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IEEE International Conference on image processing

作者： Wonseok Kang Jaehwan Jeon Eunsung Lee Changhun Cho Junghoon Jung Taechan Kim Aggelos K. Katsaggelos Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea System LSI Samsung Electronics Co. Ltd Suwon Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL 60208 USA

ISBN: (纸本)9781479923427

This paper presents a novel real-time super-resolution (SR) method using directionally adaptive image interpolation and image restoration. The proposed interpolation method estimates the edge orientation using steerable filters and performs edge refinement along the estimated edge orientation. Bi-linear and bi-cubic interpolation filters are then selectively used according to the estimated edge orientation for reducing jagging artifacts in slanting edge regions. The proposed restoration method can effectively remove image degradation caused by interpolation using the directionally adaptive truncated constrained least-squares (TCLS) filter. The proposed method provides high-quality magnified images which are similar to or better than the result of advanced interpolation or SR methods without high computational load. Experimental results indicate that the proposed system gives higher peak-to-peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values than the state-of-the-art image interpolation methods.

关键词： Super-resolution image interpolation image restoration digital zooming

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FREQUENCY-DOMAIN ANALYSIS OF DISCRETE WAVELET TRANSFORM COEFFICIENTS AND THEIR ADAPTIVE SHRINKAGE FOR ANTI-ALIASING

FREQUENCY-DOMAIN ANALYSIS OF DISCRETE WAVELET TRANSFORM COEF...

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IEEE International Conference on image processing

作者： Eunjung Chae Eunsung Lee Wonseok Kang Younghoon Lim Junghoon Jung Taechan Kim Aggelos K. Katsaggelos Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea System LSI Samsung Electronics Co. Ltd Suwon Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL 60208 USA

ISBN: (纸本)9781479923427

We present an antialiasing method using combined wavelet-Fourier transform and spatially adaptive shrinkage of the transform coefficients. Traditional antialiasing methods employ a simple low-pass filter onto the entire image, so the resulting image loses not only aliasing artifacts but also high-frequency components such as edges and ridges. The proposed algorithm analyzes the property of the LL subband of the discrete wavelet transform (DWT), and reduces aliasing artifacts using patch-adaptive shrinkage of the DWT coefficients. More specifically, an antialiased LL subband is obtained using adaptive patch-based aliasing reduction. To detect an aliased region, we subtract the discrete Fourier transform (DFT) coefficients of the LL subband from the DFT coefficients of antialiased LL subband. The detected aliasing artifacts in the LH, HL, and HH subbands are reduced by patch-wise adaptive shrinkage of the transform coefficients. The resulting antialiased image is obtained using the inverse DWT. The aliasing artifacts can be efficiently reduced by adaptively shrinking wavelet transform coefficients for preserving high-frequency image details. The proposed antialiasing algorithm is suitable for removing aliasing artifacts which frequently occur in imaging sensors with limited resolution.

关键词： Anti-aliasing wavelet-Fourier transform image enhancement patch-adaptive shrinkage antialiasing image enhancement Aliasing shrinkage Artifacts subband domain analysis Coefficients Sensors (image) Frequency-domain analysis discrete Fourier transform

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Automatic face region tracking for highly accurate face recognition in unconstrained environments

Automatic face region tracking for highly accurate face reco...

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IEEE Conference on advanced Video and Signal Based Surveillance (AVSS)

作者： Y.-O. Kim J. Paik Jingu Heo A. Koschan B. Abidi M. Abidi Korea Electronics and Technology Institute Puchon Gyeonggi South Korea Image Processing Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Imaging Robotics and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Tennessee Knoxville USA

We present a combined real-time face region tracking and highly accurate face recognition technique for an intelligent surveillance system. High-resolution face images are very important to achieving accurate identification of a human face. Conventional surveillance or security systems, however, usually provide poor image quality because they use only fixed cameras to record scenes passively. We have implemented a real-time surveillance system that tracks a moving face using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction with the PTZ. Color information in the ROI is updated to extract features for optimal tracking and zooming. FaceIt/sup /spl reg//, which is one of the most popular face recognition software packages, is evaluated and then used to recognize the faces from the video signal. Experimentation with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.

关键词： Face recognition Surveillance Real time systems Humans Cameras intelligent systems Security image quality Layout Low pass filters

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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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Dense range image smoothing using adaptive regularization

Dense range image smoothing using adaptive regularization

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IEEE International Conference on image processing

作者： Yiyong Sun Joon-Ki Paik J.R. Price M.A. Abidi Department of Electrical and Computer Engineering Imaging Robotics and Intelligent Systems Laboratory University of Tennessee Knoxville USA Department of Image Engineering Graduate School of Advanced Imaging Science Chung-Ang University Seoul South Korea

We propose an adaptive regularization algorithm for smoothing dense range images using a novel, first order stabilizing function. The stabilizer we suggest is based upon minimizing the reconstructed surface area and is derived in the native, spherical coordinate system of the range scanner. This allows adjustments to be made along only the direction of measurement, thereby preventing the data overlapping problem that can arise in dense images. Adaptation is achieved by adjusting the regularization parameter according to the results of 2D edge analysis. Results indicate effective noise suppression along with well preserved edges and details in the reconstructed, 3D surfaces.

关键词： Smoothing methods Surface reconstruction image reconstruction image edge detection intelligent robots Robot kinematics intelligent systems Laboratories Laser noise Noise measurement

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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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REGULARIZED ADAPTIVE SUPER-RESOLUTION USING KERNEL ESTIMATION-BASED EDGE RECONNECTION AND KERNEL ORIENTATION CONSTRAINTS

REGULARIZED ADAPTIVE SUPER-RESOLUTION USING KERNEL ESTIMATIO...

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IEEE International Conference on image processing

作者： Jaehwan Jeon Jinhee Lee Eunsung Lee Monson H. Hayes Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University (CAU) Seoul South Korea 1st Research and Development Center/CP group Nextchip Company Limited Seoul South Korea

This paper presents a spatially adaptive super-resolution (SR) algorithm using homogeneous region analysis for minimizing undesired interpolation artifacts such as aliasing and jagged edges. The proposed regularized SR algorithm incorporates two constraints enforcing (i) disconnected edges to be reconnected and (ii) orientation-adaptive regularization. By combining two constraints in the regularization framework, the proposed SR algorithm can significantly reduce aliasing artifacts and, as a result, produce edge-preserved high-resolution (HR) images. In addition to the formulation of the regularized SR algorithm with hybrid constraints, experimental results show that the proposed SR algorithm improves peak-to-peak signal-to-noise ratio (PSNR) of the reconstructed HR images by up to 5[dB]over existing state-of-the-art SR methods.

关键词： image edge detection Kernel image reconstruction Estimation Filtering algorithms Maximum likelihood detection image resolution

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Adaptively partitioned block-based backlit image enhancement for consumer mobile devices

Adaptively partitioned block-based backlit image enhancement...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Nahyun Kim Seungwon Lee Ewoo Chon Monson H. Hayes Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University (CAU) Seoul South Korea 1st Research and Development Center/CP group Nextchip Company Limited Seoul South Korea

In this paper, we present an efficient backlit region detection and enhancement method with low-computational cost for consumer mobile imaging devices. The proposed approach partitions an input image into multiple rectangular blocks, and uses a fuzzy logic-based optimal threshold to classify each block as backlit, background, or ambiguous. Backlit blocks are then processed using a contrast enhancement algorithm, whereas those block that are classified as ambiguous are further partitioned into four subblocks, and each subblock goes through further classification. The major advantage of the proposed method is computational efficiency and improved quality with minimized blocking artifacts compared with other block-based approaches.

关键词： Classification algorithms Partitioning algorithms image enhancement Algorithm design and analysis Histograms imaging Mobile communication

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Multimodal priority verification of face and speech using momentum back-propagation neural network

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Park, Changhan Ki, Myungseok Namkung, Jaechan Paik, Joonki Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University 221 Huksuk-dong Tongjak-Ku Seoul 156-756 Korea Republic of Broadcasting Media Research Group Digital Broadcasting Research Division ETRI 161 Gajeong-dong Yuseong-Gu Daejeon 305-700 Korea Republic of Intelligent Image Communication Laboratory Department of Computer Engineering Kwangwoon University 447-1 Wolge-dong Nowon-Gu Seoul 139-701 Korea Republic of

ISBN: (纸本)3540344373

In this paper, we propose a priority verification method for multimodal biometric features by using a momentum back-propagation artificial neural network (MBP-ANN). We also 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 MBPANN for the final decision of personal verification. Based on experimental results, the proposed system can reduce FAR down to 0.0001%, which proves that the proposed method overcomes the limitation of single biometric system and proves stable personal verification in real-time. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Neural networks

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Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

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Plant Phenomics 2023年第2期5卷 226-238页

作者： Yinglun Li Xiaohai Zhan Shouyang Liu Hao Lu Ruibo Jiang Wei Guo Scott Chapman Yufeng Ge Benoit de Solan Yanfeng Ding Frédéric Baret Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjingChina Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan School of Agriculture and Food Sciences The University of QueenslandSt.LuciaQueensland 4072Australia Department of Biological Systems Engineering University of Nebraska-LincolnLincolnNebraska 68583United States INRAE Avignon UniversitéUMR EMMAHUMT CAPTE228route de l’aérodrome-CS 4050984914 Avignon Cedex 9France ARVALIS Institut du Végétal 3 Rue Joseph et Marie Hackin75116 ParisFrance

The number of leaves at a given time is important to characterize plant growth and *** this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB *** digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning *** results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation ***,the spatial resolution should be better than 0.6 mm per pixel to identify leaf *** method is claimed to be self-supervised since no manual labeling is required for model *** self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping *** trained networks are available at https://***/YinglunLi/Wheat-leaf-tip-detection.

关键词： Wheat seedling Plant

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