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Dictionary learning

Xiao-Yuan Jing, Xiaoke Zhu, Fei Wu, Ruimin Hu, Xinge You, Yunhong Wang, Hui Feng, Jing-Yu Yang
Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification task...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Jie Zhang, Jie Shi, Cynthia Stonnington, Qingyang Li, Boris A Gutman, Kewei Chen, Eric M Reiman, Richard J Caselli, Paul M Thompson, Jieping Ye, Yalin Wang
Mild Cognitive Impairment (MCI) is a transitional stage between normal age-related cognitive decline and Alzheimer's disease (AD). Here we introduce a hyperbolic space sparse coding method to predict impending decline of MCI patients to dementia using surface measures of ventricular enlargement. First, we compute diffeomorphic mappings between ventricular surfaces using a canonical hyperbolic parameter space with consistent boundary conditions and surface tensor-based morphometry is computed to measure local surface deformations...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Sheng Li, Kang Li, Yun Fu
The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semisupervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively. Self-taught learning, which is a special type of transfer learning, has fewer restrictions on the choice of auxiliary data. It has shown promising performance in visual learning. However, existing self-taught learning methods usually ignore the structure information in data...
January 2, 2017: IEEE Transactions on Neural Networks and Learning Systems
Shaohua Qin, Juan Yin, Hongsheng Li, Jinhu Chen, Yong Yin, Dengwang Li
PURPOSE: We proposed a new dictionary learning algorithm (AK-SVD) based on K-SVD. AK-SVD can denoise the CBCT image, and did not need the noise information as prior knowledge. METHODS: The AK-SVD had two steps: signal sparse representation, and then dictionary optimization. The CBCT image was sparse, and there were limited big coefficients. The other coefficients were zero or near zero. In the sparse representation step of traditional K-SVD, the noise variance was used as a threshold to select the big representation coefficients...
June 2016: Medical Physics
Q Xu, H Liu, H Yu, G Wang, L Xing
PURPOSE: Spectral CT enabled by an energy-resolved photon-counting detector outperforms conventional CT in terms of material discrimination, contrast resolution, etc. One reconstruction method for spectral CT is to generate a color image from a reconstructed component in each energy channel. However, given the radiation dose, the number of photons in each channel is limited, which will result in strong noise in each channel and affect the final color reconstruction. Here we propose a novel dictionary learning method for spectral CT that combines dictionary-based sparse representation method and the patch based low-rank constraint to simultaneously improve the reconstruction in each channel and to address the inter-channel correlations to further improve the reconstruction...
June 2016: Medical Physics
Q Xu, H Han, L Xing
PURPOSE: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. METHODS: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out...
June 2016: Medical Physics
X Yang, A Jani, P Rossi, H Mao, W Curran, T Liu
PURPOSE: To enable MRI-guided prostate radiotherapy, MRI-CT deformable registration is required to map the MRI-defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey-level intensity characteristics between MRI and CT images, the integration of MRI into CT-based radiotherapy is very challenging. We are developing a learning-based registration approach to address this technical challenge. METHODS: We propose to estimate the deformation between MRI and CT images in a patch-wise fashion by using the sparse representation technique...
June 2016: Medical Physics
Shuai Yang, Jiaying Liu, Yuming Fang, Zongming Guo
In this paper, we present a novel method to super-resolve and recover the facial depth map nicely. The key idea is to exploit the exemplar-based method to obtain the reliable face priors from high-quality facial depth map to improve the depth image. Specifically, a new neighbor embedding (NE) framework is designed for face prior learning and depth map reconstruction. First, face components are decomposed to form specialized dictionaries and then reconstructed, respectively. Joint features, i.e., low-level depth, intensity cues and high-level position cues, are put forward for robust patch similarity measurement...
December 22, 2016: IEEE Transactions on Cybernetics
Linyuan He, Jizhong Zhao, Nanning Zheng, Duyan Bi
Fog cover is generally present in outdoor scenes, which limits the potential for efficient information extraction from images. In this paper, the goal of the developed algorithm is to obtain an optimal transmission map as well as to remove hazes from a single input image. To solve the problem, we meticulously analyze the optical model and recast the initial transmission map under an additional boundary prior. For better preservation of the results, the difference-structure-preservation dictionary could be learned such that the local consistency features of the transmission map could be well preserved after coefficient shrinkage...
December 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Szu-Wei Fu, Pei-Chun Li, Ying-Hui Lai, Cheng-Chien Yang, Li-Chun Hsieh, Yu Tsao
OBJECTIVE: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: (1) the amount of training data may be limited (because speaking for a long time is usually difficult for post-operative patients); (2) rapid conversion is desirable (for better communication)...
December 23, 2016: IEEE Transactions on Bio-medical Engineering
Duy Duc An Bui, Guilherme Del Fiol, John F Hurdle, Siddhartha Jonnalagadda
OBJECTIVES: Extracting data from publication reports is a standard process in systematic review (SR) development. However, the data extraction process still relies too much on manual effort which is slow, costly, and subject to human error. In this study, we developed a text summarization system aimed at enhancing productivity and reducing errors in the traditional data extraction process. METHODS: We developed a computer system that used machine learning and natural language processing approaches to automatically generate summaries of full-text scientific publications...
October 27, 2016: Journal of Biomedical Informatics
Jingjing Zheng, Zhuolin Jiang, Rama Chellappa
In real-world visual recognition problems, low-level features cannot adequately characterize the semantic content in images, or the spatio-temporal structure in videos. In this work, we encode objects or actions based on attributes that describe them as high-level concepts. We consider two types of attributes. One type of attributes is generated by humans, while the second type is data-driven attributes extracted from data using dictionary learning methods. Attribute-based representation may exhibit variations due to noisy and redundant attributes...
December 7, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
Seungryong Kim, Rui Cai, Kihong Park, Sunok Kim, Kwanghoon Sohn
We present a unified framework for image classification of image sets taken under varying modality conditions. Our method is motivated by a key observation that the image feature distribution is simultaneously influenced by the semantic-class and the modality category label, which limits the performance of conventional methods for that task. With this insight, we introduce modality uniqueness as a discriminative weight that divides each modality cluster from all other clusters. By leveraging the modality uniqueness, our framework is formulated as unsupervised modality clustering and classifier learning based on modality-invariant similarity kernel...
December 2, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Qiqin Dai, Seunghwan Yoo, Armin Kappeler, Aggelos K Katsaggelos
In this paper, we propose two multiple-frame superresolution (SR) algorithms based on dictionary learning and motion estimation. First, we adopt the use of video bilevel dictionary learning which has been used for single-frame SR. It is extended to multiple frames by using motion estimation with subpixel accuracy. We propose a batch and a temporally recursive multi-frame SR algorithm, which improve over single frame SR. Finally, we propose a novel dictionary learning algorithm utilizing consecutive video frames, rather than still images or individual video frames, which further improves the performance of the video SR algorithms...
November 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Shuiping Gou, Percy Lee, Peng Hu, Jean-Claude Rwigema, Ke Sheng
PURPOSE: With the advent of MR guided radiotherapy, internal organ motion can be imaged simultaneously during treatment. In this study, we evaluate the feasibility of pancreas MRI segmentation using state-of-the-art segmentation methods. METHODS AND MATERIAL: T2 weighted HASTE and T1 weighted VIBE images were acquired on 3 patients and 2 healthy volunteers for a total of 12 imaging volumes. A novel dictionary learning (DL) method was used to segment the pancreas and compared to t mean-shift merging (MSM), distance regularized level set (DRLS), graph cuts (GC) and the segmentation results were compared to manual contours using Dice's index (DI), Hausdorff distance and shift of the-center-of-the-organ (SHIFT)...
July 2016: Advances in Radiation Oncology
Nikolaos Passalis, Anastasios Tefas
In this paper, a manifold-based dictionary learning method for the bag-of-features (BoF) representation optimized toward information clustering is proposed. First, the spectral representation, which unwraps the manifolds of the data and provides better clustering solutions, is formed. Then, a new dictionary is learned in order to make the histogram space, i.e., the space where the BoF historgrams exist, as similar as possible to the spectral space. The ability of the proposed method to improve the clustering solutions is demonstrated using a wide range of datasets: two image datasets, the 15-scene dataset and the Corel image dataset, one video dataset, the KTH dataset, and one text dataset, the RT-2k dataset...
November 10, 2016: IEEE Transactions on Cybernetics
Ian B Penzel, Michelle R Persich, Ryan L Boyd, Michael D Robinson
BACKGROUND: When people think that their efforts will fail to achieve positive outcomes, they sometimes give up their efforts after control, which can have negative health consequences. PURPOSE: Problematic orientations of this type, such as pessimism, helplessness, or fatalism, seem likely to be associated with a cognitive mindset marked by higher levels of accessibility for failure words or concepts. Thus, the purpose of the present research was to determine whether there are individual differences in the frequency with which people think about failure, which in turn are likely to impact health across large spans of time...
November 11, 2016: Annals of Behavioral Medicine: a Publication of the Society of Behavioral Medicine
Pan Zhou, Chao Zhang, Zhouchen Lin
Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the `0 or `1 norm to encode each datum independently, limiting the performance of the learnt dictionaries...
October 31, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Dongyu Zhang, Liang Lin, Tianshui Chen, Xian Wu, Wenwei Tan, Ebroul Izquierdo
Sketch portrait generation benefits a wide range of applications such as digital entertainment and law enforcement. Although plenty of efforts have been dedicated to this task, several issues still remain unsolved for generating vivid and detail-preserving personal sketch portraits. For example, quite a few artifacts may exist in synthesizing hairpins and glasses, and textural details may be lost in the regions of hair or mustache. Moreover, the generalization ability of current systems is somewhat limited since they usually require elaborately collecting a dictionary of examples or carefully tuning features/components...
January 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Nikos Deligiannis, Joao F C Mota, Bruno Cornelis, Miguel R D Rodrigues, Ingrid Daubechies
In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front-and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component captures features shared by the multi-modal images, whereas the innovation component captures modality-specific information...
October 31, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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