检索结果-内蒙古大学图书馆

IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Wonseok Kang Eunsung Lee Eunjung Chea Aggelos K. Katsaggelos Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Seoul Chung-Ang University Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL 60208 USA

ISBN: (纸本)9781479903573

In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method.

关键词： Tolerances physiologic tolerance Sampling system recovery image denoising image representation images Signal analysis Statistical analysis Statistical methods image denoising algorithm

来源：评论

学校读者我要写书评

暂无评论

REAL-TIME SUPER-RESOLUTION FOR DIGITAL ZOOMING USING FINITE KERNEL-BASED EDGE ORIENTATION ESTIMATION AND TRUNCATED image RESTORATION

REAL-TIME SUPER-RESOLUTION FOR DIGITAL ZOOMING USING FINITE ...

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Adaptive image compression using sparse dictionaries

Adaptive image compression using sparse dictionaries

引用

2012 19th International Conference on Systems, Signals and image processing, IWSSIP 2012

作者： Horev, Inbal Bryt, Ori Rubinstein, Ron Signal and Image Processing Lab. Department of Electrical Engineering Technion Haifa Israel Geometric Image Processing Lab. Department of Computer Science Technion Haifa Israel

ISBN: (纸本)9783200023284

Transform coding is a widely used image compression technique, where entropy reduction can be achieved by decomposing the image over a dictionary which provides compaction. Existing algorithms, such as JPEG and JPEG2000, utilize fixed dictionaries which are shared by the encoder and decoder. Recently, works utilizing content-specific dictionaries show promising results by focusing on specific classes of images and using highly specialized dictionaries. However, such approaches lose the ability to compress arbitrary images. In this paper we propose an input-adaptive compression approach, which encodes each input image over a dictionary specifically trained for it. The scheme is based on the sparse dictionary structure, whose compact representation allows relatively low-cost transmission of the dictionary along with the compressed data. In this way, the process achieves both adaptivity and generality. Our results show that although this method involves transmitting the dictionary, it remains competitive with the JPEG and JPEG2000 algorithms. © 2012 Institute of Telecommunica.

关键词： image compression

来源：评论

学校读者我要写书评

暂无评论

Contribution of non-scrambled chroma information in privacy-protected face images to privacy leakage

Contribution of non-scrambled chroma information in privacy-...

引用

10th International Workshop on Digital-Forensics and Watermarking, IWDW 2011

作者： Sohn, Hosik Lee, Dohyoung De Neve, Wesley Plataniotis, Konstantinos N. Ro, Yong Man Image and Video Systems Lab. Department of Electrical Engineering Korea Advanced Institute of Science and Technology Korea Republic of Multimedia Lab. Department of Electrical and Computer Engineering University of Toronto Canada

ISBN: (纸本)9783642322044

To mitigate privacy concerns, scrambling can be used to conceal face regions present in surveillance video content. Given that lightweight scrambling tools may not protect chroma information in order to limit bit rate overhead in heterogeneous usage environments, this paper investigates how the presence of non-scrambled chroma information in face regions influences the effectiveness of automatic and human face recognition (FR). To that end, we apply three automatic FR techniques to face images that have been privacy-protected by means of a layered scrambling technique developed for Motion JPEG XR, testing the effectiveness of automatic FR and layered scrambling using various experimental conditions. In addition, we investigate whether agreement exists between the judgments of 32 human observers and the output of automatic FR. Our experimental results demonstrate that human observers are not able to successfully recognize face images when simultaneously visualizing scrambled luma and non-scrambled chroma information. However, when an adversary has access to the coded bit stream structure, the presence of non-scrambled chroma information may significantly contribute to privacy leakage. By additionally applying layered scrambling to chroma information, our experimental results show that the amount of privacy leakage can be substantially decreased at the cost of an increase in bit rate overhead, and with the increase in bit rate overhead dependent on the number of scalability layers used. © 2012 Springer-Verlag.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Large Pose Invariant Face Recognition Using Feature-based Recurrent Neural Network

Large Pose Invariant Face Recognition Using Feature-based Re...

引用

International Joint Conference on Neural Networks

作者： Teddy Salan Khan M. Iftekharuddin Intelligent Systems and Image Processing Lab Department of Electrical and Computer Engineering University of Memphis Vision Lab Department of Electrical and Computer Engineering Old Dominion University

ISBN: (纸本)9781467314886

Cellular Simultaneous Recurrent Network (CSRN) is a novel bio-inspired recurrent neural network that mimics reinforcement learning in the brain. CSRN has been proven to be a powerful tool for learning and predicting temporal information in face image sequences. In this work, we propose a novel implementation of feature-based CSRN for large-scale pose invariant face recognition. We also report systematic evaluation and performance comparison of our feature-based CSRN method with other well-known standard algorithms (PCA, LDA, Bayesian Classifier and EBGM) using face recognition technology standards for large-scale pose invariant face recognition.

关键词： Large pose invariant face recognition Cellular Simultaneous Neural Network Feature extraction Motion unit Face Recognition Vendor Test Face Recognition Technology (FERET) program

来源：评论

学校读者我要写书评

暂无评论

Automated region growing for segmentation of brain lesion in diffusion-weighted MRI

Automated region growing for segmentation of brain lesion in...

引用

2012 International MultiConference of Engineers and computer Scientists, IMECS 2012

作者： Mohd Saad, N. Abu-Bakar, S.A.R. Muda, Sobri Mokji, M. Abdullah, A.R. Electronics and Computer Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia Computer Vision Video and Image Processing Research Lab. Faculty of Electrical Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Radiology Department Medical Faculty Universiti Kebangsaan Malaysia Jalan Yaacob Latif 56000 Cheras Kuala Lumpur Malaysia Electrical Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia

ISBN: (纸本)9789881925114

This paper presents an automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) using region growing approach. The lesions are acute infarction, haemorrhage, tumour and abscess. Region splitting and merging is used to detect the lesion region. Then, histogram thresholding technique is applied to automate the seeds selection. The region is iteratively grown by comparing all unallocated neighbour pixels to the seeds. The difference between pixel's intensity value and the region's mean is used as the similarity measure. Evaluation is made for performance comparison between automatic and manual seeds selection. Overall, automated region growing algorithm provides comparable results with the semi-automatic segmentation.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Single camera-based full depth map estimation using color shifting property of a multiple color-filter aperture

Single camera-based full depth map estimation using color sh...

引用

International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Seungwon Lee Junghyun Lee Monson H. Hayes Aggelos K. Katsaggelos Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

A multiple color-filter aperture (MCA) camera can provide depth information as well as color and intensity in the single-camera framework, where the MCA generates misalignment between color channels depending on the distance of a region-of-interest. In this paper, we present a single camera-based estimation of the full depth map using the color shifting property of the MCA. For estimating the color shifting vectors (CSVs) among red, green, and blue color channels, edges are extracted at each color channel. At the edge, we estimate CSVs using normalized cross correlation combined with color shifting mask map. A full depth map is then generated by depth interpolation using the matting Laplacian method from sparsely estimated CSVs at an edge location. Experimental results show that the proposed method can not only estimate the full depth map but also correct the misaligned color image to generate photorealistic color images using a single camera equipped with MCA.

关键词： image color analysis Cameras Estimation Channel estimation image edge detection Interpolation Apertures

来源：评论

学校读者我要写书评

暂无评论

Combining Multiple Feature Representations and AdaBoost Ensemble Learning for Reducing False-Positive Detections in computer-aided Detection of Masses on Mammograms

Combining Multiple Feature Representations and AdaBoost Ense...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Jae Young Choi Dae Hoe Kim Konstantinos N. Plataniotis Yong Man Ro with Multimedia Lab The Edward S Rogers Sr Department of Electrical and Computer Engineering University of Toronto Toronto Ontario M5S 3GA Canada with the Image and Video Systems Lab Department of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Republic of Korea

ISBN: (纸本)9781424441198

One of the drawbacks of current computer-aided Detection (CADe) systems is a high number of false-positive (FP) detections, especially for detecting mass abnormalities. In a typical CADe system, classifier design is one of the key steps for determining FP detection rates. This paper presents the effective classifier ensemble system for tackling FP reduction problem in CADe. To construct ensemble consisting of correct classifiers while disagreeing with each other as much as possible, we develop a new ensemble construction solution that combines data resampling underpinning AdaBoost learning with the use of different feature representations. In addition, to cope with the limitation of weak classifiers in conventional AdaBoost, our method has an effective mechanism for tuning the level of weakness of base classifiers. Further, for combining multiple decision outputs of ensemble members, a weighted sum fusion strategy is used to maximize a complementary effect for correct classification. Comparative experiments have been conducted on benchmark mammogram dataset. Results show that the proposed classifier ensemble outperforms the best single classifier in terms of reducing the FP detections of masses.

关键词： Representations and AdaBoost CADe the FP detections of masses

来源：评论

学校读者我要写书评

暂无评论

Fully automated region growing segmentation of brain lesion in diffusion-weighted MRI

IAENG International Journal of Computer Science

引用

IAENG International Journal of computer Science 2012年第2期39卷 155-164页

作者： Mohd Saad, N. Abu-Bakar, S.A.R. Muda, Sobri Mokji, M. Abdullah, A.R. Electronics and Computer Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia Faculty of Electrical Engineering Computer Vision Video and Image Processing Research Lab Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Radiology Department Medical Faculty Universiti Kebangsaan Malaysia 56000 Cheras Kuala Lumpur Malaysia Electrical Engineering Department Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka Malaysia

This paper presents a fully automatic segmentation of brain lesions from diffusion-weighted magnetic resonance imaging (DW-MRI or DWI). The lesions are infarction, hemorrhage, tumor and abscess. Pre-processing stage is performed for intensity normalization, background removal and intensity enhancement. Then, split and merge algorithm is designed. Several statistical features are discussed and evaluated to select the best feature as homogeneity criteria. Lesions are segmented by merging the homogenous regions according to the selected criteria. This process produces blocky lesion region. Then, histogram thresholding is acquired to automate the seeds selection for region growing process. The region is iteratively grown by comparing all unallocated neighboring pixels to the seeds. The difference between pixel's intensity value and the region's mean is used as the similarity measure. The proposed segmentation technique has been validated by using misclassified area (MA), false positive rate (FPR), false negative rate (FNR), mean absolute percentage error (MAPE) and pixel absolute error ratio (rerr), and compared with previous methods. The result shows that automatic region growing method can successfully segment the lesions and is suitable for analysis and classification of DWI.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：