A Contour based Image Segmentation Algorithm using Morphological Edge Detection

2006 ◽  
Author(s):  
Ying-Tung Hsiao ◽  
Cheng-Long Chuang ◽  
Joe-Air Jiang ◽  
Cheng-Chih Chien
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Segmentation Algorithm ◽  
Image Segmentation Algorithm

scholarly journals Performance comparison of segmentation algorithms for hand gesture recognition

2018 ◽  
Vol 7 (3) ◽  
pp. 1227
Author(s):  
Priyanka Parvathy D ◽  
Dr Kamalraj Subramaniam
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Gesture Recognition ◽  
Noise Removal ◽  
Hand Gesture Recognition ◽  
Segmentation Algorithm ◽  
Hand Gesture ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Image Segmentation Algorithm

The gestures presented in diverse backgrounds have to be accurately processed and segmented, for it to be classified precisely by the hand gesture recognition system. This study compares performance of the proposed Image Segmentation Algorithm with a standard Canny Edge Detection Algorithm by comparing the statistical values of the features obtained from the feature extraction stage, thus validating the importance of having a robust preprocessing stage for the hand gestures. The proposed algorithm uses Non-local Mean filter for noise removal and then an improved Global Swarm Optimization based Canny edge detection for extracting the edges. Features are extracted using two dimensional Multi-resolution Discrete Wavelet Transform (2D-DWT) combined with Gray-level Co-occurrence Matrix. The efficiency of the proposed Image Segmentation Algorithm is evaluated using Radial Basis Function Neural Network as the classifier.  

A hybrid image segmentation algorithm based on edge detection, thresholding and region growing

2007 ◽  
Author(s):  
Zhiming Xie ◽  
Guannan Chen ◽  
Rong Chen ◽  
Jinping Lei ◽  
Shangyuan Feng ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Region Growing ◽  
Segmentation Algorithm ◽  
Image Segmentation Algorithm

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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