Analysis and realization of saliency map based on visual attention mechanism

Visual attention mechanism is introduced into the traditional road disaster monitoring and early warning system. In this system, the disaster region is the focus of attention (FOA), which happens to be the object needed to process. Ittis algorithm [1]was used to extract the saliency map, then quickly located the regions which may contain disaster according to saliency. The recognition and early warning of disaster can be completed, quickly. This method was tested snowstorms and rolling stones are simulated, and gave the corresponding experimental results. Experiment results show the correctness and efficiency of introducing visual attention mechanism into road disaster monitor and early warning system. It is of great significance and practical value for reducing the computation and improving real-time performance of the total system.

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A Method of Waypoint Selection in Aerial Images for Vision Navigation

International Journal of Optics ◽

10.1155/2014/161026 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Lin Song ◽

Yong-mei Cheng ◽

Lu Yu ◽

Liang Yu

Keyword(s):

Visual Attention ◽

Saliency Map ◽

Attention Mechanism ◽

Aerial Images ◽

Low Rank ◽

Support Vector ◽

Suitability Analysis ◽

Initial Image ◽

Edge Based ◽

Visual Attention Mechanism

We present a novel method to select waypoints from aerial images of candidate flying regions via matching suitability analysis, which is based on visual attention mechanism and feature attribute classification. At first, visual attention mechanism is used to get the saliency map of the initial image by low-rank recovery and sparse coding. The salient regions are selected to be as preparatory results with threshold constraint and nonmaxima suppression. Then we use support vector machine (SVM) to divide the preparatory results into two classes for suitable or unsuitable waypoints based on their feature attributes, which can be represented by two edge-based descriptors and two correlation-based descriptors. The experimental results show that the proposed method is valid and effective.

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A Novel Detection Method of Paper Defects Based on Visual Attention Mechanism

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/japuc.2011070103 ◽

2011 ◽

Vol 3 (3) ◽

pp. 24-31 ◽

Cited By ~ 1

Author(s):

Ping Jiang ◽

Tao Gao

Keyword(s):

Visual Attention ◽

Detection Method ◽

Saliency Map ◽

Attention Mechanism ◽

Feature Maps ◽

Seed Point ◽

Multi Scale ◽

Comparative Maps ◽

Salient Points ◽

Visual Attention Mechanism

In this paper, an improved paper defects detection method based on visual attention mechanism computation model is presented. First, multi-scale feature maps are extracted by linear filtering. Second, the comparative maps are obtained by carrying out center-surround difference operator. Third, the saliency map is obtained by combining conspicuity maps, which is gained by combining the multi-scale comparative maps. Last, the seed point of watershed segmentation is determined by competition among salient points in the saliency map and the defect regions are segmented from the background. Experimental results show the efficiency of the approach for paper defects detection.

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A Novel Detection Method of Paper Defects Based on Visual Attention Mechanism

Global Applications of Pervasive and Ubiquitous Computing ◽

10.4018/978-1-4666-2645-4.ch018 ◽

2013 ◽

pp. 159-165 ◽

Cited By ~ 1

Author(s):

Ping Jiang ◽

Tao Gao

Keyword(s):

Visual Attention ◽

Detection Method ◽

Saliency Map ◽

Attention Mechanism ◽

Feature Maps ◽

Seed Point ◽

Multi Scale ◽

Comparative Maps ◽

Salient Points ◽

Visual Attention Mechanism

In this paper, an improved paper defects detection method based on visual attention mechanism computation model is presented. First, multi-scale feature maps are extracted by linear filtering. Second, the comparative maps are obtained by carrying out center-surround difference operator. Third, the saliency map is obtained by combining conspicuity maps, which is gained by combining the multi-scale comparative maps. Last, the seed point of watershed segmentation is determined by competition among salient points in the saliency map and the defect regions are segmented from the background. Experimental results show the efficiency of the approach for paper defects detection.

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