Authors:
Jose M. Saavedra
1
;
Benjamin Bustos
2
and
Violeta Chang
2
Affiliations:
1
University of Chile and ORAND S.A., Chile
;
2
University of Chile, Chile
Keyword(s):
Hand Segmentation, Hand Localization, Color based Segmentation, Local Descriptors.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Segmentation and Grouping
;
Shape Representation and Matching
Abstract:
Hand segmentation is an important stage for a variety of applications such as gesture recognition and biometrics.
The accuracy of the hand segmentation process becomes more critical in applications that are based on
hand measurements as in the case of biometrics. In this paper, we present a very accurate hand segmentation
technique, relying on both hand localization and color information. First, our proposal locates a hand on an
input image, the hand location is then used to extract a training region which will play a critical role for segmenting
the whole hand in an accurate way. We use a structure-based method (STELA), originally proposed
for 3D model retrieval, for the hand localization stage. STELA exploits not only locality but also structural
information of the hand image and does not require a large image collection for training. Second, our proposal
separates the hand region from the background using the color information captured from the training region.
In this way, the se
gmentation depends only on the user skin color. This segmentation approach allows us to
handle a variety of skin colors and illumination conditions. In addition, our proposal is characterized by being
fully automatic, where a user calibration stage is not required. Our results show a 100% in the hand localization
process under different kinds of images and a very accurate hand segmentation achieving over 90% of
correct segmentation at the expense of having only 5% for false positives.
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