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Adjacent Pixel
The Experts below are selected from a list of 213 Experts worldwide ranked by ideXlab platform
Yuhyih Lu – 1st expert on this subject based on the ideXlab platform
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high capacity reversible data hiding with Adjacent Pixel based difference expansion
International Conference on Innovative Computing Information and Control, 2009Co-Authors: Hsiangcheh Huang, Ihung Wang, Yuhyih LuAbstract:Data hiding has been an interesting research topic since the early 1990’s. Among the hiding schemes, reversible data hiding has attracted more and more attention in both researches and applications. With reversible data hiding, at the data extraction stage, both the original content and the hidden message should be perfectly extracted, hence, how to design such schemes seem an interesting task. It can be classified into two branches, one is histogram-based scheme, and the other is performed by adjusting the difference between Adjacent Pixels. The major insufficiency for the two schemes above is the limited amount of capacity. We consider the advantages of both schemes and propose a method to hide much more data for reversible data hiding in comparison with conventional schemes. Simulation results demonstrate the advantages of the proposed method.
Tadahiro Ohmi – 2nd expert on this subject based on the ideXlab platform
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a robust face recognition algorithm using markov stationary features and Adjacent Pixel intensity difference quantization histogram
Signal-Image Technology and Internet-Based Systems, 2011Co-Authors: Koji Kotani, Qiu Chen, Tadahiro OhmiAbstract:In this paper, we present a robust face recognition algorithm using Adjacent Pixel intensity difference quantization (APIDQ) histogram combined with Markov Stationary Features (MSF). Previously, we have proposed a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram. After the intensity variation vectors for all the Pixels in an image are calculated, each vector is quantized directly in (dIx, dIy) plane instead of polar plane. By counting the number of elements in each quantized area in the (dIx, dIy) plane, a histogram can be created. This histogram, obtained by APIDQ for facial images, is utilized as a very effective personal feature. In this paper, we combine the APIDQ histogram with MSF so as to add spatial structure information to histogram. Experimental results show maximum average recognition rate of 97.16% is obtained for 400 images of 40 persons from the publicly available face database of AT&T Laboratories Cambridge. Furthermore, Top 1 recognition rate of 98.2% is achieved by using FB task of the publicly available face database of FERET.
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SITIS – A Robust Face Recognition Algorithm Using Markov Stationary Features and Adjacent Pixel Intensity Difference Quantization Histogram
2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems, 2011Co-Authors: Koji Kotani, Qiu Chen, Tadahiro OhmiAbstract:In this paper, we present a robust face recognition algorithm using Adjacent Pixel intensity difference quantization (APIDQ) histogram combined with Markov Stationary Features (MSF). Previously, we have proposed a very simple yet highly reliable face recognition algorithm using Adjacent Pixel Intensity Difference Quantization (APIDQ) histogram. After the intensity variation vectors for all the Pixels in an image are calculated, each vector is quantized directly in (dIx, dIy) plane instead of polar plane. By counting the number of elements in each quantized area in the (dIx, dIy) plane, a histogram can be created. This histogram, obtained by APIDQ for facial images, is utilized as a very effective personal feature. In this paper, we combine the APIDQ histogram with MSF so as to add spatial structure information to histogram. Experimental results show maximum average recognition rate of 97.16% is obtained for 400 images of 40 persons from the publicly available face database of AT&T Laboratories Cambridge. Furthermore, Top 1 recognition rate of 98.2% is achieved by using FB task of the publicly available face database of FERET.
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fast and efficient search for mpeg 4 video using Adjacent Pixel intensity difference quantization histogram feature
International Conference on Digital Image Processing, 2010Co-Authors: Koji Kotani, Qiu Chen, Tadahiro OhmiAbstract:In this paper, a fast search algorithm for MPEG-4 video clips from video database is proposed. An Adjacent Pixel
intensity difference quantization (APIDQ) histogram is utilized as the feature vector of VOP (video object plane), which
had been reliably applied to human face recognition previously. Instead of fully decompressed video sequence, partially
decoded data, namely DC sequence of the video object are extracted from the video sequence. Combined with active
search, a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has
been evaluated by total 15 hours of video contained of TV programs such as drama, talk, news, etc. to search for given
200 MPEG-4 video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect
the similar video clip in merely 80ms, and Equal Error Rate (ERR) of 2 % in drama and news categories are achieved,
which are more accurately and robust than conventional fast video search algorithm.
Hsiangcheh Huang – 3rd expert on this subject based on the ideXlab platform
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high capacity reversible data hiding with Adjacent Pixel based difference expansion
International Conference on Innovative Computing Information and Control, 2009Co-Authors: Hsiangcheh Huang, Ihung Wang, Yuhyih LuAbstract:Data hiding has been an interesting research topic since the early 1990’s. Among the hiding schemes, reversible data hiding has attracted more and more attention in both researches and applications. With reversible data hiding, at the data extraction stage, both the original content and the hidden message should be perfectly extracted, hence, how to design such schemes seem an interesting task. It can be classified into two branches, one is histogram-based scheme, and the other is performed by adjusting the difference between Adjacent Pixels. The major insufficiency for the two schemes above is the limited amount of capacity. We consider the advantages of both schemes and propose a method to hide much more data for reversible data hiding in comparison with conventional schemes. Simulation results demonstrate the advantages of the proposed method.