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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/30235131/fast-adaptive-bilateral-filtering
#1
Ruturaj G Gavaskar, Kunal N Chaudhury
In the classical bilateral filter, a fixed Gaussian range kernel is used along with a spatial kernel for edge-preserving smoothing. We consider a generalization of this filter, the socalled adaptive bilateral filter, where the center and width of the Gaussian range kernel is allowed to change from pixel to pixel. Though this variant was originally proposed for sharpening and noise removal, it can also be used for other applications such as artifact removal and texture filtering. Similar to the bilateral filter, the brute-force implementation of its adaptive counterpart requires intense computations...
September 20, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30235128/kernel-distance-metric-learning-using-pairwise-constraints-for-person-re-identification
#2
Bac Nguyen, Bernard De Baets
Person re-identification is a fundamental task in many computer vision and image understanding systems. Due to appearance variations from different camera views, person reidentification still poses an important challenge. In the literature, KISSME has already been introduced as an effective distance metric learning method using pairwise constraints to improve the re-identification performance. Computationally, it only requires two inverse covariance matrix estimations. However, the linear transformation induced by KISSME is not powerful enough for more complex problems...
September 20, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30235130/dynamic-feature-matching-for-partial-face-recognition
#3
Lingxiao He, Haiqing Li, Qi Zhang, Zhenan Sun
Partial face recognition (PFR) in an unconstrained environment is a very important task, especially in situations where partial face images are likely to be captured due to occlusions, out-of-view, and large viewing angle, e.g., video surveillance and mobile devices. However, little attention has been paid to PFR so far and thus, the problem of recognizing an arbitrary patch of a face image remains largely unsolved. This study proposes a novel partial face recognition approach, called Dynamic Feature Matching (DFM), which combines Fully Convolutional Networks (FCNs) and Sparse Representation Classification (SRC) to address partial face recognition problem regardless of various face sizes...
September 18, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30235129/low-rank-transfer-human-motion-segmentation
#4
Lichen Wang, Zhengming Ding, Yun Fu
Human motion segmentation has great potential in real world applications. Conventional segmentation approaches cluster data with no guidance from prior knowledge, which could easily cause unpredictable segmentation output and decrease the performance. To this end, we seek to improve the humanmotion segmentation performance by fully utilizing pre-existing well-labeled source data. Specifically, we design a new transfer subspace clustering method for motion segmentation with a weighted rank constraint. Specifically, our proposed model obtains representations of both source and target sequences by mitigating their distribution divergence, which allows for more effective knowledge transfer to the target...
September 18, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30235127/graph-regularized-locality-constrained-joint-dictionary-and-residual-learning-for-face-sketch-synthesis
#5
Junjun Jiang, Yi Yu, Zheng Wang, Xianming Liu, Jiayi Ma
Face sketch synthesis is a crucial issue in digital entertainment and law enforcement. It can bridge the considerable texture discrepancy between face photos and sketches. Most of the current face sketch synthesis approaches directly learn the relationship between the photos and sketches, and it is very difficult for them to generate the individual specific features, which we call rare characteristics. In this work, we propose a novel face sketch synthesis approach through residual learning. In contrast to traditional approaches, which aim to reconstruct a sketch image directly (i...
September 18, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30235126/rectification-using-different-types-of-cameras-attached-to-a-vehicle
#6
Vinh Quang Dinh, Tien Phuoc Nguyen, Jae Wook Jeon
The rectification process is a compulsory step in stereo matching computation. To obtain depth information, stereo camera systems are often installed in vehicles for outdoor and street-related applications, including vehicle and pedestrian detection, lane detection, and traffic sign recognition. In this paper, we propose a rectification method that uses currently available front and rear view vehicle cameras to produce rectified stereo images. The proposed method can be employed with different types of cameras that have varying focal lengths...
September 18, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222572/a-convergent-image-fusion-algorithm-using-scene-adapted-gaussian-mixture-based-denoising
#7
Afonso M Teodoro, Jose M Bioucas-Dias, Mario A T Figueiredo
We propose a new approach to image fusion, inspired by the recent plug-and-play (PnP) framework. In PnP, a denoiser is treated as a black-box and plugged into an iterative algorithm, taking the place of the proximity operator of some convex regularizer, which is formally equivalent to a denoising operation. This approach offers flexibility and excellent performance, but convergence may be hard to analyze, as most state-of-the-art denoisers lack an explicit underlying objective function. Here, we propose using a scene-adapted denoiser (i...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222571/image-estimation-in-the-presence-of-irregular-sampling-noise-and-pointing-jitter
#8
Lorenzo Piazzo
We consider an acquisition system where a continuous image is reconstructed from a set of irregularly distributed, noisy samples. Moreover, the system is affected by a random pointing jitter which makes the actual sampling positions different from the nominal ones. We develop a model for the system and derive the optimal, Minimum Variance Unbiased (MVU) estimate. Unfortunately, the latter estimate is not practical to compute when the data size is large. Therefore, we develop a simplified, low resolution model and derive the corresponding MVU estimate, which has a drastically lower complexity...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222570/d3r-net-dynamic-routing-residue-recurrent-network-for-video-rain-removal
#9
Jiaying Liu, Wenhan Yang, Shuai Yang, Zongming Guo
In this paper, we address the problem of video rain removal by considering rain occlusion regions, i.e. very low light transmittance for rain streaks. Different from additive rain streaks, in such occlusion regions, the details of backgrounds are completely lost. Therefore, we propose a hybrid rain model to depict both rain streaks and occlusions. Integrating the hybrid model and useful motion segmentation context information, we present a Dynamic Routing Residue cRecurrent Network (D3RNet). D3R-Net first extracts the spatial features by a residual network...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222569/extracting-privileged-information-for-enhancing-classifier-learning
#10
Yazhou Yao, Fumin Shen, Jian Zhang, Li Liu, Zhenmin Tang, Ling Shao
The accuracy of data-driven learning approaches is often unsatisfactory when the training data is inadequate either in quantity or quality. Manually labeled privileged information (PI), e.g., attributes, tags or properties, is usually incorporated to improve classifier learning. However, the process of manually labeling is time-consuming and labor-intensive. Moreover, due to the limitations of personal knowledge, manually labeled PI may not be rich enough. To address these issues, we propose to enhance classifier learning by exploring PI from untagged corpora, which can effectively eliminate the dependency on manually labeled data and obtain much richer PI...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222568/interpreting-and-extending-the-guided-filter-via-cyclic-coordinate-descent
#11
Longquan Dai, Mengke Yuan, Liang Tang, Yuan Xie, Xiaopeng Zhang, Jinhui Tang
The Guided Filter (GF) is a widely used smoothing tool in computer vision and image processing. However, to the best of our knowledge, few papers investigate the mathematical connection between this filter and the least squares optimization. In this paper, we first interpret the guided filter as the cyclic coordinate descent solver of a least squares objective function. This discovery implies an extension approach to generalize the guided filter since we can change the least squares objective function and define new filters as the first pass iteration of the cyclic coordinate descent solver of modified objective functions...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222567/cross-scale-predictive-dictionaries
#12
Vishwanath Saragadam, Xin Li, Aswin C Sankaranarayanan
Sparse representations using learnt dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations. However, solving inverse problems with sparsifying dictionaries can be computationally expensive, especially when the dictionary under consideration has a large number of atoms. In this paper, we incorporate additional structure on to dictionary-based sparse representations for visual signals to enable speedups when solving sparse approximation problems...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222566/triple-verification-network-for-generalised-zero-shot-learning
#13
Haofeng Zhang, Yang Long, Yu Guan, Ling Shao
Conventional Zero-shot Learning approaches often suffer from severe performance degradation in the Generalised Zero-shot Learning (GZSL) scenario, i.e. to recognise test images that are from both seen and unseen classes. This paper studies the Class-level Over-fitting (CO) and empirically shows its effects to GZSL. We then address ZSL as a Triple Verification problem and propose a unified optimisation of regression and compatibility functions, i.e. two main streams of existing ZSL approaches. The complementary losses mutually regularise the same model to mitigate the CO problem...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222565/gated-gan-adversarial-gated-networks-for-multi-collection-style-transfer
#14
Xinyuan Chen, Chang Xu, Xiaokang Yang, Li Song, Dacheng Tao
Style transfer describes the rendering of an image's semantic content as different artistic styles. Recently, generative adversarial networks (GANs) have emerged as an effective approach in style transfer by adversarially training the generator to synthesize convincing counterfeits. However, traditional GAN suffers from the mode collapse issue, resulting in unstable training and making style transfer quality difficult to guarantee. In addition, the GAN generator is only compatible with one style, so a series of GANs must be trained to provide users with choices to transfer more than one kind of style...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222564/hyperspectral-imagery-classification-via-stochastic-hhsvms
#15
Weiwei Liu, Xiaobo Shen, Bo Du, Ivor W Tsang, Wenjie Zhang, Xuemin Lin
Hyperspectral imagery (HSI) has shown promising results in real-world applications. However, the technological evolution of optical sensors poses two main challenges in HSI classification: 1) the spectral band is usually redundant and noisy; and 2) HSI with millions of pixels has become increasingly common in real-world applications. Motivated by the recent success of hybrid huberized support vector machines (HHSVM), which inherit the benefits of both lasso and ridge regression, this paper first investigates the advantages of HHSVM for HSI applications...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222563/dual-transfer-face-sketch-photo-synthesis
#16
Mingjin Zhang, Ruxin Wang, Xinbo Gao, Jie Li, Dacheng Tao
Recognizing the identity of a sketched face from a face photograph dataset is a critical yet challenging task in many applications, not least law enforcement and criminal investigations. An intelligent sketched face identification system would rely on automatic face sketch synthesis from photos, thereby avoiding the cost of artists manually drawing sketches. However, conventional face sketch-photo synthesis methods tend to generate sketches that are consistent with the artists'drawing styles. Identity-specific information is often overlooked, leading to unsatisfactory identity verification and recognition performance...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222562/statistical-nearest-neighbors-for-image-denoising
#17
Iuri Frosio, Jan Kautz
Non-Local-Means image denoising is based on processing a set of neighbors for a given reference patch. Few Nearest Neighbors (NN) can be used to limit the computational burden of the algorithm. Resorting to a toy problem, we show analytically that sampling neighbors with the NN approach introduces a bias in the denoised patch. We propose a different neighbors' collection criterion to alleviate this issue, which we name Statistical NN (SNN). Our approach outperforms the traditional one in case of both white and colored noise: fewer SNNs can be used to generate images of superior quality, at a lower computational cost...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222561/large-scale-study-of-perceptual-video-quality
#18
Zeina Sinno, Alan Conrad Bovik
The great variations of videographic skills in videography, camera designs, compression and processing protocols, communication and bandwidth environments, and displays leads to an enormous variety of video impairments. Current noreference (NR) video quality models are unable to handle this diversity of distortions. This is true in part because available video quality assessment databases contain very limited content, fixed resolutions, were captured using a small number of camera devices by a few videographers and have been subjected to a modest number of distortions...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30222560/muon-tracing-and-image-reconstruction-algorithms-for-cosmic-ray-muon-computed-tomography
#19
Zhengzhi Liu, Stylianos Chatzidakis, John M Scaglione, Can Liao, Haori Yang, Jason P Hayward
Cosmic ray muon-computed tomography (μCT) is a new imaging modality with unique characteristics that could be particularly important for diverse applications including nuclear nonproliferation, spent nuclear fuel monitoring, cargo scanning, and volcano imaging. The strong scattering dependence of muons on atomic number Z in combination with high penetration range could offer a significant advantage over existing techniques when dense, shielded containers must be imaged. However, μCT reconstruction using conventional filtered back-projection is limited due to the overly simple assumptions that do not take into account the curved path caused by multiple Coulomb scattering prompting the need for more sophisticated approaches to be developed...
September 12, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/30188821/aipnet-image-to-image-single-image-dehazing-with-atmospheric-illumination-prior
#20
Anna Wang, Wenhui Wang, Jinglu Liu, Nanhui Gu
The atmospheric scattering and absorption gives rise to the natural phenomenon of haze, which severely affects the visibility of scenery. Thus, the image taken by the camera can easily lead to over brightness and ambiguity. To resolve an illposed and intractable problem of single image dehazing, we propose a straightforward but remarkable prior-atmospheric illumination prior in this paper. The extensive statistical experiments for different colorspaces and theoretical analyses indicate that the atmospheric illumination in hazy weather mainly has a great influence on the luminance channel in YCrCb colorspace, and has less impact on the chrominance channels...
September 4, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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