A novel image segmentation algorithm based on visual saliency detection and integrated feature extraction

2016 ◽  
Author(s):  
Weiting Liu ◽  
Xue Qing ◽  
Jian Zhou
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Segmentation Algorithm ◽  
Visual Saliency Detection ◽  
Image Segmentation Algorithm

scholarly journals The Study of Randomized Visual Saliency Detection Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Yuantao Chen ◽  
Weihong Xu ◽  
Fangjun Kuang ◽  
Shangbing Gao
Keyword(s):  
Image Segmentation ◽  
Detection Method ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Input Image ◽  
Memory Space ◽  
Segmentation Process ◽  
Visual Saliency Detection

Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results.

The Agriculture Vision Image Segmentation Algorithm Based on Improved Quantum-Behaved Particle Swarm Optimization

2015 ◽  
Vol 713-715 ◽  
pp. 1947-1950 ◽  
Author(s):  
Ming Hui Deng ◽  
Zhan Cheng Li ◽  
Shao Peng Zhu
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Spatial Information ◽  
Vision System ◽  
Particle Swarm ◽  
Segmentation Algorithm ◽  
Extraction Technology ◽  
Swarm Optimization ◽  
Image Segmentation Algorithm

Image segmentation and feature extraction are the premise for machine vision system to analyze and identify the image. Threshold image segmentation algorithm according to the method of two dimension threshold has a lot of calculation in calculating the threshold, and the minimum error threshold method can not use the spatial information of image. This paper presents an improved quantum-behaved particle swarm optimization based on the night segmentation and feature extraction technology. This paper introduces the QPSO algorithm based on multi group and multi stage improvement. The QPSO optimizing algorithm gradually approaches the global optimum threshold value to achieve better convergence and stability. An algorithm of vision image segmentation and feature extraction based on improved quantum-behaved particle swarm optimization is designed. Experimental results show that the optimization process of this algorithm has less control parameters and faster convergence speed.

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

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