Authors:
Andrey Kopylov
;
Oleg Seredin
;
Olesia Kushnir
;
Inessa Gracheva
and
Aleksandr Larin
Affiliation:
Tula State University, Russian Federation
Keyword(s):
Hand Detection, One-class Classification, Pixel Color Classifier, Support Vector Data Description, Structure Transferring Filter, Skeleton Matching.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Bayesian Models
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Understanding
;
Kernel Methods
;
Pattern Recognition
;
Similarity and Distance Learning
;
Software Engineering
;
Theory and Methods
;
Video Analysis
Abstract:
In this paper we present a new method of hand detection in cluttered background for video stream processing.
At first, skin segmentation is performed by one-class color pixel classifier which is trained using just a face
image fragment without any background training sample. The modified version of one-class classifier is
proposed. For each pixel it returns the grade (probability) of its belonging to the skin category instead of
common binary decision. To adjust output of the one-class classifier the structure-transferring filter built on
probabilistic gamma-normal model is applied. It utilizes additional information about the structure of an image
and coordinates local decisions in order to achieve more robust segmentation results. To make a final decision
whether an image fragment is the image of human hand or not, the method of binary image matching based on
skeletonization is employed. The experimental study on segmentation and detection quality of the proposed
method shows promi
sing results.
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