scholarly journals Deer Body Adaptive Threshold Segmentation Algorithm Based on Colour Space

2020 ◽  
Vol 64 (2) ◽  
pp. 1317-1328
Author(s):  
Sun Yuheng ◽  
Mu Ye ◽  
Feng Qin ◽  
Hu Tianli ◽  
Gong He ◽  
...  
Keyword(s):  
Adaptive Threshold ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Threshold Segmentation

An Adaptive Threshold Segmentation Algorithm for Gesture Segmentation

2014 ◽  
Vol 513-517 ◽  
pp. 457-460
Author(s):  
Mei Wang ◽  
Jzau Sheng Lin ◽  
Zhou Xing Fu ◽  
Guo Qing Meng
Keyword(s):  
Color Space ◽  
Adaptive Threshold ◽  
Segmentation Algorithm ◽  
Hand Gesture ◽  
Effect Evaluation ◽  
Skin Model ◽  
Threshold Segmentation ◽  
Space Model ◽  
Segmentation Approach ◽  
Gesture Segmentation

The key step for gesture recognition is hand gesture segmentation. A new dynamic thresholding segmentation approach named Adaptive Threshold Segmentation Algorithm (ATSA) was developed. The segmentation effect evaluation was conducted based on the construction of a new color space model of skin model. The images of hand gesture were processed by using the proposed ATSA and the Fixed Threshold Segmentation (FTS) algorithm. Comparing with FTS, the ATSA is experimentally demonstrated that the segmented result has a less brightness impact, a lower redundancy rate, a lower rate of false alarm and missing, and a higher integrity rate.

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 />

The Detection Method of Lane Line Based on the Improved Otsu Threshold Segmentation

2015 ◽  
Vol 741 ◽  
pp. 354-358 ◽  
Author(s):  
Yang Shan Tang ◽  
Dao Hua Xia ◽  
Gui Yang Zhang ◽  
Li Na Ge ◽  
Xin Yang Yan
Keyword(s):  
Image Segmentation ◽  
Processing Time ◽  
Detection Method ◽  
Segmentation Algorithm ◽  
Test Results ◽  
Threshold Segmentation ◽  
Otsu Method ◽  
Time Requirements ◽  
Lane Line ◽  
Center Distance

For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentation algorithm is 144 and the threshold of the improved Otsu threshold segmentation algorithm is 131, the processing time is within 0.453 s. Test results show that the white part markings appear more, the intersection place of white lines and the background is more clear, so this method can identify lane markings well and meet the real-time requirements.

An Improved Adaptive Threshold Skin Color Model

2014 ◽  
Vol 610 ◽  
pp. 358-361
Author(s):  
Hong Wei Di ◽  
Wei Xu
Keyword(s):  
Skin Color ◽  
Adaptive Threshold ◽  
Experimental Results ◽  
Color Model ◽  
Threshold Segmentation ◽  
Change Rate ◽  
Skin Segmentation ◽  
Strong Robustness ◽  
Skin Color Model ◽  
Optimum Threshold

To solve the problem that traditional threshold segmentation model is not very robust in skin segmentation under different skin colors and different illuminations, an improved adaptive skin color model is proposed. This model detects the change rate of the skin color pixels by modifying the certain threshold while fixing others, then selects the optimum threshold adaptively. The experimental results show that this algorithm can effectively distinguish skin color regions and background regions, and has strong robustness on light disturbance.

Image Threshold Segmentation Algorithm Based on Histogram Statistical Property

2014 ◽  
Vol 644-650 ◽  
pp. 4027-4030
Author(s):  
Hong Li ◽  
Zhong An Lai ◽  
Jun Wei Lei
Keyword(s):  
Normal Distribution ◽  
Complex Shape ◽  
Good Method ◽  
Segmentation Algorithm ◽  
Gray Level ◽  
Background Region ◽  
Threshold Segmentation ◽  
Target Region ◽  
Statistical Property ◽  
Otsu’S Method

The traditional Otsu threshold algorithm is not a good method for processing the real images because of complex shape and unbalanced distribution. To solve this problem, the paper uses the thinking of Otsu’s method for reference, introduces a threshold segmentation algorithm based on histogram statistical property. In addition, the paper draws a comparison between the new algorithm and Otsu’s algorithm. Experimental results show that the new algorithm can get better segmentation effect than that of Otsu’s method when the gray-level distribution of the background follows normal distribution approximatively, and the target region is less than the background region.

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