A Fast Image Thresholding Method Based on Chaos Optimization and Recursive Algorithm for Two-Dimensional Tsallis Entropy

Zhang, Xinming; Yan, Lin

doi:10.4304/jcp.5.7.1054-1061

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The previous adaptive threshold calculation methods based on 2D histogram include OTSU [23], [24], maximum entropy [10], some derived entropy methods (e.g., Renyi entropy [25], [26], Tsallis-Havrda-Charvat entropy [27], [28], K-L divergence [29], relative entropy [30] and Tsallis entropy [31]), and some entropy methods with additional conditions (the fuzzy set in [18] and weights in [32] were added to the entropy as additional constraints to calculate the maximum entropy threshold). It is worth mentioning that the interclass algorithm is not suitable for detection when the defect is out of proportion to the background, such as the OTSU algorithm.…”

Section: Image Threshold Segmentation Includes Bi-level Thresholdingmentioning

confidence: 99%

A Defect Detection Method for the Image With Intersecting Feature

Yuan

Liu

2020

IEEE Access

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Since the intersecting feature between the defect and the background of the image, the defect detection often results in under-segmentation or over-segmentation. To solve this problem, we propose a new defect extraction method by calculating the maximum mutual information of intersecting features. Firstly, we construct a new two-dimensional histogram according to the defect features. The new histogram is called Gray Level and Local Spatial Difference histogram (GLSD), which is constructed by grayscale and the improved local gray difference with the spatial relationship. Secondly, considering the geometric distribution of high-probability background events, we improve the segmentation shape of the background event distribution and divide the GLSD histogram preliminary. Finally, we calculate the maximum mutual information of the intersecting feature between the defect and the background. At this point, the boundary of the intersecting feature interval of the GLSD histogram is determined. To verify the effectiveness of the proposed method, we used two sets of databases for performance evaluation. The experimental results show that the proposed method is suitable for non-obvious defect detection under the local uniform background. Meanwhile, it can improve the sensitivity, specificity, and accuracy of defect detection compared with the classical threshold segmentation methods.

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Section: Image Threshold Segmentation Includes Bi-level Thresholdingmentioning

confidence: 99%

A Defect Detection Method for the Image With Intersecting Feature

Yuan

Liu

2020

IEEE Access

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“…With the development of multimedia technology, video data are highly increased, and Content-based Video Retrieval [1,2] is widely used. Key-frame extraction is the foundation of video content analysis and Contentbased Video Retrieval.…”

Section: Introductionmentioning

confidence: 99%

An Improved Keyframe Extraction Method Based on HSV Colour Space

Lin²,

Gao³

et al. 2013

JSW

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Video segmentation andkeyframe extraction are the basis of Content-based Video Retrieval (CBVR), inwhich keyframe selection is at the very core of CBVR. At shot level, key-frameextraction provides sufficient indexing and browsing of large video databases.In this paper, we proposed two improved approaches of key-frame extraction forvideo summarization. In our first synthesis method based on histogram-basedmethod and pixel-based method, videos were firstly segmented into shotsaccording to video content, by our improved histogram-based method, with theuse of histogram intersection and nonuniform partitioning and weighting. Then,the obtained results are secondly detected to optimize the results. On theother hand, we realized an improved clustering algorithm for video shotsegmentation, in consideration of video characteristics. Within each shot,key-frames were determined with the calculation of image entropy of every framein HSV colour space. Our simulation results in section 4 prove that extractedkey frames with our method are compact and faithful to the original video.Moreover, according to the types of test videos, different methods for shotsegmentation are highly recommended

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Color impulse noise removal by modified alpha trimmed median mean filter for FVIN

Yadav¹,

Singh²

2014

2014 IEEE International Conference on Computational Intelligence and Computing Research

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A Fast Image Thresholding Method Based on Chaos Optimization and Recursive Algorithm for Two-Dimensional Tsallis Entropy

Cited by 3 publications

References 6 publications

A Defect Detection Method for the Image With Intersecting Feature

A Defect Detection Method for the Image With Intersecting Feature

An Improved Keyframe Extraction Method Based on HSV Colour Space

Color impulse noise removal by modified alpha trimmed median mean filter for FVIN

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