Biologically Plausible Saliency Detection Model

Abstract In order to accurately detect the abnormal looseness of strapping in the process of steel coil hoisting, an accurate detection method of strapping abnormality based on CCD structured light active imaging is proposed. Firstly, a maximum entropy laser stripe automatic segmentation model integrating multi-scale saliency features is constructed. With the help of saliency detection model, the purpose is to reduce the interference of the environment to the laser stripe and highlight the distinguishability between the stripe and the background. Then, the maximum entropy is used to segment the fused saliency features and accurately extract the stripe contour. Finally, the stripe normal field is obtained by calculating the stripe gradient vector, the stripe center line is extracted based on the stripe distribution normal direction, and the abnormal strapping is recognized online according to the stripe center. Experiments show that the proposed method is effective in terms of detection accuracy and time efficiency, and has certain engineering application value.

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A Saliency Detection Model Based on Local and Global Kernel Density Estimation

Neural Information Processing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24955-6_20 ◽

2011 ◽

pp. 164-171 ◽

Cited By ~ 2

Author(s):

Huiyun Jing ◽

Xin He ◽

Qi Han ◽

Xiamu Niu

Keyword(s):

Density Estimation ◽

Kernel Density Estimation ◽

Saliency Detection ◽

Kernel Density ◽

Detection Model ◽

Model Based

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Double Low-Rank and Sparse Decomposition for Surface Defect Segmentation of Steel Sheet

Applied Sciences ◽

10.3390/app8091628 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1628 ◽

Cited By ~ 8

Author(s):

Shiyang Zhou ◽

Shiqian Wu ◽

Huaiguang Liu ◽

Yang Lu ◽

Nianzong Hu

Keyword(s):

Steel Sheet ◽

Surface Defect ◽

Detection System ◽

Saliency Detection ◽

Image Understanding ◽

Low Rank ◽

Sparse Decomposition ◽

Detection Model ◽

Otsu’S Method ◽

Surface Defect Detection

Surface defect segmentation supports real-time surface defect detection system of steel sheet by reducing redundant information and highlighting the critical defect regions for high-level image understanding. Existing defect segmentation methods usually lack adaptiveness to different shape, size and scale of the defect object. Based on the observation that the defective area can be regarded as the salient part of image, a saliency detection model using double low-rank and sparse decomposition (DLRSD) is proposed for surface defect segmentation. The proposed method adopts a low-rank assumption which characterizes the defective sub-regions and defect-free background sub-regions respectively. In addition, DLRSD model uses sparse constrains for background sub-regions so as to improve the robustness to noise and uneven illumination simultaneously. Then the Laplacian regularization among spatially adjacent sub-regions is incorporated into the DLRSD model in order to uniformly highlight the defect object. Our proposed DLRSD-based segmentation method consists of three steps: firstly, using DLRSD model to obtain the defect foreground image; then, enhancing the foreground image to establish the good foundation for segmentation; finally, the Otsu’s method is used to choose an optimal threshold automatically for segmentation. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both subjective and objective tests. Meanwhile, the proposed method is applicable to industrial detection with limited computational resources.

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Saliency Detection Model for Low Contrast Images Based on Amplitude Spectrum Analysis and Superpixel Segmentation

Bio-inspired Computing – Theories and Applications - Communications in Computer and Information Science ◽

10.1007/978-981-10-3614-9_56 ◽

2016 ◽

pp. 454-460

Author(s):

Hua Yang ◽

Xin Xu ◽

Nan Mu

Keyword(s):

Spectrum Analysis ◽

Saliency Detection ◽

Amplitude Spectrum ◽

Low Contrast ◽

Superpixel Segmentation ◽

Detection Model

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Applications and New Research of Visual Saliency and Attention

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.333-335.1171 ◽

2013 ◽

Vol 333-335 ◽

pp. 1171-1174

Author(s):

Fan Hui ◽

Ren Lu ◽

Jin Jiang Li

Keyword(s):

Visual Attention ◽

Quality Evaluation ◽

Saliency Detection ◽

Video Quality ◽

Visual Saliency ◽

Attention Model ◽

Detection Model ◽

Detection Algorithms ◽

New Research ◽

Video Quality Evaluation

Drawing on the suvey of visual attention degree and its significance in psychology and physiology , in recent years, researchers have proposed a lot of visual attention model and algorithms, such as Itti model and many saliency detection algorithms. And in recent years, the researchers applied the visual attention of this technology in a lot of directions, such as a significant regional shifts and visual tracing detection model based on network loss, for video quality evaluation. This paper summarizes the various algorithms and its application of visual attention and its significance.

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