{"title":"一种基于联合色深超像素搬运工距离的手势识别新算法","authors":"Chong Wang, S. Chan","doi":"10.1109/CIP.2014.6844497","DOIUrl":null,"url":null,"abstract":"This paper presents a novel hand gesture recognition algorithm based on Kinect. Using the depth and skeleton from Kinect, mark-less hand extraction is achieved. The hand shapes (depth) and corresponded textures (color) are represented in the form of superpixels, which better retain the overall shapes and color of the gestures to be recognized. Based on this representation, a novel distance metric, Superpixel Earth Mover's Distance (SP-EMD), is proposed to measure the dissimilarity between the hand gestures. The effectiveness of the proposed distance metric and recognition algorithm is illustrated by experimental results and a high mean accuracy of 98.8% for hand gesture recognition is achieved based on the joint color-depth SP-EMD.","PeriodicalId":117669,"journal":{"name":"2014 4th International Workshop on Cognitive Information Processing (CIP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"A new hand gesture recognition algorithm based on joint color-depth Superpixel Earth Mover's Distance\",\"authors\":\"Chong Wang, S. Chan\",\"doi\":\"10.1109/CIP.2014.6844497\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel hand gesture recognition algorithm based on Kinect. Using the depth and skeleton from Kinect, mark-less hand extraction is achieved. The hand shapes (depth) and corresponded textures (color) are represented in the form of superpixels, which better retain the overall shapes and color of the gestures to be recognized. Based on this representation, a novel distance metric, Superpixel Earth Mover's Distance (SP-EMD), is proposed to measure the dissimilarity between the hand gestures. The effectiveness of the proposed distance metric and recognition algorithm is illustrated by experimental results and a high mean accuracy of 98.8% for hand gesture recognition is achieved based on the joint color-depth SP-EMD.\",\"PeriodicalId\":117669,\"journal\":{\"name\":\"2014 4th International Workshop on Cognitive Information Processing (CIP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 4th International Workshop on Cognitive Information Processing (CIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CIP.2014.6844497\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 4th International Workshop on Cognitive Information Processing (CIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CIP.2014.6844497","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new hand gesture recognition algorithm based on joint color-depth Superpixel Earth Mover's Distance
This paper presents a novel hand gesture recognition algorithm based on Kinect. Using the depth and skeleton from Kinect, mark-less hand extraction is achieved. The hand shapes (depth) and corresponded textures (color) are represented in the form of superpixels, which better retain the overall shapes and color of the gestures to be recognized. Based on this representation, a novel distance metric, Superpixel Earth Mover's Distance (SP-EMD), is proposed to measure the dissimilarity between the hand gestures. The effectiveness of the proposed distance metric and recognition algorithm is illustrated by experimental results and a high mean accuracy of 98.8% for hand gesture recognition is achieved based on the joint color-depth SP-EMD.