Regression-Based Locating Landmark on Dynamic Humans

dc.contributor.authorJang, Deok-Kyeongen_US
dc.contributor.authorLee, Sung-Heeen_US
dc.contributor.editorBernhard Thomaszewski and KangKang Yin and Rahul Narainen_US
dc.date.accessioned2017-12-31T10:43:03Z
dc.date.available2017-12-31T10:43:03Z
dc.date.issued2017
dc.description.abstractWe present a novel framework that consists of two-level regressors for finding correlations between human shapes and landmark positions in both body part and holistic scales. To this end, we first develop pose invariant coordinates of landmarks that represent both local and global shape features by using the pose invariant local shape descriptors and their spatial relationships. Our body part-level regression deals with the shape features from only those body parts corresponding to a certain landmark. For this, we develop a method that identi es such body parts per landmark, by using geometric shape dictionary obtained through the bag of features method. Our method is nearly automatic, requiring human assistance only once to di erentiate the left and right sides, and shows the prediction accuracy comparable to or better than those of existing methods, with a test data set containing a large variation of human shapes and poses.en_US
dc.description.sectionheadersPoster Abstracts
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters
dc.identifier.doi10.1145/3099564.3106645
dc.identifier.isbn978-1-4503-5091-4
dc.identifier.pagesDeok-Kyeong Jang and Sung-Hee Lee-Computing methodologies Learning linear models; Shape analysis; KCCA, regression, segmentation, landmark detection
dc.identifier.urihttps://doi.org/10.1145/3099564.3106645
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1145/3099564-3106645
dc.publisherACMen_US
dc.subjectComputing methodologies Learning linear models
dc.subjectShape analysis
dc.subjectKCCA
dc.subjectregression
dc.subjectsegmentation
dc.subjectlandmark detection
dc.titleRegression-Based Locating Landmark on Dynamic Humansen_US
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