The morphological gradient is a widely used edge detector for grey-level images in many applications. In this paper, we generalize the definition of the morphological gradient of the fuzzy mathematical morphology based on t-norms. Concretely, instead of defining the morphological gradient from the usual definitions of fuzzy dilation and erosion, where the minimum and the maximum are used, we define it from generalized fuzzy dilation and erosion, where we consider a general t-norm and t-conorm, respectively. Once the generalized morphological gradient is defined, we determine which t-norm and tconorm have to be considered in order to obtain a high performance edge detector. Some t-norms and their dual t-conorms are taken into account and the experimental results conclude that the t-norms of the Schweizer-Sklar family generate a morphological gradient which outperforms notably the classical morphological gradient.

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