A gravitational approach to edge detection based on triangular norms

López-Molina, Carlos; Bustince, Humberto; Fernández, Javier; Couto, Pedro; Baets, Bernard De

doi:10.1016/j.patcog.2010.05.035

Cited by 90 publications

(53 citation statements)

References 36 publications

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“…The BEM proposed in [21] has been considered in several research works for measuring errors in detection and localization. This index is devoid of the shortcomings prevailed in the earlier two [25], [31].…”

Section: The Perfomance Indexmentioning

confidence: 95%

“…It consists of a wide range of natural images along with 5-9 binary edge maps for each of these images obtained by human subjects. These binary edge images can be considered as ground truth for comparison of the output of any edge detection algorithm [24][25][26]. The images in this dataset are of sizes 321 481and 481 321.…”

Section: The Datasetmentioning

confidence: 99%

“…Studies suggest few indices generally employed to assess the performance of an edge detector with the available ground truth [18], [21], [25], [29][30]. These are as Misclassification Error, Pratt's Figure of Merit (FOM), and Baddeley Error Metric (BEM).…”

Section: The Perfomance Indexmentioning

confidence: 99%

“…The metric is expressed as follow - [21], [25][26], [31]. In the present article we choose to use 2 w as the performance index for the proposed edge detector.…”

Section: The Perfomance Indexmentioning

confidence: 99%

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A New Method for Edge Extraction in Images using Local Form Factors

Gupta¹,

Routray²,

Mukherjee³

2011

IJCA

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This article proposes a new spatial domain measure of local energy to extract the image features like edges. We define the measure as the local form factor (FF). It is the ratio of RMS to average of the pixel values in a region. Inverse square of the local FF around a center pixel is defined as an index of edge strength at that pixel. The proposed method could be applied directly on any image without smoothing for noise removal. It only needs an estimate of the Signal-to-Noise ratio (SNR) of the images to compensate the effect of noise. The compensated feature image is passed through non-minimum suppression and universal thresholding processes to produce the final edge map. The performance of the method is assessed using Baddeley Error Metric (BEM) and compared with those resulted from the popular Canny edge detector with different scales. The experimental results are encouraging the application of the method to extract edges and hence can be used as a potential candidate for general feature extraction. General TermsImage feature extraction, Low-level image processing

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Section: The Perfomance Indexmentioning

confidence: 95%

Section: The Datasetmentioning

confidence: 99%

Section: The Perfomance Indexmentioning

confidence: 99%

“…The metric is expressed as follow - [21], [25][26], [31]. In the present article we choose to use 2 w as the performance index for the proposed edge detector.…”

Section: The Perfomance Indexmentioning

confidence: 99%

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A New Method for Edge Extraction in Images using Local Form Factors

Gupta¹,

Routray²,

Mukherjee³

2011

IJCA

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“…Other gravitation model based approaches for classification include data gravitation classification (DGC) [37][38][39] and gravitational self-organizing maps (GSOM) [40]. The advantages of the gravity field model have inspired the development of new edge-detection method for gray-scale images [41,42]. Although the image-processing procedure using gravitational theory is relatively easy for understanding, this theory is rarely used in edge detection for HSIs.…”

Section: Introductionmentioning

confidence: 99%

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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