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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/28715330/anti-impulse-noise-edge-detection-via-anisotropic-morphological-directional-derivatives
#1
Peng-Lang Shui, Fu-Ping Wang
Traditional differential-based edge detection suffers from abrupt degradation in performance when images are corrupted by impulse noises. The morphological operators such as the median filters and weighted median filters possess the intrinsic ability to counteract impulse noise. In this paper, by combining the biwindow configuration with weighted median filters, anisotropic morphological directional derivatives (AMDD) robust to impulse noise are proposed to measure the local grayscale variation around a pixel...
July 13, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708560/fast-and-orthogonal-locality-preserving-projections-for-dimensionality-reduction
#2
Rong Wang, Feiping Nie, Richang Hong, Xiaojun Chang, Xiaojun Yang, Weizhong Yu
The locality preserving projections algorithm (LPP) is a recently developed linear dimensionality reduction (DR) algorithm that has been frequently used in face recognition and other applications. However, the projection matrix in LPP is not orthogonal, thus creating difficulties for both reconstruction and other applications. As the orthogonality property is desirable, orthogonal LPP (OLPP) has been proposed so that an orthogonal projection matrix can be obtained based on a step by step procedure; however, this makes the algorithm computationally more expensive...
July 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708559/texture-characterization-using-shape-co-occurrence-patterns
#3
Gui-Song Xia, Gang Liu, Xiang Bai, Liangpei Zhang
Texture characterization is a key problem in image understanding and pattern recognition. In this paper, we present a flexible shape-based texture representation using shape co-occurrence patterns. More precisely, texture images are first represented by a tree of shapes, each of which is associated with several geometrical and radiometric attributes. Then, four typical kinds of shape co-occurrence patterns based on the hierarchical relationships among the shapes in the tree are learned as codewords. Three different coding methods are investigated for learning the codewords, which can be used to encode any given texture image into a descriptive vector...
July 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708558/local-directional-ternary-pattern-for-facial-expression-recognition
#4
Byungyong Ryu, Adin Ramirez Rivera, Jaemyun Kim, Oksam Chae
This paper presents a new face descriptor, local directional ternary pattern (LDTP), for facial expression recognition. LDTP efficiently encodes information of emotion-related features (i.e., eyes, eyebrows, upper nose, and mouth) by using the directional information and ternary pattern in order to take advantage of the robustness of edge patterns in the edge region while overcoming weaknesses of edge-based methods in smooth regions. Our proposal, unlike existing histogram-based face description methods that divide the face into several regions and sample the codes uniformly, uses a two level grid to construct the face descriptor while sampling expression-related information at different scales...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708557/blind-deep-s3d-image-quality-evaluation-via-local-to-global-feature-aggregation
#5
Heeseok Oh, Sewoong Ahn, Jongyoo Kim, Sanghoon Lee
Previously, no-reference (NR) stereoscopic 3D (S3D) image quality assessment (IQA) algorithms have been limited to the extraction of reliable hand-crafted features based on an understanding of the insufficiently revealed human visual system or natural scene statistics. Furthermore, compared with full-reference (FR) S3D IQA metrics, it is difficult to achieve competitive quality score predictions using the extracted features, which are not optimized with respect to human opinion. To cope with this limitation of the conventional approach, we introduce a novel deep learning scheme for NR S3D IQA in terms of local to global feature aggregation...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708556/deep-learning-on-sparse-manifolds-for-faster-object-segmentation
#6
Jacinto C Nascimento, Gustavo Carneiro
We propose a new combination of deep belief networks and sparse manifold learning strategies for the 2D segmentation of non-rigid visual objects. With this novel combination, we aim to reduce the training and inference complexities while maintaining the accuracy of machine learning based non-rigid segmentation methodologies. Typical non-rigid object segmentation methodologies divide the problem into a rigid detection followed by a non-rigid segmentation, where the low dimensionality of the rigid detection allows for a robust training (i...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708555/going-deeper-with-contextual-cnn-for-hyperspectral-image-classification
#7
Hyungtae Lee, Heesung Kwon
In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28708554/distance-metric-learning-via-iterated-support-vector-machines
#8
Wangmeng Zuo, Faqiang Wang, David Zhang, Liang Lin, Yuchi Huang, Deyu Meng, Lei Zhang
Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs)...
July 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28692976/near-infrared-coloring-via-a-contrast-preserving-mapping-model
#9
Chang-Hwan Son, Xiao-Ping Zhang
Near-infrared gray images captured along with corresponding visible color images have recently proven useful for image restoration and classification. This paper introduces a new coloring method to add colors to near-infrared gray images based on a contrast-preserving mapping model. A naive coloring method directly adds the colors from the visible color image to the near-infrared gray image. However, this method results in an unrealistic image because of the discrepancies in the brightness and image structure between the captured near-infrared gray image and the visible color image...
July 7, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28692975/a-novel-method-of-minimizing-view-synthesis-distortion-based-on-its-non-monotonicity-in-3d-video
#10
Meng Yang, Nanning Zheng, Ce Zhu, Fei Wang
In depth-based 3D video, the view synthesis distortion (VSD) is generally measured by modeling the effect of texture and depth errors separately. With such a development, it has been referred that the VSD changes monotonically w.r.t. to both the texture and depth distortions. In this paper, we find the VSD not always change monotonically with them by both theoretical analysis and experimental test, when the effect of the texture and depth errors are considered together. Specifically, 1) we prove that the VSD is non-monotonic with the texture distortion...
July 5, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28692974/multi-image-blind-super-resolution-of-3d-scenes
#11
Abhijith Punnappurath, T M Nimisha, A N Rajagopalan
We address the problem of estimating the latent high-resolution (HR) image of a 3D scene from a set of non-uniformly motion blurred low-resolution (LR) images captured in the burst mode using a hand-held camera. Existing blind super-resolution (SR) techniques that account for motion blur are restricted to fronto-parallel planar scenes. We initially develop an SR motion blur model to explain the image formation process in 3D scenes. We then use this model to solve for the three unknowns - the camera trajectories, the depth map of the scene and the latent HR image...
July 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28692973/greedy-batch-based-minimum-cost-flows-for-tracking-multiple-objects
#12
Xinchao Wang, Bin Fan, Shiyu Chang, Zhangyang Wang, Xianming Liu, Dacheng Tao, Thomas S Huang
Minimum-cost flow algorithms have recently achieved state-of-the-art results in multi-object tracking. However, they rely on the whole image sequence as input. When deployed in real-time applications or in distributed settings, these algorithms first operate on short batches of frames and then stitch the results into full trajectories. This decoupled strategy is prone to errors because the batch-based tracking errors may propagate to the final trajectories and can not be corrected by other batches. In this paper, we propose a greedy batch-based minimum-cost flow approach for tracking multiple objects...
July 4, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28682257/higher-order-energies-for-image-segmentation
#13
Jianbing Shen, Jianteng Peng, Xingping Dong, Ling Shao, Fatih Porikli
A novel energy minimization method for general higher-order binary energy functions is proposed in this paper. We first relax a discrete higher-order function to a continuous one, and use the Taylor expansion to obtain an approximate lower-order function, which is optimized by the quadratic pseudo-boolean optimization (QPBO) or other discrete optimizers. The minimum solution of this lower-order function is then used as a new local point, where we expand the original higher-order energy function again. Our algorithm does not restrict to any specific form of the higher-order binary function or bring in extra auxiliary variables...
July 3, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28682256/volumetric-image-registration-from-invariant-keypoints
#14
Blaine Rister, Mark A Horowitz, Daniel L Rubin
We present a method for image registration based on 3D scale- and rotation-invariant keypoints. The method extends the Scale Invariant Feature Transform (SIFT) to arbitrary dimensions by making key modifications to orientation assignment and gradient histograms. Rotation invariance is proven mathematically. Additional modifications are made to extrema detection and keypoint matching based on the demands of image registration. Our experiments suggest that the choice of neighborhood in discrete extrema detection has a strong impact on image registration accuracy...
July 3, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28682255/visual-attention-saccadic-models-learn-to-emulate-gaze-patterns-from-childhood-to-adulthood
#15
Olivier Le Meur, Antoine Coutrot, Zhi Liu, Pia Rama, Adrien Le Roch, Andrea Helo
How people look at visual information reveals fundamental information about themselves, their interests and their state of mind. While previous visual attention models output static 2-dimensional saliency maps, saccadic models aim to predict not only where observers look at but also how they move their eyes to explore the scene. In this paper, we demonstrate that saccadic models are a flexible framework that can be tailored to emulate observer's viewing tendencies. More specifically, we use fixation data from 101 observers split into 5 age groups (adults, 8-10 y...
June 30, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28682254/multiscale-shannon-s-entropy-modelling-of-orientation-and-distance-in-steel-fiber-micro-tomography-data
#16
John P Chiverton, Olubisi Ige, Stephanie J Barnett, Tony Parry
This work is concerned with the modelling and analysis of the orientation and distance between steel fibers in Xray Micro-Tomography (XCT) data. The advantage of combining both orientation and separation in a model is that it helps provide a detailed understanding of how the steel fibers are arranged, which is easy to compare. The developed models are designed to summarise the randomness of the orientation distribution of the steel fibers both locally and across an entire volume based on multiscale entropy...
June 30, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28682253/bilinear-optimized-product-quantization-for-scalable-visual-content-analysis
#17
Litao Yu, Zi Huang, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xiaofang Zhou
Product quantization (PQ) has been recognized as a useful technique to encode visual feature vectors into compact codes to reduce both the storage and computation cost. Recent advances in retrieval and vision tasks indicate that high-dimensional descriptors are critical to ensuring high accuracy on large-scale datasets. However, optimizing PQ codes with high-dimensional data is extremely time and memory consuming. To solve this problem, in this paper, we present a novel PQ method based on bilinear projection, which can well exploit the natural data structure and reduce the computational complexity...
June 30, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28682252/low-rank-and-joint-sparse-representations-for-multi-modal-recognition
#18
Heng Zhang, Vishal M Patel, Rama Chellappa
We propose multi-task and multivariate methods for multi-modal recognition based on low-rank and joint sparse representations. Our formulations can be viewed as generalized versions of multivariate low-rank and sparse regression, where sparse and low-rank representations across all modalities are imposed. One of our methods simultaneously couples information within different modalities by enforcing the common low-rank and joint sparse constraints among multi-modal observations. We also modify our formulations by including an occlusion term that is assumed to be sparse...
June 29, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28682251/qoe-guided-warping-for-stereoscopic-image-retargeting
#19
Feng Shao, Wenchong Lin, Weisi Lin, Qiuping Jiang, Gangyi Jiang
In the field of stereoscopic three-dimensional (S3D) display, it is an interesting as well as meaningful issue to retarget the stereoscopic images to the target resolution, while the existing stereoscopic image retargeting methods do not fully take user's Quality of Experience (QoE) in account. In this paper, we have presented a QoE-guided warping method for stereoscopic image retargeting, which retarget the stereoscopic image and adapt its depth range to the target display while promoting user's quality of experience (QoE)...
June 29, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28678708/gaussian-process-domain-experts-for-modeling-of-facial-affect
#20
Stefanos Eleftheriadis, Ognjen Oggi Rudovic, Marc Peter Deisenroth, Maja Pantic
Most of existing models for facial behavior analysis rely on generic classifiers, which fail to generalize well to previously unseen data. This is because of inherent differences in source (training) and target (test) data, mainly caused by variation in subjects' facial morphology, camera views, etc. All of these account for different contexts in which target and source data are recorded, and thus, may adversely affect the performance of the models learned solely from source data. In this paper, we exploit the notion of domain adaptation and propose a data efficient approach to adapt already learned classifiers to new unseen contexts...
June 28, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
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