Precise hand segmentation from a single depth image

We propose a new approach to segmenting a hand accurately from a single depth image. Given a depth image, we extract first a rough hand region of interest (RoI) including a hand and a part of an arm. Then, the RoI is partitioned into triangles by using a constrained Delaunay triangulation (CDT) appr...

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Bibliographic Details
Published in2016 23rd International Conference on Pattern Recognition (ICPR) pp. 2398 - 2403
Main Authors Minglei Li, Lei Sun, Qiang Huo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Image color analysis
Image segmentation
Merging
Proposals
Resource description framework
Sensors
Shape
Online AccessGet full text
DOI10.1109/ICPR.2016.7899995

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Summary:We propose a new approach to segmenting a hand accurately from a single depth image. Given a depth image, we extract first a rough hand region of interest (RoI) including a hand and a part of an arm. Then, the RoI is partitioned into triangles by using a constrained Delaunay triangulation (CDT) approach from which hand segmentation proposals are generated. Each segmentation proposal is evaluated by a shallow convolutional neural network (CNN) which is trained as a regression function to predict a confidence score for each proposal. Finally, the segmentation proposal with the highest confidence score is selected as our hand segmentation result. To evaluate the effectiveness of our approach, we use a set of real data containing more than 370,000 frames of hand depth images collected from 40 subjects with large variations in pose, orientation and sensing distance. Compared with segmentation results achieved by a random decision forest (RDF) based approach, our approach achieves much higher accuracy.
DOI:10.1109/ICPR.2016.7899995