A Novel Decision Mechanism for Image Edge Detection

Jing, Junfeng; Liu, Shenjuan; Liu, Chao; Gao, Tengfei; Zhang, Weichuan; Sun, Changming

doi:10.1007/978-3-030-84522-3_22

Cited by 2 publications

(2 citation statements)

References 176 publications

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“…It is well known that the accuracy of LSI has great influence over subsequent tasks of an input image in computer vision and image processing. As the basic structural feature of an image, image corner and edge are generally detected by using the first-order derivatives 25,27,44 , and blob are generally detected by using the second-order derivatives 45 . Next, examples of these three basic structural features detection are used to show our way to extract LSI from an input image, in which both the scale factor and the anisotropic factor are set to √ 1.5.…”

Section: Lsi Extractionmentioning

confidence: 99%

Image local structure information learning for fine-grained visual classification

Lü

Zhang

Zhao

et al. 2022

Sci Rep

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Learning discriminative visual patterns from image local salient regions is widely used for fine-grained visual classification (FGVC) tasks such as plant or animal species classification. A large number of complex networks have been designed for learning discriminative feature representations. In this paper, we propose a novel local structure information (LSI) learning method for FGVC. Firstly, we indicate that the existing FGVC methods have not properly considered how to extract LSI from an input image for FGVC. Then an LSI extraction technique is introduced which has the ability to properly depict the properties of different local structure features in images. Secondly, a novel LSI learning module is proposed to be added into a given backbone network for enhancing the ability of the network to find salient regions. Thirdly, extensive experiments show that our proposed method achieves better performance on six image datasets. Particularly, the proposed method performs far better on datasets with a limited number of images.

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Section: Lsi Extractionmentioning

confidence: 99%

Image local structure information learning for fine-grained visual classification

Lü

Zhang

Zhao

et al. 2022

Sci Rep

Self Cite

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show abstract

“…Edge detection [1] is a foundational task in image processing and computer vision. Edges refer to positions where the information of pixels change suddenly in gray or color images [2][3][4][5][6][7]. The information of edges is used in many image processing and analysis techniques, such as image segmentation [8][9][10], image retrieval [11][12][13], corner detection [14][15][16][17][18], etc.…”

Section: Introductionmentioning

confidence: 99%

M2GF: Multi-Scale and Multi-Directional Gabor Filters for Image Edge Detection

Li,

Bi,

Zhang

et al. 2023

Applied Sciences

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An image edge detection algorithm using multi-directional and multi-scale Gabor filters is proposed in this paper. The main merit of this method is that high edge detection accuracy can be obtained while maintaining noise robustness. The approach proposed in this paper consists of three procedures: firstly, the transformation to the CIE L*a*b* color space, which has a wide shading area and uniform distribution; secondly, under different scales, the edge feature information of the image is extracted from several different directions by Gabor filters, and a new edge strength map is obtained by feature fusion; thirdly, the new fused edge strength map is enhanced with local features, and a noise-resistant image edge detector is obtained under a novel hysteresis threshold calculation. The experiments illustrate that, compared to the methods involved, the designed edge detector outperforms by about 2% to 4%, and also shows competitive performance regarding the ability to handle noise.

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A Novel Decision Mechanism for Image Edge Detection

Cited by 2 publications

References 176 publications

Image local structure information learning for fine-grained visual classification

Image local structure information learning for fine-grained visual classification

M2GF: Multi-Scale and Multi-Directional Gabor Filters for Image Edge Detection

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