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检索条件"主题词=sparse coding"

共 2101 条记录，以下是351-360 订阅

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Regression-based sparse coding for Facial Point Detection 6

Regression-based Sparse Coding for Facial Point Detection

引用

6th International Conference on Virtual Reality and Visualization (ICVRV)

作者： Tan, Shuqiu Guo, Chenggang Chen, Dongyi Huang, Zhiqi Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China

ISBN: (纸本)9781509051885

Facial point detection in real-world conditions presents large variations in shapes and occlusions due to differences in poses, expressions, use of accessories, which may lead to a large difficultly in locating facial points. In this paper, we propose a regression-based sparse coding method for facial point detection. The method combines the regression-based concept with sparse reconstruction methods to search candidate facial feature points. Specifically, during training, the proposed method learns a group of differential shape dictionaries and local appearance dictionaries. Through system analysis, the results show that our approach outperforms the reference method in terms of detection accuracy.

关键词： sparse coding Regression-based method Facial feature point detection Face recognition

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ACCELERATING DYNAMIC MAGNETIC RESONANCE IMAGING BY NONLINEAR sparse coding 13

ACCELERATING DYNAMIC MAGNETIC RESONANCE IMAGING BY NONLINEAR...

引用

13th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Nakarmi, Ukash Zhou, Yihang Lyu, Jingyuan Slavakis, Konstantinos Ying, Leslie Univ Buffalo State Univ New York Dept Elect Engn Buffalo NY 14228 USA Univ Buffalo State Univ New York Dept Biomed Engn Buffalo NY USA

ISBN: (纸本)9781479923496;9781479923502

Although being high-dimensional, dynamic magnetic resonance images usually lie on low-dimensional manifolds. Nonlinear models have been shown to capture well that latent low-dimensional nature of data, and can thus lead to improvements in the quality of constrained recovery algorithms. This paper advocates a novel reconstruction algorithm for dynamic magnetic resonance imaging (dMRI) based on nonlinear dictionary learned from low-spatial but high-temporal resolution images. The nonlinear dictionary is initially learned using kernel dictionary learning, and the proposed algorithm subsequently alternates between sparsity enforcement in the feature space and the data-consistency constraint in the original input space. Extensive numerical tests demonstrate that the proposed scheme is superior to popular methods that use linear dictionaries learned from the same set of training data.

关键词： sparse coding kernel dictionary learning compressed sensing dynamic MRI

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USM Sharpening Detection Based on sparse coding

USM Sharpening Detection Based on Sparse Coding

引用

International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Gu, Yuzhou Wang, Shilin Lin, Xiang Sun, Tanfeng Shanghai Jiao Tong Univ Dept Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781509028962

USM sharpening is one of the most widely used sharpening methods. The detection of USM sharpening has attracted much concern in image forensics. Previous research has demonstrated that USM sharpening will cause overshoot artifacts along image edges. In order to detect such artifacts accurately and comprehensively, a sparse-coding based local feature representation is proposed in this paper. The K-SVD algorithm is adopted to build an over-complete dictionary characterizing the local textures along image edges. Then the sparse code for each local patch is calculated by the Orthogonal Matching Pursuit (OMP) algorithm and the overall feature for the image is constructed by max-pooling over local features. Experimental results have demonstrated the superior performance of the proposed approach compared with some state-of-the-art methods investigated.

关键词： Image forensics Sharpening detection sparse coding Max pooling

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Fast Image Super-resolution with sparse coding

Fast Image Super-resolution with Sparse Coding

引用

International Conference on Mechatronic, Manufacturing and Materials Engineering (MMME)

作者： Yuan, Zhi-chao Li, Ben-tu Shandong Vocat Coll Sci & Technol Weifang 261053 Peoples R China

In this paper, we introduce a novel fast image reconstruction method for super-resolution (SR) base on sparse coding. This method combine online dictionary learning and a fast sparse coding way, both of which can improve the efficiency of the reconstruction process and ensure the image visual quality. The new online optimization algorithm for dictionary learning based on stochastic approximations, which can drastically advance the learning speed, especially on millions of training samples. Meanwhile, we trained a neural network to speed up the reconstruction process, which based on iterative shrinkage-thresholding algorithm (ISTA), we called learned iterative shrinkage-thresholding algorithm (LISTA). It would produce best approximation sparse code with some fixed depth. We demonstrate that our approach can simultaneously improve the image fidelity and cost less computation.

关键词： SRIR sparse coding super-resolution fast image super-resolution

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SPECKLE NOISE REDUCTION VIA PHASE-AWARE CLUSTERING-BASED NON-LOCAL SIMULTANEOUS sparse coding 23

SPECKLE NOISE REDUCTION VIA PHASE-AWARE CLUSTERING-BASED NON...

引用

23rd IEEE International Conference on Image Processing (ICIP)

作者： Yu, Jinze Univ Tokyo Inst Ind Sci Tokyo Japan

ISBN: (纸本)9781467399616

Ultrasonography is an important tool and has been widely used in clinical applications, however, the physicians and surgeons still often suffers great difficulties in diagnosis and treatment due to the high speckle noise of ultrasound images. Existing speckle reduction methods usually over-smooth low contrast features since they are sensitive to contrast variations in the images. In our paper, we propose a novel learning based scheme to effectively remove the speckle noise while preserving the image features. This is achieved by incorporating a denoising algorithm non-local simultaneous sparse coding (NLSSC) which is modified by adding a pre-clustering process guided by feature asymmetry (FA). The proposed pre-clustering procedures can help better train the dictionaries in NLSSC and make them more feature-aware. Qualitative and quantitative experiments were carried out to evaluate the performance of our methods against some state-of-the-art on both synthetic and clinical ultrasound images. Experimental results demonstrate that our approach has effective performance and can produce good speckle reduced images.

关键词： Ultrasound speckle noise reduction sparse coding Non-local filtering Clustering Phase-aware

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A Cluster Sampling Method for Image Matting via sparse coding 14th

A Cluster Sampling Method for Image Matting via Sparse Codin...

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14th European Conference on Computer Vision (ECCV)

作者： Feng, Xiaoxue Liang, Xiaohui Zhang, Zili Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China

ISBN: (纸本)9783319464756;9783319464749

In this paper, we present a new image matting algorithm which solves two major problems encountered by previous samplingbased algorithms. The first is that existing sampling-based approaches typically rely on certain spatial assumptions in collecting samples from known regions, and thus their performance deteriorates if the underlying assumptions are not satisfied. Here, we propose a method that a more representative set of samples is collected so as not to miss out true samples. This is accomplished by clustering the foreground and background pixels and collecting samples from each of the clusters. The second problem is that the quality of matting result is determined by the goodness of a single sample pair which causes errors when sampling-based methods fail to select the best pairs. In this paper, we derive a new objective function for directly obtaining the estimation of the alpha matte from a bunch of samples. Comparison on a standard benchmark dataset demonstrates that the proposed approach generates more robust and accurate alpha matte than state-of-the-art methods.

关键词： Image matting Sampling Clustering sparse coding Foreground extraction

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Online Writer Identification using sparse coding and Histogram based descriptors 15

Online Writer Identification using Sparse Coding and Histogr...

引用

15th International Conference on Frontiers in Handwriting Recognition (ICFHR)

作者： Dwivedi, Isht Gupta, Swapnil Venugopal, Vivek Sundaram, Suresh Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati Assam India

ISBN: (纸本)9781509009817

In this paper, we present a novel scheme for text independent online writer identification. As a first contribution, we propose histogram based features, inspired from the area of object detection, to describe the structural primitives of handwriting. Secondly, we have used sparse coding techniques to learn prototypes, that describe the general writing characteristics of the authors. To the best of our knowledge, the present proposal is the first of its kind that exploits the sparse learning framework for online writer identification. In addition, we consider the inclusion of ideas from information retrieval into our sparse representation to formulate a novel descriptor for each document. The efficacy of our proposal is tested on the handwritten paragraphs and text lines of the IAM On-Line Handwriting Database. We also provide a quantitative comparison of performance of our histogram based features with Fourier and Wavelet descriptors. The results are promising

关键词： Online text-independent writer identification sparse coding Feature Extraction IAM On-Line Handwriting Database

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Classification of Hyperspectral Image Based on K-means and Structured sparse coding 3

Classification of Hyperspectral Image Based on K-means and S...

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3rd International Conference on Information Science and Control Engineering (ICISCE)

作者： Liu, Yang Wang, Yangyang Shenyang Aerosp Univ Coll Automat Shenyang Peoples R China

ISBN: (纸本)9781509025350

The combination of spatial and spectral information of hyperspectral image benefits the improvement of classification accuracy. The structured sparse coding is proposed to reconstruct the pixels of hyperspectral image. The reconstructed pixels characterize the spatial structure. The K-means method is used to form the dictionary, which has stronger representation ability. Finally, the classification is implemented according to the reconstruction residuals. The experiments are conducted on AVIRIS and the results show that the classification accuracy is improved obviously compared with the other state-of-the-art methods.

关键词： hyperspectral image classification K-means sparse coding reconstruction

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A sparse coding APPROACH FOR THE EFFICIENT REPRESENTATION AND SEGMENTATION OF WHITE MATTER FIBERS 13

A SPARSE CODING APPROACH FOR THE EFFICIENT REPRESENTATION AN...

引用

13th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Kumar, Kuldeep Desrosiers, Christian Ecole Technol Super Software & IT Engn Dept Montreal PQ Canada

ISBN: (纸本)9781479923496;9781479923502

A sparse coding method is proposed for the representation and segmentation of multi-subject white matter fiber tracts. Instead of representing bundles as a single centroid, this method learns a compact dictionary of training fibers, capable of describing the whole dataset, and encodes bundles as a sparse combination of dictionary prototypes. This provides an efficient and accurate way to segment new fiber data, without explicitly embedding fibers. A strategy based on the Nystrom method is used to approximate the pairwise similarities of training fibers. Experiments using dMRI data from the Human Connectome Project show the ability of our method to identify white matter bundles across subjects, and illustrates the impact of sparsity on the performance of the proposed method.

关键词： Dictionary learning sparse coding diffusion MRI white matter fibers clustering segmentation

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A comparative analysis of compression techniques - the sparse coding and BWT 2

A comparative analysis of compression techniques - the spars...

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2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC)

作者： Pradhan, Annapurna Pati, Nibedita Rup, Suvendu Panda, Avipsa S. Kanoje, Lalit Kumar Trident Acad Technol Bhubaneswar 751021 Orissa India IIIT Bhubaneswar Orissa India

The process of image compression has been the most researched area for decades. Image compression is a necessity for the transmission of images and the storage of images in an efficient manner. This is because image compression represents image having less correlated pixels, eliminates redundancy and also removes irrelevant pixels. The most commonly known techniques for image compression are JPEG and JPEG 2000. But these two have certain drawbacks and thus various other techniques have been popping up, of late. Recently, a growing interest has been marked for the use of basis selection algorithms for signal approximation and compression. In the recent past, the orthogonal and bi-orthogonal complete dictionaries (like the Discrete Cosine Transform (DCT) or wavelets) have been the dominant transform domain representations. But, the DCT and the wavelet transform techniques experience blocking and ringing artefacts and also these are not capable of capturing directional information. Hence, sparse coding method (by Orthogonal Matching Pursuit (OMP) algorithm) comes into picture. Another, novel technique that has taken up recent interests of the image compression area is the Burrows-Wheeler transform (BWT). BWT is generally applied prior to entropy encoding for a better regularity structure. The paper puts forth the comparison results of the methods of sparse approximation and BWT. The comparison analysis was done using the two techniques on various images, out of which one has been given in the paper. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： image compression BWT sparse coding orthogonal matching pursuit MTF coding

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