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International Conference on Control, Engineering & Information Technology

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Univ Ouargla Fac. des nouvelles technologies de l'information et de la communication Lab. de Genie Electrique Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB School of Computing Engineering and Information Sciences Northumbria University

ISBN: (纸本)9781479982134

Automatic personal identification is becoming an increasingly important requirement in a variety of applications like access control, surveillance systems and physical buildings. In recent years, biometric systems are used in these fields, offering greater convenience and several advantages over traditional security. In this paper, we propose an efficient online personal identification system based on Finger-Knuckle-Print (FKP) images. In this study, the finger image is characterized by the two dimensional Bloc based Discrete Cosine Transform (2D-BDCT) coefficients. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same finger is transformed by the two dimensional Discrete Fourier Transform (2D-DFT). The response of this transformation is directly used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. The results on a medium-size database, 150 users, show good identification performance based on individual modalities as well as after fusing multiple finger types.

关键词： Biometrics Identification FKP 2D-BDCT 2D-DFT HMM Data fusion

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Robust human identity identification system by using hand biometric traits 26

Robust human identity identification system by using hand bi...

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2014 26th International Conference on Microelectronics, ICM 2014

作者： Meraoumia, Abdallah Chitroub, Salim Bouridane, Ahmed Univ Ouargla Fac. des Nouvelles Technologies de L'information et de la Communication Lab. de Génie Électrique Ouargla30 000 Algeria Signal and Image Processing Laboratory Electronics and Computer Science Faculty USTHB P.O. box 32 El Alia Bab Ezzouar Algiers16111 Algeria Department of Computer Science and Digital Technologies Northumbria University Newcastle Pandon Building Newcastle-upon-TyneNE2 1XE United Kingdom

ISBN: (纸本)9781479981533

Automatic personal identification has become an important issue in several applications, such as physical buildings and information systems. Nowadays, biometric techniques are an important and effective solution for automatic personal identification. One of the most popular biometric systems is based on the hand due to its ease of use. Hand has several modalities to be extracted, among them, Finger-Knuckle-Print (FKP) and PaLMprint (PLM), has attracted an increasing amount of attention. This paper investigates these modalities for elaborating an efficient multimodal biometric identification system. For that, the texture information of FKP and PLM is characterized by the Local Binary Pattern (LBP). During the matching phase, an Euclidian distance score is employed to measure the similarity between templates. The proposed system is tested and evaluated using a database of 165 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate. © 2014 IEEE.

关键词： Biometrics

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Multimodal biometric system based on palmprint using progressive image compression

Multimodal biometric system based on palmprint using progres...

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International Conference on Information Technology for Organizations Development (IT4OD)

作者： Djamel Samai Abdallah Meraoumia Salim Chitroub Noureddine Doghmane Abdelmalik Taleb-Ahmed Faculté des Nouvelles Technologies de l'Information et de la Communication Laboratoire de Génie Électrique Ouargla Algérie Electronics and Computer Science Faculty Signal and Image Processing Laboratory Algiers Algeria Department of Electronics Badji Mokhtar University Annaba Algeria UMR University of Valenciennes France

In this paper, we propose a multimodal palmprint verification system based on two bands (gray scale and near infrared) using a progressive image compression through the famous Set Partitioning In Hierarchical Trees (SPIHT) coder. Each palmprint image is compressed an decompressed at 0.5 bit per pixel (bpp). By exploiting the progressiveness of the SPIHT algorithm, we decompressed the palmprint images in two bitrates. We obtained three images, an image at 0.25 bpp which represents the image approximation, the other at 0.25 bpp which represents the edges or details, and the third is the global of the both preceding. After analyzing the extracted features from different bands, we propose a score-level fusion scheme to integrate the multimodal information. The palmprint verification experiments demonstrated the superiority of multibands fusion to each single band, which results in higher verification accuracy.

关键词： Biometrics (access control) Feature extraction image coding Databases image resolution Authentication Bit rate

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Performance of the H.263 Video Compression Standard

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Journal of VLSI signal processing Systems for signal, image, and Video Technology 1997年第2-3期17卷 101-111页

作者： Girod, Bernd Steinbach, Eckehard Färber, Nikolaus Telecommunications Institute University of Erlangen-Nuremberg 91058 Erlangen Cauerstrasse 7 Germany Georgia Institute of Technology United States University of Hannover Germany Inst. F. Theor. N. University of Hannover Germany Massachussetts Inst. of Technology Cambridge MA United States Research Laboratory of Electronics United States Department of Media Technology Media Laboratory Germany Department of Computer Graphics Germany Academy of Media Arts Cologne Germany Computer Science Section Cologne University Germany Digital Signal Processing Group Georgia Institute of Technology Atlanta GA United States Dept. of Elec. Eng./Telecom. University of Erlangen-Nuremberg Germany Telecommunications Institute Germany Vivo Software Inc. Waltham MA United States University of Karlsruhe Germany University of Essex United Kingdom ESIEE Paris France Image Communication Group Telecommunications Institute University of Erlangen-Nuremberg Germany University of Munich Germany Telecommunications Institute University of Erlangen-Nuremberg Germany Image Communication Group

The draft international standard ITU-T H.263 is closely related to the well known and widely used ITU-T Recommendation H.261. However, H.263 does provide the same subjective image quality at less than half the bit-rate. In this paper we investigate to what extend single enhancements of H.263 contribute to this performance gain, and consider the trade-off quality vs. complexity. Based on typical test sequences, H.263 with its various modes is compared to H.261 on the basis of rate distortion curves at bit-rates up to 128 kbps. At 64 kbps, the performance gain of H.263 in its default mode compared to H.261 is approximately 2 dB. This improvement is achieved with only little increase of complexity, and is mainly due to more accurate motion compensation with half-pel accuracy. Considering the trade-off quality vs. complexity, the combination of the optional coding-modes "Advanced prediction mode" and "PB-frames mode" is a good compromise, resulting in an additional performance gain of 1.5 dB PSNR at 64 kbps. The "Syntax-based arithmetic coding mode" on the other hand, offers only a very small performance gain (0.1 dB at 64 kbps) for its increased computational complexity. Results from profiling an H.263 software codec are presented in order to support complexity considerations of the optional coding-modes.

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

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Robust multispectral palmprint identification system by jointly using contourlet decomposition & Gabor Filter response

Robust multispectral palmprint identification system by join...

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International Conference on Security and Cryptography (SECRYPT)

作者： Abdallah Meraoumia Salim Chitroub Ahmed Bouridane Fac. des nouvelles technologies de l'information et de la communication Lab. de Génie Electrique Ouargla Algeria Electronics and Computer Science Faculty Signal and Image Processing Laboratory Algiers Algeria Department of Computer Science and Digital Technologies Northumbria University Newcastle Newcastle upon Tyne U.K

In current society, reliable identification and verification of individuals are becoming more and more necessary tasks for many fields, not only in police environment, but also in civilian applications, such as access control or financial transactions. Biometric systems are used nowadays in these fields, offering greater convenience and several advantages over traditional security methods based on something that you know (password) or something that you have (keys). In this paper, we propose an efficient online personal identification system based on Multi-Spectral Palmprint (MSP) images using Contourlet Transform (CT) and Gabor Filter (GF) response. In this study, the spectrum image is characterized by the contourlet coefficients sub-bands. Then, we use the Hidden Markov Model (HMM) for modeling the observation vector. In addition, the same spectrum is filtered by the Gabor filter. The real and imaginary responses of the filtering image are used to create another observation vector. Subsequently, the two sub-systems are integrated in order to construct an efficient multi-modal identification system based on matching score level fusion. Our experimental results show the effectiveness and reliability of the proposed method, which brings both high identification and accuracy rate.

关键词： Biometrics (access control) Feature extraction Hidden Markov models Databases Computational modeling Computed tomography Hamming distance

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Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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AIM 2019 challenge on real-world image super-resolution: Methods and results

arXiv

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arXiv 2019年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Fritsche, Manuel Gu, Shuhang Purohit, Kuldeep Kandula, Praveen Suin, Maitreya Rajagopalan, A.N. Joon, Nam Hyung Won, Yu Seung Kim, Guisik Kwon, Dokyeong Hsu, Chih-Chung Lin, Chia-Hsiang Huang, Yuanfei Sun, Xiaopeng Lu, Wen Li, Jie Gao, Xinbo Bell-Kligler, Sefi Computer Vision Lab ETH Zurich Indian Institute Of Technology Madras India Image Communication & Signal Processing Laboratory Hanyang University Seoul Korea Republic of CVML Chung-Ang University Department Management Information Systems National Pingtung University of Science and Technology Department of Electrical Engineering National Cheng-Kung University Xidian University Weizmann Institute of Science Israel

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the superresolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

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Cooperative MIMO Channel Modeling and Multi-Link Spatial Correlation Properties

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IEEE Journal on Selected Areas in communications 2012年第2期30卷 388-396页

作者： Xiang Cheng Cheng-Xiang Wang Haiming Wang Xiqi Gao Xiao-Hu You Dongfeng Yuan Bo Ai Qiang Huo Ling-Yang Song Bing-Li Jiao School of Electronics and Computer Science Peking University Beijing China Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University China Joint Research Institute for Signal and Image Processing School of Engineering & Physical Sciences Heriot-Watt University Edinburgh UK School of Information Science and Engineering South East University Najing China School of Information Science and Engineering Shandong University Jinan China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.

关键词： MIMO Correlation Channel models Scattering Educational institutions Fading Adaptation models

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Quality Inspection of Phalaenopsis Hybrids Using Hyperspectral Band Selection Techniques

Quality Inspection of Phalaenopsis Hybrids Using Hyperspectr...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yen-Chieh Ouyang Bo-Han Chen Meng-Chueh Lee Tsang-Sen Liu Mang Ou-Yang Hsian-Min Chen Chao-Cheng Wu Chia-Hsien Wen Min-Shao Shih Chein-I Chang Yung-Jhe Yan Department of Electrical Engineering National Chung Hsing University Taiwan Institute of communication Engineering National Chung Hsing University Taiwan Taiwan Agriculture Research Institute Nantou Taiwan Department of Electrical and Computer Engineering National Chiao-Tung University Hsinchu City Taiwan Center for Quantitative Imaging in Medicine Taichung Veterans General Hospital Taiwan Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Computer Science and Information Management Providence University Taiwan Remote Sensing Signal and Image Processing Laboratory University of Maryland Baltimore MD USA

Fusarium wilt on Phalaenopsis is a disease that makes farmers suffer seriously. Although Phalaenopsis does not die immediately with Fusarium wilt, it seriously decreases the quality that buyers cannot accept. In this paper, we introduce an emerging method to detect Fusarium wilt at the base of Phalaenopsis stems. The detection model divides Phalaenopsis samples into two categories, healthy and infection. The band selection (BS) processing technique based on band prioritization (BP) is applied to extract significant bands and eliminate redundant bands. Subsequently, some algorithms which are constrained energy minimization (CEM), spectral information divergence(SID) and SeQuential N-FINDER to detect the Fusarium wilt, and we hope the research would help farmers decrease their losses.

关键词： Hyperspectral imaging Support vector machines Diseases signal to noise ratio Hybrid fiber coaxial cables Correlation

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