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IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society

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https://www.readbyqxmd.com/read/27925590/modality-invariant-image-classification-based-on-modality-uniqueness-and-dictionary-learning
#1
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
https://www.readbyqxmd.com/read/27925589/manifold-regularized-experimental-design-for-active-learning
#2
Lining Zhang, Hubert P H Shum, Ling Shao
Various machine learning and data mining tasks in classification require abundant data samples to be labeled for training. Conventional active learning methods aim at labeling the most informative samples for alleviating the labor of the user. Many previous studies in active learning select one sample after another in a greedy manner. However, this is not very effective because the classification models has to be retrained for each newly labeled sample. Moreover, many popular active learning approaches utilize the most uncertain samples by leveraging the classification hyperplane of the classifier, which is not appropriate since the classification hyperplane is inaccurate when the training data are small-sized...
December 2, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27913351/adaptive-multispectral-demosaicking-based-on-frequency-domain-analysis-of-spectral-correlation
#3
Sunil Jaiswal, Lu Fang, Vinit Jakhetiya, Jiahao Pang, Klaus Mueller, Oscar C Au
Color filter array (CFA) interpolation, or 3-band demosaicking, is a process of interpolating the missing color samples in each band to reconstruct a full color image. In this paper, we are concerned with the challenging problem of multispectral demosaicking, where each band is significantly undersampled due to the increment in the number of bands. Specifically, we demonstrate a frequency-domain analysis of the subsampled color-difference signal and observe that the conventional assumption of highly correlated spectral bands for estimating undersampled components is not precise...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27913350/guided-wavelet-shrinkage-for-edge-aware-smoothing
#4
Guang Deng
Edge-aware smoothing has been extensively studied due to its wide range of applications in computer vision and graphics.Most published works have been focused on formulating the smoothing problem in the spatial domain. In this paper, we propose a new edge-aware smoothing framework called guided wavelet shrinkage (GWS) which is formulated in the wavelet domain as a maximum a posterior estimation problem. We impose a number of desirable properties on the statistical models and the associated parameters in order to derive an effective and computationally efficient algorithm...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27913349/adaptive-cascade-regression-model-for-robust-face-alignment
#5
Qingshan Liu, Jiankang Deng, Jing Yang, Guangcan Liu, Dacheng Tao
Cascade regression is a popular face alignment approach, and it has achieved good performances on the wild databases. However, it depends heavily on local features in estimating reliable landmark locations and therefore suffers from corrupted images, such as images with occlusion, which often exists in real-world face images. In this paper, we present a new adaptive cascade regression model for robust face alignment. In each iteration, the shape-indexed appearance is introduced to estimate the occlusion level of each landmark, and each landmark is then weighted according to its estimated occlusion level...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27913348/automatic-refinement-strategies-for-manual-initialization-of-object-trackers
#6
Hao Zhu, Fatih Porikli
Tracking objects across multiple frames is a wellinvestigated problem in computer vision. The majority of the existing algorithms assume an accurate initialization is readily available. However, in many real-life settings, in particular for applications where the video is streaming in real-time, the initialization has to be provided by a human operator. This limitation raises an inevitable uncertainty issue. Here, we first collect a large and new dataset of inputs that consists of more than 20K human initialization clicks, called as HIC, by several subjects under three practical user interface scenarios for the popular TB50 tracking benchmark...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27913347/tree-structure-sparsity-pattern-guided-convex-optimization-for-compressive-sensing-of-large-scale-images
#7
Wei-Jie Liang, Gang-Xuan Lin, Chun-Shien Lu
Cost-efficient compressive sensing of large-scale images with quickly reconstructed high-quality results is very challenging. In this paper, we present an algorithm to solve convex optimization via the tree structure sparsity pattern, which can be run in the operator to reduce computation cost and maintain good quality, especially for large-scale images. We also provide convergence analysis and convergence rate analysis for the proposed method. The feasibility of our method is verified through simulations and comparison with state-of-theart algorithms...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27913346/automatic-design-of-high-sensitivity-color-filter-arrays-with-panchromatic-pixels
#8
Jia Li, Chenyan Bai, Zhouchen Lin, Jian Yu
In most of existing digital cameras, color images have to be reconstructed from raw images which only have one color sensed at each pixel, as their imaging sensors are covered by color filter arrays (CFAs). At each pixel a CFA usually allows only a portion of the light spectrum to pass through and thereby reduces the light sensitivity of pixels. To address this issue, previous works have explored adding panchromatic pixels into CFAs. However, almost all existing methods assign panchromatic pixels empirically, making the designed CFAs prone to aliasing artifacts...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27913345/facial-age-estimation-with-age-difference
#9
Zhenzhen Hu, Yonggang Wen, Jianfeng Wang, Meng Wang, Richang Hong, Shuicheng Yan
Age estimation based on the human face remains a significant problem in computer vision and pattern recognition. In order to estimate an accurate age or age group of a facial image, most of the existing algorithms require a huge face data set attached with age labels. This imposes a constraint on the utilization of the immensely unlabeled or weakly labeled training data, e.g. the huge amount of human photos in the social networks. These images may provide no age label, but it is easily to derive the age difference for an image pair of the same person...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27913344/perceptual-image-fusion-using-wavelets
#10
Paul Hill, Mohammed Ebrahim Al-Mualla, David Bull
A perceptual image fusion method is proposed that employs explicit luminance and contrast masking models. These models are combined to give the perceptual importance of each coefficient produced by the Dual-Tree Complex Wavelet Transform of each input image. This combined model of perceptual importance is used to select which coefficients are retained and furthermore to determine how to present the retained information in the most effective way. This work is the first to give a principled approach to image fusion from a perceptual perspective...
December 1, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27893395/fast-physically-correct-refocusing-for-sparse-light-fields-using-block-based-multi-rate-view-interpolation
#11
Chao-Tsung Huang, Yu-Wen Wang, Li-Ren Huang, Jui Chin, Liang-Gee Chen
Digital refocusing has a tradeoff between complexity and quality when using sparsely sampled light fields for lowstorage applications. In this paper, we propose a fast physicallycorrect refocusing algorithm to address this issue in a two-fold way. First, view interpolation is adopted to provide photorealistic quality at infocus-defocus hybrid boundaries. Regarding its conventional high complexity, we devised a fast line-scan method specifically for refocusing, and its 1D kernel can be 30x faster than the benchmark VSRS-1D-Fast...
November 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27893394/a-robust-and-efficient-approach-to-license-plate-detection
#12
Yule Yuan, Wenbin Zou, Yong Zhao, Xinan Wang, Xuefeng Hu, Nikos Komodakis
This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates from complex scenes in real time. A simple yet effective image downscaling method is first proposed to substantially accelerate license plate localization without sacrificing detection performance compared with that achieved using the original image. Furthermore, a novel line density filter approach is proposed to extract candidate regions, thereby significantly reducing the area to be analyzed for license plate localization...
November 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27893393/salient-object-detection-with-spatiotemporal-background-priors-for-video
#13
Tao Xi, Wei Zhao, Han Wang, Weisi Lin
Saliency detection for images has been studied for many years, for which a lot of methods have been designed. In saliency detection, background priors which are often regarded as pseudo-background are effective clues to find salient objects in images. Although image boundary is commonly used background priors, it doesn't work well for images of complex scenes and videos. In this paper, we explore how to identify the background priors for a video and propose a saliency based method to detect the visual objects by using background priors...
November 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27893392/waterloo-exploration-database-new-challenges-for-image-quality-assessment-models
#14
Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, Lei Zhang
The great content diversity of real-world digital images poses a grand challenge to image quality assessment (IQA) models, which are traditionally designed and validated on a handful of commonly used IQA databases with very limited content variation. To test the generalization capability and to facilitate the wide usage of IQA techniques in realworld applications, we establish a large-scale database named the Waterloo Exploration Database, which in its current state contains 4; 744 pristine natural images and 94; 880 distorted images created from them...
November 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27893391/robust-transfer-metric-learning-for-image-classification
#15
Zhengming Ding, Yun Fu
Metric learning has attracted increasing attention due to its critical role in image analysis and classification. Conventional metric learning always assumes the training and test data are sampled from the same or similar distribution. However, to build an effective distance metric, we need abundant supervised knowledge (i.e., side/label information), which is generally inaccessible in practice because of the expensive labeling cost. In this paper, we develop a Robust Transfer Metric Learning (RTML) framework to effectively assist the unlabeled target learning by transferring the knowledge from the well-labeled source domain...
November 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27893390/unsupervised-multi-class-co-segmentation-via-joint-cut-over-l1-manifold-hyper-graph-of-discriminative-image-regions
#16
Jizhou Ma, Shuai Li, Hong Qin, Aimin Hao
This paper systematically advocates a robust and efficient unsupervised multi-class co-segmentation approach by leveraging underlying subspace manifold propagation to exploit the cross-image coherency. It can combat certain image cosegmentation difficulties due to viewpoint change, partial occlusion, complex background, transient illumination, and cluttering texture patterns. Our key idea is to construct a powerful hyper-graph joint-cut framework, which incorporates mid-level image regions based intra-image feature representation and L1- manifold graph based inter-image coherency exploration...
November 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27893389/joint-defogging-and-demosaicking
#17
Yeejin Lee, Keigo Hirakawa, Truong Nguyen
Image defogging is a technique used extensively for enhancing visual quality of images in bad weather conditions. Even though defogging algorithms have been well studied, defogging performance is degraded by demosaicking artifacts and sensor noise amplification in distant scenes. In order to improve visual quality of restored images, we propose a novel approach to perform defogging and demosaicking simultaneously. We conclude that better defogging performance with fewer artifacts can be achieved when a defogging algorithm is combined with a demosaicking algorithm simultaneously...
November 22, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27893388/sparse-representation-based-multiple-frame-video-super-resolution
#18
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
https://www.readbyqxmd.com/read/27875225/the-shape-interaction-matrix-based-affine-invariant-mismatch-removal-for-partial-duplicate-image-search
#19
Yang Lin, Zhouchen Lin, Hongbin Zha
Mismatch removal is a key step in many computer vision problems. In this paper, we handle the mismatch removal problem by adopting shape interaction matrix (SIM). Given the homogeneous coordinates of the two corresponding point sets, we first compute the SIMs of the two point sets. Then we detect the mismatches by picking out the most different entries between the two SIMs. Even under strong affine transformations, outliers, noises, and burstiness, our method can still work well. Actually, this work is the first non-iterative mismatch removal method that achieves affine invariance...
November 16, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/27875224/parallel-proximal-algorithm-for-anisotropic-total-variation-minimization
#20
Ulugbek Kamilov
Total variation (TV) is a one of the most popular regularizers for stabilizing the solution of ill-posed inverse problems. This paper proposes a novel proximal-gradient algorithm for minimizing TV regularized least-squares cost functionals. Unlike traditional methods that require nested iterations for computing the proximal step of TV, our algorithm approximates the latter with several simple proximals that have closed form solutions. We theoretically prove that the proposed parallel proximal method achieves the TV solution with arbitrarily high precision at a global rate of converge that is equivalent to the fast proximalgradient methods...
November 16, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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