2017
DOI: 10.1016/j.ins.2017.02.021
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Gesture segmentation based on a two-phase estimation of distribution algorithm

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Cited by 28 publications
(6 citation statements)
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“…In addition, we hope to show the performance of DCABC by Null Hypothesis Significance Testing (NHST) [35,36] in our future work. We only test the new algorithm on classical benchmark functions and have not used it to solve practical problems, such as fault diagnosis [37], path plan [38], Knapsack [39][40][41], multi-objective optimization [42], gesture segmentation [43], unit commitment problem [44], and so on. There is an increasing interest in prompting the performance of DCABC, which will be our future research direction.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, we hope to show the performance of DCABC by Null Hypothesis Significance Testing (NHST) [35,36] in our future work. We only test the new algorithm on classical benchmark functions and have not used it to solve practical problems, such as fault diagnosis [37], path plan [38], Knapsack [39][40][41], multi-objective optimization [42], gesture segmentation [43], unit commitment problem [44], and so on. There is an increasing interest in prompting the performance of DCABC, which will be our future research direction.…”
Section: Discussionmentioning
confidence: 99%
“…Threshold segmentation is easy to calculate and implement, but it has low fault tolerance and can be easily disturbed by noise. Therefore, it is usually used to preprocess data [3] but not the main steps in image segmentation tasks [4] . Some other traditional methods, such as the k-means algorithm [5] and fuzzy c-means (FCM) algorithm [6] based on clustering [7] , regional competition segmentation method based on the active contour model [8] , and region growing and merging algorithm [9] , can be used.…”
Section: Related Workmentioning
confidence: 99%
“…Regarding the location of the hand, also known as the hand detection, the authors of [26][27][28] extract the hand from the body using depth indices and by setting a threshold, estimated at a specific moment. The authors of [29,30] used skin color maps, and the authors of [31,32] achieved better segmentation results using both depth thresholding and skin detection (using color). In terms of classification, several CNN-based approaches relied on hand-crafted features [33,34], which can capture information about the silhouette, shape, and structure.…”
Section: Hand Gesture Recognition (Detection and Classification)mentioning
confidence: 99%