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RISS 인기검색어
- 검색키워드
- 저자 : Abdelmalik, Ouamane (검색결과 5 건)
- 무료
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- 유료
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Evaluation of Histograms Local Features and Dimensionality Reduction for 3D Face Verification
Ammar, Chouchane,Mebarka, Belahcene,Abdelmalik, Ouamane,Salah, Bourennane Korea Information Processing Society 2016 Journal of information processing systems Vol.12 No.3
The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.
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Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods
Elhannachi, Sid Ahmed,Benamrane, Nacera,Abdelmalik, Taleb-Ahmed Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.1
Since the progress of digital medical imaging techniques, it has been needed to compress the variety of medical images. In medical imaging, reversible compression of image's region of interest (ROI) which is diagnostically relevant is considered essential. Then, improving the global compression rate of the image can also be obtained by separately coding the ROI part and the remaining image (called background). For this purpose, the present work proposes an efficient reversible discrete cosine transform (RDCT) based embedded image coder designed for lossless ROI coding in very high compression ratio. Motivated by the wavelet structure of DCT, the proposed rearranged structure is well coupled with a lossless embedded zerotree wavelet coder (LEZW), while the background is highly compressed using the set partitioning in hierarchical trees (SPIHT) technique. Results coding shows that the performance of the proposed new coder is much superior to that of various state-of-art still image compression methods.
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Adaptive Medical Image Compression Based on Lossy and Lossless Embedded Zerotree Methods
( Sid Ahmed Elhannachi ),( Nacera Benamrane ),( Taleb-ahmed Abdelmalik ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.1
Since the progress of digital medical imaging techniques, it has been needed to compress the variety of medical images. In medical imaging, reversible compression of image`s region of interest (ROI) which is diagnostically relevant is considered essential. Then, improving the global compression rate of the image can also be obtained by separately coding the ROI part and the remaining image (called background). For this purpose, the present work proposes an efficient reversible discrete cosine transform (RDCT) based embedded image coder designed for lossless ROI coding in very high compression ratio. Motivated by the wavelet structure of DCT, the proposed rearranged structure is well coupled with a lossless embedded zerotree wavelet coder (LEZW), while the background is highly compressed using the set partitioning in hierarchical trees (SPIHT) technique. Results coding shows that the performance of the proposed new coder is much superior to that of various state-of-art still image compression methods.
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Medical Image Compression Using Quincunx Wavelets and SPIHT Coding
Beladgham, Mohammed,Bessaid, Abdelhafid,Taleb-Ahmed, Abdelmalik,Boucli Hacene, Ismail The Korean Institute of Electrical Engineers 2012 Journal of Electrical Engineering & Technology Vol.7 No.2
In the field of medical diagnostics, interested parties have resorted increasingly to medical imaging. It is well established that the accuracy and completeness of diagnosis are initially connected with the image quality, but the quality of the image is itself dependent on a number of factors including primarily the processing that an image must undergo to enhance its quality. This paper introduces an algorithm for medical image compression based on the quincunx wavelets coupled with SPIHT coding algorithm, of which we applied the lattice structure to improve the wavelet transform shortcomings. In order to enhance the compression by our algorithm, we have compared the results obtained with those of other methods containing wavelet transforms. For this reason, we evaluated two parameters known for their calculation speed. The first parameter is the PSNR; the second is MSSIM (structural similarity) to measure the quality of compressed image. The results are very satisfactory regarding compression ratio, and the computation time and quality of the compressed image compared to those of traditional methods.
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Evaluation of Histograms Local Features and Dimensionality Reduction for 3D Face Verification
( Chouchane Ammar ),( Belahcene Mebarka ),( Ouamane Abdelmalik ),( Bourennane Salah ) 한국정보처리학회 2016 Journal of information processing systems Vol.12 No.3
The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.
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