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

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https://read.qxmd.com/read/30762549/joint-multi-view-face-alignment-in-the-wild
#1
Jiankang Deng, George Trigeorgis, Yuxiang Zhou, Stefanos Zafeiriou
The de facto algorithm for facial landmark estimation involves running a face detector with a subsequent deformable model fitting on the bounding box. This encompasses two basic problems: i) the detection and deformable fitting steps are performed independently, while the detector might not provide best-suited initialization for the fitting step, ii) the face appearance varies hugely across different poses, which makes the deformable face fitting very challenging and thus distinct models have to be used (e...
February 13, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30762547/visual-quality-assessment-for-super-resolved-images-database-and-method
#2
Fei Zhou, Rongguo Yao, Bozhi Liu, Guoping Qiu
Image super-resolution (SR) has been an active re-search problem which has recently received renewed interest due to the introduction of new technologies such as deep learning. However, the lack of suitable criteria to evaluate the SR perfor-mance has hindered technology development. In this paper, we fill a gap in the literature by providing the first publicly available database as well as a new image quality assessment (IQA) method specifically designed for assessing the visual quality of su-per-resolved images (SRIs)...
February 12, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30762548/modelling-point-spread-function-in-fluorescence-microscopy-with-a-sparse-gaussian-mixture-trade-off-between-accuracy-and-efficiency
#3
Denis K Samuylov, Prateek Purwar, Gabor Szekely, Gregory Paul
Deblurring is a fundamental inverse problem in bioimaging. It requires modelling the point spread function (PSF), which captures the optical distortions entailed by the image formation process. The PSF limits the spatial resolution attainable for a given microscope. However, recent applications require a higher resolution, and have prompted the development of super-resolution techniques to achieve sub-pixel accuracy. This requirement restricts the class of suitable PSF models to analog ones. In addition, deblurring is computationally intensive, hence further requiring computationally efficient models...
February 11, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30762546/quadruplet-network-with-one-shot-learning-for-fast-visual-object-tracking
#4
Xingping Dong, Jianbing Shen, Dongming Wu, Kan Guo, Xiaogang Jin, Fatih Porikli
In the same vein of discriminative one-shot learning, Siamese networks allow recognizing an object from a single exemplar with the same class label. However, they do not take advantage of the underlying structure of the data and the relationship among the multitude of samples as they only rely on pairs of instances for training. In this paper, we propose a new quadruplet deep network to examine the potential connections among the training instances, aiming to achieve a more powerful representation. We design a shared network with four branches that receive multi-tuple of instances as inputs and are connected by a novel loss function consisting of pair-loss and triplet-loss...
February 11, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30735998/predicting-human-saccadic-scanpaths-based-on-iterative-representation-learning
#5
Chen Xia, Junwei Han, Fei Qi, Guangming Shi
Visual attention is a dynamic process of scene exploration and information acquisition. However, existing research on attention modeling has concentrated on estimating static salient locations. In contrast, dynamic attributes presented by saccade have not been well explored in previous attention models. In this paper, we address the problem of saccadic scanpath prediction by introducing an iterative representation learning framework. Within the framework, saccade can be interpreted as an iterative process of predicting one fixation according to the current representation and updating the representation based on the gaze shift...
February 7, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30735997/discrete-latent-factor-model-for-cross-modal-hashing
#6
Qing-Yuan Jiang, Wu-Jun Li
Due to its storage and retrieval efficiency, cross-modal hashing (CMH) has been widely used for cross-modal similarity search in many multimedia applications. According to the training strategy, existing CMH methods can be mainly divided into two categories: relaxation-based continuous methods and discrete methods. In general, the training of relaxation-based continuous methods is faster than discrete methods, but the accuracy of relaxation-based continuous methods is not satisfactory. On the contrary, the accuracy of discrete methods is typically better than relaxation-based continuous methods, but the training of discrete methods is very time-consuming...
February 6, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30735996/supplementary-material-for-an-iterative-spanning-forest-framework-for-superpixel-segmentation
#7
John E Vargas-Munoz, Ananda S Chowdhury, Eduardo B Alexandre, Felipe L Galvao, Paulo A Vechiatto Miranda, Alexandre X Falcao
No abstract text is available yet for this article.
February 6, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30735995/aesthetics-guided-graph-clustering-with-absent-modalities-imputation
#8
Luming Zhang, Yiyang Yao, Zhenguang Liu, Ling Shao
Accurately clustering Internet-scale Internet users into multiple communities according to their aesthetic styles is a useful technique in image modeling and data mining. In this work, we present a novel partially-supervised model which seeks a sparse representation to capture photo aesthetics1. It optimally fuzes multi-channel features, i.e., human gaze behavior, quality scores, and semantic tags, each of which could be absent. Afterward, by leveraging the KL-divergence to distinguish the aesthetic distributions between photo sets, a large-scale graph is constructed to describe the aesthetic correlations between users...
February 6, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30716037/content-aware-enhancement-of-images-with-filamentous-structures
#9
Haris Jeelani, Haoyi Liang, Scott T Acton, Daniel S Weller
In this article we describe a novel enhancement method for images containing filamentous structures. Our method combines a gradient sparsity constraint with a filamentous structure constraint for effective removal of clutter and noise from the background. The method is applied and evaluated on three types of data: confocal microscopy images of neurons, calcium imaging data and images of road pavement. We found that images enhanced by our method preserve both the structure and the intensity details of the original object...
February 4, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30716036/hyperspectral-image-unmixing-with-endmember-bundles-and-group-sparsity-inducing-mixed-norms
#10
Lucas Drumetz, Travis R Meyer, Jocelyn Chanussot, Andrea L Bertozzi, Christian Jutten
Hyperspectral images for remote sensing provide much more information than conventional imaging techniques, allowing a precise identification of the materials in the observed scene, but their limited spatial resolution makes that observations are usually mixtures of the contributions of several materials. The spectral unmixing problem aims at recovering the spectra of the pure materials of the scene (endmembers), along with their proportions (abundances) in each pixel. In order to deal with the intra-class variability of the materials, several spectra per material, constituting endmember bundles, can be considered to take into account spectral variability...
February 4, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30716035/vssa-net-vertical-spatial-sequence-attention-network-for-traffic-sign-detection
#11
Yuan Yuan, Zhitong Xiong, Qi Wang
Although traffic sign detection has been studied for years and great progress has been made with the rise of deep learning technique, there are still many problems remaining to be addressed. For complicated real-world traffic scenes, there are two main challenges. Firstly, traffic signs are usually smallsize objects, which makes it more difficult to detect than large ones; Secondly, it is hard to distinguish false targets which resemble real traffic signs in complex street scenes without context information...
February 1, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30714925/adaptive-admm-for-dictionary-learning-in-convolutional-sparse-representation
#12
Guan-Ju Peng
In this paper, we propose a novel approach to convolutional sparse representation with the aim of resolving the dictionary learning problem. The proposed method, referred to as the adaptive alternating direction method of multipliers (AADMM), employs constraints comprising non-convex nonsmooth terms, such as ℓ0 norm imposed on the coefficients and the unit-norm sphere imposed on the length of each dictionary element. The proposed scheme incorporates a novel parameter adaption scheme that enables ADMM to achieve convergence more quickly, as evidenced by numerical and theoretical analysis...
January 31, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30714924/spatio-temporal-feature-extraction-recognition-in-videos-based-on-energy-optimization
#13
Hidetomo Sakaino
Videos are spatio-temporally rich in static to dynamic objects/scenes, sparse to dense, and periodic to non-periodic motions. Particularly, dynamic texture (DT) exhibits complex appearance and motion changes that remain challenge to deal with. This paper presents an energy optimization method for feature extraction and recognition in videos. For noise and background jitter, Tikhonov regularization (TR) with eigen-vector and Frenet-Serret formula based energy constraints is also proposed. Different periodicity of DT can be adapted by the time-varying number of learning temporal frames...
January 31, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30714923/online-subspace-learning-from-gradient-orientations-for-robust-image-alignment
#14
Qingqing Zheng, Yi Wang, Pheng Ann Heng
Robust and efficient image alignment remains a challenging task, due to the massiveness of images, great illumination variations between images, partial occlusion and corruption. To address these challenges, we propose an online image alignment method via subspace learning from image gradient orientations (IGO). The proposed method integrates the subspace learning, transformed IGO reconstruction and image alignment into a unified online framework, which is robust for aligning images with severe intensity distortions...
January 31, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30714922/multi-modal-non-line-of-sight-passive-imaging
#15
Andre Beckus, Alexandru Tamasan, George K Atia
We consider the non-line-of-sight (NLOS) imaging of an object using light reflected off a diffusive wall. The wall scatters incident light such that a lens is no longer useful to form an image. Instead, we exploit the four-dimensional spatial coherence function to reconstruct a two-dimensional projection of the obscured object. The approach is completely passive in the sense that no control over the light illuminating the object is assumed, and is compatible with the partially coherent fields ubiquitous in both indoor and outdoor environments...
January 31, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30714921/automatic-land-cover-reconstruction-from-historical-aerial-images-an-evaluation-of-features-extraction-and-classification-algorithms
#16
Remi Ratajczak, Carlos Fernando Crispim, Elodie Faure, Beatrice Fervers, Laure Tougne
The land cover reconstruction from monochromatic historical aerial images is a challenging task that has recently known an increasing interest from the scientific community with the proliferation of large scale epidemiological studies involving retrospective analysis of spatial pattern. However, the efforts engaged by the computer vision community in remote sensing applications are mostly focused on prospective approaches through the analysis of high resolution multi-spectral data acquired by advanced spatial programs...
January 31, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30714920/content-aware-convolutional-neural-network-for-in-loop-filtering-in-high-efficiency-video-coding
#17
Chuanmin Jia, Shiqi Wang, Xinfeng Zhang, Shanshe Wang, Jiaying Liu, Shiliang Pu, Siwei Ma
Recently, convolutional neural network (CNN) has attracted tremendous attention and achieved great success in many image processing tasks. In this paper, we focus on CNN technology joining with image restoration to facilitate video coding performance, and propose the content-aware CNN based in-loop filtering for High Efficiency Video Coding (HEVC). In particular, we quantitatively analyze the structure of the proposed CNN model from multiple dimensions to make the model interpretable and optimal for CNN based loop filtering...
January 31, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30714919/predicting-detection-performance-on-security-x-ray-images-as-a-function-of-image-quality
#18
Praful Gupta, Zeina Sinno, Jack L Glover, Nicholas G Paulter, Alan C Bovik
Developing methods to predict how image quality affects task performance is a topic of great interest in many applications. While such studies have been performed in the medical imaging community, little work has been reported in the security X-ray imaging literature. In this work, we develop models that predict the effect of image quality on the detection of improvised explosive device (IED) components by bomb technicians in images taken using portable X-ray systems. Using a newly developed NIST-LIVE X-Ray Task Performance Database, we created a set of objective algorithms that predict bomb technician detection performance based on measures of image quality...
January 31, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30714918/generative-adversarial-networks-and-perceptual-losses-for-video-super-resolution
#19
Alice Lucas, Santiago Lopez-Tapiad, Rafael Molinae, Aggelos K Katsaggelos
Video super-resolution (VSR) has become one of the most critical problems in video processing. In the deep learning literature, recent works have shown the benefits of using adversarial-based and perceptual losses to improve the performance on various image restoration tasks; however, these have yet to be applied for video super-resolution. In this work, we propose a Generative Adversarial Network(GAN)-based formulation for VSR. We introduce a new generator network optimized for the VSR problem, named VSRResNet, along with a new discriminator architecture to properly guide VSRResNet during the GAN training...
January 29, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://read.qxmd.com/read/30714917/learning-sheared-epi-structure-for-light-field-reconstruction
#20
Gaochang Wu, Yebin Liu, Qionghai Dai, Tianyou Chai
Research in light field reconstruction focuses on synthesizing novel views with the assistance of depth information. In this paper, we present a learning-based light field reconstruction approach by fusing a set of sheared epipolar plane images (EPIs). We start by showing that a patch in a sheared EPI will exhibit a clear structure when the sheared value equals the depth of that patch. By taking advantage of this pattern, a convolutional neural network (CNN) is then trained to evaluate the sheared EPIs, and output a reference score for fusing the sheared EPIs...
January 29, 2019: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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