Application of binocular disparity and receptive field dynamics: A biologically-inspired model for contour detection

Zhang, Qing; Lin, Chia-Wen; Li, Fuzhang

doi:10.1016/j.patcog.2020.107657

Cited by 24 publications

(6 citation statements)

References 23 publications

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“…The proposed method was compared with several non-deep learning methods and deep learning-based contour detection methods, including N4-field [26], DeepContour [11], DeepEdge [4], HED [12], RCF [13], CED [14], LRC [47], TIN [49], and PiDiNet [50], and traditional contour detection methods, including Canny [1], gPb [3], SE [8], and PMI [18]. The results are summarized in Table 4 and Fig 7 . The proposed method achieved better results (ODS = 0.828, OIS = 0.841, AP = 0.875) than the previous best method CED [14] and also exceeded the human standard on the BSDS500 dataset (ODS = 0.803).…”

Section: ) Performance Comparison On Bsds500mentioning

confidence: 99%

“…and SE [8], and deep learning methods, including HED [12], RCF [13], AMH-Net-Resnet [41], LPCB [15], BDCN [42], LRC [47], TIN [49], and PiDiNet [50]. All experiments were based on single-scale inputs.…”

Section: ) Performance Comparison On Nyudv2mentioning

confidence: 99%

“…BDCN [42] trained the neural network by using multiscale features. Inspired by LPCB [15], BDCN [42], LRC [47], RCF [13], and CBAM [32], a bidirectional multiscale refined network (BMRN) is proposed. The BMRN model uses a multi-scale training method to train on the enhanced Berkeley segmentation dataset (BSDS500), the NYUDv2 depth dataset, and Multi-Cue in order to further improve its generalization performance.…”

Section: Introductionmentioning

confidence: 99%

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Bidirectional Multiscale Refinement Network for Crisp Edge Detection

Bao

Huang

2022

IEEE Access

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With the development of deep convolution neural networks (CNNs), contour detection has made great progress. Some contour detectors based on CNNs have better performance than human beings on standard benchmarks. However, it is easier for CNNs to learn the similar features of adjacent pixels, and the number of background pixels and edge pixels in the input training sample is highly imbalanced. Therefore, the prediction edge by the edge detector based on CNNs is thick and requires post-processing to obtain crisp edges. Accordingly, we introduce a novel parallel attention model and a novel loss function that combines cross-entropy and dice loss through the use of adaptive coefficients, and propose a novel bidirectional multiscale refinement network (BMRN) that stacks multiple refinement modules in order to achieve richer feature representation. The experimental results show that our method has better performance than the state-of-the-art on BSDS500 (ODS F-score of 0.828), NYUDv2 depth datasets (ODS F-score of 0.778) and Multi-Cue dataset (ODS F-score 0.905(0.002)).

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Section: ) Performance Comparison On Bsds500mentioning

confidence: 99%

Section: ) Performance Comparison On Nyudv2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bidirectional Multiscale Refinement Network for Crisp Edge Detection

Bao

Huang

2022

IEEE Access

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“…Starting from the active contour technique, the initial curves of an image were specified, and then, the active contour function evolved the curves towards the object's limits. Using this segmentation technique, the mask argument is a binary image that specifies the initial state of the active contour [55]. The limits of the object's regions in the mask define the starting position of the contour used for the evolution of the contour to segment the image.…”

Section: Image Preprocessingmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

et al. 2021

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Contamination on insulators may increase the surface conductivity of the insulator, and as a consequence, electrical discharges occur more frequently, which can lead to interruptions in a power supply. To maintain reliability in an electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance is a difficult task as there are several levels of contamination that are hard to notice during inspections. To improve the quality of inspections, this paper proposes using k-nearest neighbors (k-NN) to classify the levels of insulator contamination based on images of insulators at various levels of contamination simulated in the laboratory. Computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, a statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed up to 85.17% accuracy using the k-fold cross-validation method, with an average accuracy higher than 82% for the multi-classification of contamination of insulators, being superior to the compared models.

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“…Starting from the active contour technique, the initial curves of an image are specified, and then the active contour function evolves the curves towards the object's limits. Using this segmentation technique, the mask argument is a binary image that specifies the initial state of the active contour [35]. The limits of the object's regions in the mask define the starting position of the contour used for the evolution of the contour to segment the image.…”

Section: Image Pre-processingmentioning

confidence: 99%

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Corso¹,

Perez²,

Stefenon³

et al. 2021

Preprint

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The contamination on the insulators may increase its surface conductivity and, as a consequence, electrical discharges occur more frequently, which can lead to interruptions in the power supply. To maintain reliability in the electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance, is a difficult task as there are several levels of contamination that are hardly noticed during inspections. To improve the quality of inspections, this paper proposes to use the k-nearest neighbours (k-NN) to classify the levels of insulator contamination, based on the image of insulators at various levels of contamination simulated in the laboratory. Using computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed results of up to 85.17 % accuracy using the k-fold cross-validation method, with an average accuracy higher than 82 % for multi-classification of the contamination of the insulators, being superior to the compared models.

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Application of binocular disparity and receptive field dynamics: A biologically-inspired model for contour detection

Cited by 24 publications

References 23 publications

Bidirectional Multiscale Refinement Network for Crisp Edge Detection

Bidirectional Multiscale Refinement Network for Crisp Edge Detection

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision

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