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Frontiers in the Convergence of Bioscience and Information Technologies, FBIT

作者： Sooyeun Jung Dongeun Lee Seongwon Lee Joonki Paik Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Department of Computer Engineering Kwangwoon University Seoul South Korea

In this paper we propose a method that identifies users at H.264 streaming using watermarking with fingerprints. The watermark can efficiently reduce the potential danger of forgery or alteration. Especially a biometric watermark has various advantages. Among entire biometric characteristics, the fingerprint is the most convenient and economical. In this paper we propose a novel fingerprint-based watermarking technique that can survive under very low bit-rate compression. The proposed algorithm consists of enhancement of a fingerprint image, watermark insertion using discrete wavelet transform. In this paper we also analyze H.264 compression pattern and its effects on watermarking. After restoring the damaged watermark sequence using various filters, the algorithm extracts the embedded watermark. The proposed algorithm can achieve robust watermark extraction against H.264 compressed videos.

关键词： Fingerprint recognition Watermarking Streaming media image coding Biometrics Discrete wavelet transforms Forgery image matching Pattern analysis image restoration

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Block matching-based background generation and non-rigid shape tracking for video surveillance

Block matching-based background generation and non-rigid sha...

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Taekyung Kim David Tohyun Paik Joonki Paik Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advance Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea College of Arts and Sciences Class of Biology major Boston University Boston USA

This paper presents a robust, real-time block matching-based tracking method for deformable objects. Block matching has been used for various image processing applications as well as video coding because it has a regular computing structure. Video tracking based on block matching, however, fails to provide correct motion information of the object when; (i) background changes spatio-temporally, (ii) an object deforms, and (iii) occlusion occurs. To solve these problems, background is first generated to localize a deformable object, and the object is defined by the corresponding boundary. Shape of the object is reconstructed by using a set of shape control points (SCPs) inside the object boundary, and the trajectory of each SCP is traced by block matching. We use an additional SCP Preserving step at every certain interval of frames for more efficient object tracking. For the experiment we used a computer-generated moving fish sequence and an outdoor sequence. Experimental results show that the proposed tracking algorithm can solve the problem of existing block matching-based methods, and at the same time it can track an object with occlusion and/or deformation.

关键词： Video surveillance Shape control Robustness Video compression image processing Tracking Wavelet analysis image analysis Pattern analysis Pattern recognition

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

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关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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Edge based adaptive Kalman filtering for real-time video stabilization

Edge based adaptive Kalman filtering for real-time video sta...

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International Conference on Consumer Electronics, ICCE '06

作者： Kwon, Ohyun Shin, Jeongho Paik, Joonki Image Processing and Intelligent Systems Laboratory Department of Image Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)0780394593

image stabilization, particularly during zoom-in is an important feature in consumer cameras. Existing methods to stabilize an image often make an incorrect motion vector by an improper choice of motion estimation or correction method. The proposed algorithm is simple and effective;it is suitable for low cost camcorders, digital cameras, CCTV, surveillance video systems, and television broadcasting systems. © 2006 IEEE.

关键词： Kalman filtering

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LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

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Asian Journal of Control 2008年第3期1卷

作者： Omid Shakernia Yi Ma T. John Koo Shankar Sastry Dept. of Electrical Engineering & Computer Science University of California at Berkeley Berkeley CA94720-1774 U.S.A. Tak-Kuen John Koo received the B.Eng. degree in 1992 in Electronic Engineering and the M.Phil. in 1994 in Information Engineering both from the Chinese University of Hong Kong. From 1994 to 1995 he was a graduate student in Signal and Image Processing Institute at the University of Southern California. He is currently a Ph.D. Candidate in Electrical Engineering and Computer Sciences at the University of California at Berkeley. His research interests include nonlinear control theory hybrid systems inertial navigation systems with applications to unmanned aerial vehicles. He received the Distinguished M.Phil. Thesis Award of the Faculty of Engineering The Chinese University of Hong Kong in 1994. He was a consultant of SRI International in 1998. Currently he is the team leader of the Berkeley AeRobot Team and a delegate of The Graduate Assembly University of California at Berkeley. He is a student member of IEEE and SIAM. S. Shankar Sastry received his Ph.D. degree in 1981 from the University of California Berkeley. He was on the faculty of MIT from 1980-82 and Harvard University as a Gordon McKay professor in 1994. He is currently a Professor of Electrical Engineering and Computer Sciences and Bioengineering and Director of the Electronics Research Laboratory at Berkeley. He has held visiting appointments at the Australian National University Canberra the University of Rome Scuola Normale and University of Pisa the CNRS laboratory LAAS in Toulouse (poste rouge) and as a Vinton Hayes Visiting fellow at the Center for Intelligent Control Systems at MIT. His areas of research are nonlinear and adaptive control robotic telesurgery control of hybrid systems and biological motor control. He is a coauthor (with M. Bodson) of “Adaptive Control: Stability Convergence and Robustness Prentice Hall 1989.” and (with R. Murray and Z. Li) of “A Mathematical Introduction to Robotic Manipulati

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

关键词： Motion estimation computer vision based control unmanned air vehicle differential flatness

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Edge based adaptive Kalman filtering for real-time video stabilization

Edge based adaptive Kalman filtering for real-time video sta...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Ohyun Kwon Jeongho Shin Joonki Paik Image Processing and Intelligent Systems Laboratory Department of Image Engineering Chung-Ang University Seoul South Korea

image stabilization, particularly during zoom-in is an important feature in consumer cameras. Existing methods to stabilize an image often make an incorrect motion vector by an improper choice of motion estimation or correction method. The proposed algorithm is simple and effective; it is suitable for low cost camcorders, digital cameras, CCTV, surveillance video systems, and television broadcasting systems.

关键词： Kalman filters Adaptive filters Filtering Motion estimation Costs Video equipment Digital cameras Surveillance TV Digital video broadcasting

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Multiresolution Object Recognition Using Dual Heuristic Programming

Multiresolution Object Recognition Using Dual Heuristic Prog...

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International Joint Conference on Neural Networks (IJCNN)

作者： F. Siddiqui K.M. Iftekharuddin Intelligent Systems and Image Processing Laboratory Department of Electrical and Computer Engineering University of Memphis Memphis TN USA

We present automatic target recognition (ATR) model based on principles of biological vision systems. The model employs reinforcement learning (RL) through Dual Heuristic Programming (DHP). The performance of the reported ATR model is compared to that of our previously reported ATR model, based on Heuristic Dynamic Programming (HDP). The HDP and DHP, known as the Adaptive Critic Designs (ACD), are neural network based implementation of Dynamic Programming (DP). The simulation shows promising results for both HDP and DHP based ATR model in presence of resolution distortion in incoming images and confirms that the DHP model is faster and more robust for our ATR system than the HDP.

关键词： Object recognition Learning Robustness Biological system modeling Machine vision Target recognition Dynamic programming Neural networks image resolution Nonlinear distortion

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Multi-Modal Human Verification Using Face and Speech

Multi-Modal Human Verification Using Face and Speech

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IEEE International Conference on Computer Vision systems (ICVS)

作者： Changhan Park Joonki Paik Taewoong Choi Soonhyob Kim Youngouk Kim J. Namkung Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Multimedia Signal Processing Laboratory Kwangwoon University Seoul South Korea Precision Machinery Center Korea Electronics and Technology Institute Puchon Gyeonggi South Korea Intelligent Image Communication Laboratory Department of Computer Engineering Kwangwoon University Seoul South Korea

In this paper, we propose a personal verification method using both face and speech to improve the rate of single biometric verification. False acceptance rate (FAR) and false rejection rate (FRR) have been a fundamental bottleneck of real-time personal verification. The proposed multimodal biometric method is to improve both verification rate and reliability in real-time by overcoming technical limitations of single biometric verification methods. The proposed method uses principal component analysis (PCA) for face recognition and hidden markov model (HMM) for speech recognition. It also uses fuzzy logic for the final decision of personal verification. Based on experimental results, the proposed system can reduce FAR down to 0.0001%, which provides that the proposed method overcomes the limitation of single biometric system and provides stable personal verification in real-time.

关键词： Humans Speech Face detection Face recognition Biometrics Principal component analysis Feature extraction Hidden Markov models image edge detection Laboratories

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Optimization of H.264 encoder using adaptive mode decision and SIMD instructions

Optimization of H.264 encoder using adaptive mode decision a...

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2005 Digest of Technical Papers - International Conference on Consumer Electronics, ICCE 2005

作者： Kim, Yong-Hwan Yoo, Jin-Woo Lee, Seong-Won Paik, Joonki Choi, Byeongho Image Processing and Intelligent Systems Laboratory Department of Image Engineering Chung-Ang University Seoul Korea Republic of Digital Media Research Center Korea Electronics Technology Institute

This paper presents algorithms that optimize the baseline H.264 encoder without loss of image quality and compression ratio. We propose an adaptive mode decision algorithm to speedup complex mode decision with rate distortion optimization (RDO). Also we optimize computation-intensive modules using single instruction multiple data (SIMD) instructions. As a result, the proposed encoder is ten times faster than JM72 reference encoder. ©2005 IEEE.

关键词： image coding

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Segmentation-based adaptive spatio-temporal filtering for noise canceling and MPEG pre-processing

Segmentation-based adaptive spatio-temporal filtering for no...

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2005 Digest of Technical Papers - International Conference on Consumer Electronics, ICCE 2005

作者： Lee, Seong-Won Maik, Vivek Jang, Jihoon Shin, Jeongho Paik, Joonki Image Processing and Intelligent Systems Laboratory Department of Image Engineering Chung-Ang University Seoul Korea Republic of R and D Center Next Chip Solution Co. Ltd. Seoul Korea Republic of

Noise reduction especially in low light level images is an important feature in consumer cameras. Existing methods to reduce such noise often degrade image quality due to an improper choice of filters. We present a high quality, low-cost noise reduction filter for enhancing low light level images. The proposed algorithm is simple, effective and computationally fast;it is suitable for low cost camcorders, digital cameras, CCTVs, and surveillance video systems. ©2005 IEEE.

关键词： Noise abatement

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