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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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Optical flow-based tracking of deformable objects using a non-prior training active feature model 5th

Optical flow-based tracking of deformable objects using a no...

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5th Pacific Rim Conference on Multimedia, PCM 2004

作者： Kim, Sangjin Kang, Jinyoung Shin, Jeongho Lee, Seongwon Paik, Joonki Kang, Sangkyu Abidi, Besma Abidi, Mongi Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University 221 Huksuk-Dong Tongjak-Ku Seoul156-756 Korea Republic of Imaging Robotics and Intelligent Systems Laboratory Department of Electrical and Computer Engineering The University of Tennessee KnoxvilleTN37996-2100 United States

ISBN: (纸本)9783540239857

This paper presents a feature point tracking algorithm using optical flow under the non-prior training active feature model (NPT-AFM) framework. The proposed algorithm mainly focuses on analysis of deformable objects, and provides real-time, robust tracking. The proposed object tracking procedure can be divided into two steps: (i) optical flow-based tracking of feature points and (ii) NPT-AFM for robust tracking. In order to handle occlusion problems in object tracking, feature points inside an object are estimated instead of its shape boundary of the conventional active contour model (ACM) or active shape model (ASM), and are updated as an element of the training set for the AFM. The proposed NPT-AFM framework enables the tracking of occluded objects in complicated background. Experimental results show that the proposed NPT-AFM-based algorithm can track deformable objects in real-time. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Optical flows

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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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IEEE International Conference on Consumer Electronics (ICCE)

作者： Seong-Won Lee V. Maik Jihoon Jang Jeongho Shin Joonki Paik Image Processing and Intelligent Systems Laboratory Department of Image Engineering Chung Ang University Seoul South Korea Research & Development Center Next chip Solution Company Limited Seoul South Korea

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.

关键词： Adaptive filters Filtering Noise cancellation Noise reduction image segmentation Cameras Degradation image quality Costs Video equipment

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Plane identification and segmentation from uncalibrated stereo based on evolutionary approach

Plane identification and segmentation from uncalibrated ster...

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IEEE International Symposium on intelligent Signal processing and Communication systems (ISPACS)

作者： Junghoon Jung Taekyung Kim Ohyun Kwon 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

ISBN: (纸本)0780386396

Planes are widely used in various vision tasks due to their abundance especially in man-made environments. Assuming an uncalibrated stereo image acquisition system, we propose a new plane identification and segmentation algorithm using an evolutionary approach. The proposed algorithm enables faster and more robust estimation of the plane since it progressively searches the best solution in a guided direction. Experimental results show that the proposed algorithm has higher performance than the existing method.

关键词： image segmentation Iterative algorithms Evolutionary computation Robustness Transmission line matrix methods image processing intelligent systems Laboratories Multimedia systems Electronic mail

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