193 research outputs found
An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique
From The late 90th, "Skin Detection" becomes one of the major problems in
image processing. If "Skin Detection" will be done in high accuracy, it can be
used in many cases as face recognition, Human Tracking and etc. Until now so
many methods were presented for solving this problem. In most of these methods,
color space was used to extract feature vector for classifying pixels, but the
most of them have not good accuracy in detecting types of skin. The proposed
approach in this paper is based on "Color based image retrieval" (CBIR)
technique. In this method, first by means of CBIR method and image tiling and
considering the relation between pixel and its neighbors, a feature vector
would be defined and then with using a training step, detecting the skin in the
test stage. The result shows that the presenting approach, in addition to its
high accuracy in detecting type of skin, has no sensitivity to illumination
intensity and moving face orientation.Comment: 9 Pages, 4 Figure
Human Skin Detection Using RGB, HSV and YCbCr Color Models
Human Skin detection deals with the recognition of skin-colored pixels and
regions in a given image. Skin color is often used in human skin detection
because it is invariant to orientation and size and is fast to process. A new
human skin detection algorithm is proposed in this paper. The three main
parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue,
Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The
objective of proposed algorithm is to improve the recognition of skin pixels in
given images. The algorithm not only considers individual ranges of the three
color parameters but also takes into ac- count combinational ranges which
provide greater accuracy in recognizing the skin area in a given image.Comment: ICCASP/ICMMD-2016. Published by Atlantic Press. Part of series: AISR
ISBN: 978-94-6252-305-0 ISSN: 1951-685
Automatic skin segmentation for gesture recognition combining region and support vector machine active learning
Skin segmentation is the cornerstone of many applications such as gesture recognition, face detection, and objectionable image filtering. In this paper, we attempt to address the skin segmentation problem for gesture recognition. Initially, given a gesture video sequence, a generic skin model is applied to the first couple of frames to automatically collect the training data. Then, an SVM classifier based on active learning is used to identify the skin pixels. Finally, the results are improved by incorporating region segmentation. The proposed algorithm is fully automatic and adaptive to different signers. We have tested our approach on the ECHO database. Comparing with other existing algorithms, our method could achieve better performance
Face detection and clustering for video indexing applications
This paper describes a method for automatically detecting human faces in generic video sequences. We employ an iterative algorithm in order to give a confidence measure for the presence or absence of faces within video shots. Skin colour filtering is carried out on a selected number of frames per video shot, followed by the application of shape and size heuristics. Finally, the remaining candidate regions are normalized and projected into an eigenspace, the reconstruction error being the measure of confidence for presence/absence of face. Following this, the confidence score for the entire video shot is calculated. In order to cluster extracted faces into a set of face classes, we employ an incremental procedure using a PCA-based dissimilarity measure in con-junction with spatio-temporal correlation. Experiments were carried out on a representative broadcast news test corpus
Real-Time Hand Shape Classification
The problem of hand shape classification is challenging since a hand is
characterized by a large number of degrees of freedom. Numerous shape
descriptors have been proposed and applied over the years to estimate and
classify hand poses in reasonable time. In this paper we discuss our parallel
framework for real-time hand shape classification applicable in real-time
applications. We show how the number of gallery images influences the
classification accuracy and execution time of the parallel algorithm. We
present the speedup and efficiency analyses that prove the efficacy of the
parallel implementation. Noteworthy, different methods can be used at each step
of our parallel framework. Here, we combine the shape contexts with the
appearance-based techniques to enhance the robustness of the algorithm and to
increase the classification score. An extensive experimental study proves the
superiority of the proposed approach over existing state-of-the-art methods.Comment: 11 page
A Novel Scheme for Intelligent Recognition of Pornographic Images
Harmful contents are rising in internet day by day and this motivates the
essence of more research in fast and reliable obscene and immoral material
filtering. Pornographic image recognition is an important component in each
filtering system. In this paper, a new approach for detecting pornographic
images is introduced. In this approach, two new features are suggested. These
two features in combination with other simple traditional features provide
decent difference between porn and non-porn images. In addition, we applied
fuzzy integral based information fusion to combine MLP (Multi-Layer Perceptron)
and NF (Neuro-Fuzzy) outputs. To test the proposed method, performance of
system was evaluated over 18354 download images from internet. The attained
precision was 93% in TP and 8% in FP on training dataset, and 87% and 5.5% on
test dataset. Achieved results verify the performance of proposed system versus
other related works
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