Regression-Based Landmark Detection on Dynamic Human Models

dc.contributor.authorJang, Deok-Kyeongen_US
dc.contributor.authorLee, Sung-Heeen_US
dc.contributor.editorJernej Barbic and Wen-Chieh Lin and Olga Sorkine-Hornungen_US
dc.date.accessioned2017-10-16T05:23:51Z
dc.date.available2017-10-16T05:23:51Z
dc.date.issued2016
dc.description.abstractDetecting anatomical landmarks on various human models with dynamic poses remains an important and challenging problem in computer graphics research. We 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 that correspond to a certain landmark. In order to do this, we develop a method that identifies such body parts per landmark, by using geometric shape dictionary obtained through the bag of features method. Our method is nearly automatic, as it requires human assistance only once to differentiate the left and right sides. The method also 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.number7
dc.description.sectionheadersAnalyzing Geometries
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.13273
dc.identifier.issn1467-8659
dc.identifier.pages73-82
dc.identifier.urihttps://doi.org/10.1111/cgf.13273
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13273
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.5 [Computer Graphics]
dc.subjectComputational Geometry and Object Modeling
dc.subjectGeometric algorithms
dc.subjectlanguages
dc.subjectand systems
dc.titleRegression-Based Landmark Detection on Dynamic Human Modelsen_US
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