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  Subjects -> ELECTRONICS (Total: 179 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 7)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 5)
Advances in Electronics     Open Access   (Followers: 78)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
Advances in Power Electronics     Open Access   (Followers: 33)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 313)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 24)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 13)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 28)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 36)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 266)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 105)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 86)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 92)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 51)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 3)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 189)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 97)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 77)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 46)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 66)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 70)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 56)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 19)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 40)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 71)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 46)
IET Smart Grid     Open Access  
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 58)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 24)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 13)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
International Journal of Control     Hybrid Journal   (Followers: 12)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 2)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 14)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 8)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 12)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 10)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 24)
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access  
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 167)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 7)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal  
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 10)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 28)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 5)
Microelectronics and Solid State Electronics     Open Access   (Followers: 18)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 33)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal  
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 53)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 75)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Similar Journals
Journal Cover
IEEE Transactions on Circuits and Systems for Video Technology
Journal Prestige (SJR): 0.977
Citation Impact (citeScore): 5
Number of Followers: 19  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1051-8215
Published by IEEE Homepage  [191 journals]
  • IEEE Transactions on Circuits and Systems for Video Technology publication
           information
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • IEEE Transactions on Circuits and Systems for Video Technology publication
           information
    • Abstract: Provides a listing of current committee members and society officers.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • A Low-Cost Portable Polycamera for Stereoscopic 360° Imaging
    • Authors: Hong-Shiang Lin;Chao-Chin Chang;Hsu-Yu Chang;Yung-Yu Chuang;Tzong-Li Lin;Ming Ouhyoung;
      Pages: 915 - 929
      Abstract: This paper proposes a low-cost and portable polycamera system and accompanying methods for capturing and synthesizing stereoscopic 360° panoramas. The polycamera consists of only four cameras with fisheye lenses. Synthesizing panoramas from only four views is challenging because the cameras view very differently and the captured images have significant distortions and color degradation including vignetting, contrast loss, and blurriness. For coping with these challenges, this paper proposes methods for rectifying the polyview images, estimating depth of the scene, and synthesizing stereoscopic panoramas. The proposed camera is compact in size, light in weight, and inexpensive. The proposed methods allow the synthesis of visually pleasing stereoscopic 360° panoramas using the images captured with the proposed polycamera. We have built a prototype of the polycamera and tested it on a set of scenes with different characteristics of depth ranges and depth variations. The experiments show that the proposed camera and methods are effective in generating stereoscopic 360° panoramas that can be viewed on popular virtual reality displays.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Subspace Clustering Under Complex Noise
    • Authors: Baohua Li;Huchuan Lu;Ying Zhang;Zhouchen Lin;Wei Wu;
      Pages: 930 - 940
      Abstract: In this paper, we study the subspace clustering problem under complex noise. A wide class of reconstruction-based methods model the subspace clustering problem by combining a quadratic data-fidelity term and a regularization term. In a statistical framework, the data-fidelity term assumes to be contaminated by a unimodal Gaussian noise, which is a popular setting in most current subspace clustering models. However, the realistic noise is much more complex than our assumptions. Besides, the coarse representation of the data-fidelity term may depress the clustering accuracy, which is often used to evaluate the models. To address this issue, we propose the mixture of Gaussian regression (MoG Regression) for subspace clustering. The MoG Regression seeks a valid way to model the unknown noise distribution, which approaches the real one as far as possible, so that the desired affinity matrix is better at characterizing the structure of data in the real world, and furthermore, improving the performance. Theoretically, the proposed model enjoys the grouping effect, which encourages the coefficients of highly correlated points are nearly equal. Drawing upon the ideal of the minimum message length, a model selection strategy is proposed to estimate the numbers of the Gaussian components that shows a way how to seek the number of Gaussian components besides determining it by empirical value. In addition, the asymptotic property of our model is investigated. The proposed model is evaluated on the challenging datasets. The experimental results show that the proposed MoG Regression model significantly outperforms several state-of-the-art subspace clustering methods.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Horizontal and Vertical Nuclear Norm-Based 2DLDA for Image Representation
    • Authors: Yuwu Lu;Chun Yuan;Zhihui Lai;Xuelong Li;David Zhang;Wai Keung Wong;
      Pages: 941 - 955
      Abstract: 2-D linear discriminant analysis (2DLDA) has been widely used in pattern recognition and image classification. 2DLDA selects discriminative features from the up and left corner of images. However, 2DLDA uses the Frobenius norm (F-norm), which is sensitive to noise or outliers in data, as a metric. In this paper, we propose a novel framework, called horizontal and vertical nuclear norm-based 2DLDA (HVNN-2DLDA) for image representation. In the proposed framework, HVNN-2DLDA methods (i.e., HNN-2DLDA and VNN-2DLDA) are proposed, and both use the nuclear norm as a criterion. The nuclear norm can provide more structure and global information for the reconstruction of noisy images. HNN-2DLDA and VNN-2DLDA represent images in the row and column directions, respectively. In addition, by combining the row and column directions, we propose a bilateral nuclear norm-based 2DLDA method called BNN-2DLDA. The advantage of BNN-2DLDA over HNN-2DLDA and VNN-2DLDA is that an image sample can be represented by both the row and the column directions instead of only the row or column direction. HVNN-2DLDA learns a set of local optimal projection vectors by maximizing the ratio of the nuclear norm of the between-class scatter matrix and the nuclear norm of the within-class scatter matrix. To verify the robustness and recognition performance in image classification of HVNN-2DLDA, six public image databases are used for experiments. The experimental results demonstrate the effectiveness and the feasibility of the proposed framework.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Binocular Fusion Net: Deep Learning Visual Comfort Assessment for
           Stereoscopic 3D
    • Authors: Hak Gu Kim;Hyunwook Jeong;Heoun-taek Lim;Yong Man Ro;
      Pages: 956 - 967
      Abstract: In this paper, we propose a novel deep learning-based visual comfort assessment (VCA) for stereoscopic images. To assess the overall degree of visual discomfort in stereoscopic viewing, we devise a binocular fusion deep network (BFN) learning binocular characteristics between stereoscopic images. The proposed BFN learns the latent binocular feature representations for the visual comfort score prediction. In the BFN, the binocular feature is encoded by fusing the spatial features extracted from left and right views. Finally, the visual comfort score is predicted by projecting the binocular feature onto the subjective score space. In addition, we devise a disparity regularization network (DRN) for improving the prediction results. The proposed DRN takes the binocular feature from the BFN and estimates disparity maps from the feature in order to embed disparity relations between left and right views into the deep network. The proposed deep network with BFN and DRN is end-to-end trained in a unified framework in which the DRN acts as disparity regularization. We evaluated the prediction performance of the proposed deep network for VCA by the comparison of existing objective VCA metrics. Further, we demonstrated that the proposed BFN showed various factors causing visual discomfort by using network visualization.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • LECARM: Low-Light Image Enhancement Using the Camera Response Model
    • Authors: Yurui Ren;Zhenqiang Ying;Thomas H. Li;Ge Li;
      Pages: 968 - 981
      Abstract: Low-light image enhancement algorithms can improve the visual quality of low-light images and support the extraction of valuable information for some computer vision techniques. However, existing techniques inevitably introduce color and lightness distortions when enhancing the images. To lower the distortions, we propose a novel enhancement framework using the response characteristics of cameras. First, we discuss how to determine a reasonable camera response model and its parameters. Then, we use the illumination estimation techniques to estimate the exposure ratio for each pixel. Finally, the selected camera response model is used to adjust each pixel to the desired exposure according to the estimated exposure ratio map. Experiments show that our method can obtain enhancement results with fewer color and lightness distortions compared with the several state-of-the-art methods.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • A Robust Moving Object Detection in Multi-Scenario Big Data for Video
           Surveillance
    • Authors: Bo-Hao Chen;Ling-Feng Shi;Xiao Ke;
      Pages: 982 - 995
      Abstract: Advanced wireless imaging sensors and cloud data storage contribute to video surveillance by enabling the generation of large amounts of video footage every second. Consequently, surveillance videos have become one of the largest sources of unstructured data. Because multi-scenario surveillance videos are often continuously produced, using these videos to detect moving objects is challenging for the conventional moving object detection methods. This paper presents a novel model that harnesses both sparsity and low-rankness with contextual regularization to detect moving objects in multi-scenario surveillance data. In the proposed model, we consider moving objects as a contiguous outlier detection problem through the use of low-rank constraint with contextual regularization, and we construct dedicated backgrounds for multiple scenarios using dictionary learning-based sparse representation, which ensures that our model can be effectively applied to multi-scenario videos. Quantitative and qualitative assessments indicate that the proposed model outperforms existing methods and achieves substantially more robust performance than the other state-of-the-art methods.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Comparative Evaluations of Selected Tracking-by-Detection Approaches
    • Authors: Alhayat Ali Mekonnen;Frédéric Lerasle;
      Pages: 996 - 1010
      Abstract: In this paper, we present a comparative evaluation of various multi-person tracking-by-detection approaches on public data sets. This paper investigates five popular trackers coupled with six relevant visual people detectors evaluated on seven public data sets. The evaluation emphasizes on exhibited performance variation depending on tracker-detector choices. Our experimental results show that the overall performance depends on how challenging the data set is, the performance of the detector on the specific data set, and the tracker-detector combination. Some trackers are more sensitive to the choice of a detector and some detectors to the choice of a tracker than others. Based on our results, two of the trackers demonstrate the best performances consistently across different data sets, whereas the best performing detectors vary per data set. This underscores the need for careful application context specific evaluation when choosing a detector.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Deep Continuous Conditional Random Fields With Asymmetric Inter-Object
           Constraints for Online Multi-Object Tracking
    • Authors: Hui Zhou;Wanli Ouyang;Jian Cheng;Xiaogang Wang;Hongsheng Li;
      Pages: 1011 - 1022
      Abstract: Online multi-object tracking (MOT) is a challenging problem and has many important applications including intelligence surveillance, robot navigation, and autonomous driving. In existing MOT methods, individual object’s movements and inter-object relations are mostly modeled separately and relations between them are still manually tuned. In addition, inter-object relations are mostly modeled in a symmetric way, which we argue is not an optimal setting. To tackle those difficulties, in this paper, we propose a deep continuous conditional random field (DCCRF) for solving the online MOT problem in a track-by-detection framework. The DCCRF consists of unary and pairwise terms. The unary terms estimate tracked objects’ displacements across time based on visual appearance information. They are modeled as deep convolution neural networks, which are able to learn discriminative visual features for tracklet association. The asymmetric pairwise terms model inter-object relations in an asymmetric way, which encourages high-confidence tracklets to help correct errors of low-confidence tracklets and not to be affected by low-confidence ones much. The DCCRF is trained in an end-to-end manner for better adapting the influences of visual information as well as inter-object relations. Extensive experimental comparisons with state-of-the-arts as well as detailed component analysis of our proposed DCCRF on two public benchmarks demonstrate the effectiveness of our proposed MOT framework.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Salient Object Detection via Two-Stage Graphs
    • Authors: Yi Liu;Jungong Han;Qiang Zhang;Long Wang;
      Pages: 1023 - 1037
      Abstract: Despite recent advances made in salient object detection using graph theory, the approach still suffers from accuracy problems when the image is characterized by a complex structure, either in the foreground or background, causing erroneous saliency segmentation. This fundamental challenge is mainly attributed to the fact that most existing graph-based methods take only the adjacently spatial consistency among graph nodes into consideration. In this paper, we tackle this issue from a coarse-to-fine perspective and propose a two-stage-graphs approach for salient object detection, in which two graphs having the same nodes but different edges are employed. Specifically, a weighted joint robust sparse representation model, rather than the commonly used manifold ranking model, helps to compute the saliency value of each node in the first-stage graph, thereby providing a saliency map at the coarse level. In the second-stage graph, along with the adjacently spatial consistency, a new regionally spatial consistency among graph nodes is considered in order to refine the coarse saliency map, assuring uniform saliency assignment even in complex scenes. Particularly, the second stage is generic enough to be integrated in existing salient object detectors, enabling improved performance. Experimental results on benchmark data sets validate the effectiveness and superiority of the proposed scheme over related state-of-the-art methods.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Long-Short-Term Features for Dynamic Scene Classification
    • Authors: Yuanjun Huang;Xianbin Cao;Qi Wang;Baochang Zhang;Xiantong Zhen;Xuelong Li;
      Pages: 1038 - 1047
      Abstract: Dynamic scene classification has been extensively studied in computer vision due to its widespread applications. The key to dynamic scene classification lies in jointly characterizing spatial appearance and temporal dynamics to achieve informative representation, which remains an outstanding task in the literature. In this paper, we propose a unified framework to extract spatial and temporal features for dynamic scene representation. More specifically, we deploy two variants of deep convolutional neural networks to encode spatial appearance and short-term dynamics into short-term deep features (STDF). Based on STDF, we propose using the autoregressive moving average model to extract long-term frequency features (LTFF). By combining STDF and LTFF, we establish the long–short-term feature (LSTF) representations of dynamic scenes. The LSTF characterizes both spatial and temporal patterns of dynamic scenes for comprehensive and information representation that enables more accurate classification. Extensive experiments on three-dynamic scene classification benchmarks have shown that the proposed LSTF achieves high performance and substantially surpasses the state-of-the-art methods.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Recognizing Distractions for Assistive Driving by Tracking Body Parts
    • Authors: Tashrif Billah;S. M. Mahbubur Rahman;M. Omair Ahmad;M. N. S. Swamy;
      Pages: 1048 - 1062
      Abstract: Busy life as well as the prevalence of infotainment is increasingly making people more occupied even during tasks that require serious attention. One such task is driving and at the same time getting involved in activities that may distract drivers cognitively from watching the road and cause fatal accidents. This paper presents a method that is capable of monitoring different types of distractions, such as talking and texting on cell phone, casual eating, and operating cabin equipment while driving, so that a driver can be assisted to remain cautious on the road. The proposed method automatically detects and tracks fiducial body parts of a driver from video captured by a camera mounted on the front windshield inside a vehicle. Relative distances between the tracking trajectories are used as features that represent actions of the driver. Then, the well-known kernel support vector machine is applied for recognizing a particular distraction from the features extracted from body parts. The proposed feature is also compared with previously employed features for tracking-based human action recognition schemes to substantiate its better result in terms of mean accuracy and robustness for distraction recognition. The effectiveness of the proposed method of distraction recognition is also analyzed with respect to tracking errors.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • A Super Descriptor Tensor Decomposition for Dynamic Scene Recognition
    • Authors: Muhammad Rizwan Khokher;Abdesselam Bouzerdoum;Son Lam Phung;
      Pages: 1063 - 1076
      Abstract: This paper presents a new approach for dynamic scene recognition based on a super descriptor tensor decomposition. Recently, local feature extraction based on dense trajectories has been used for modeling motion. However, dense trajectories usually include a large number of unnecessary trajectories, which increase noise, add complexity, and limit the recognition accuracy. Another problem is that the traditional bag-of-words techniques encode and concatenate the local features extracted from multiple descriptors to form a single large vector for classification. This concatenation not only destroys the spatio-temporal structure among the features but also yields high dimensionality. To address these problems, first, we propose to refine the dense trajectories by selecting only salient trajectories in a region of interest containing motion. Visual descriptors consisting of oriented gradient and motion boundary histograms are then computed along the refined dense trajectories. In case of camera motion, a short-window video stabilization is integrated to compensate for global motion. Second, the extracted features from multiple descriptors are encoded using a super descriptor tensor model. To this end, the TUCKER-3 tensor decomposition is employed to obtain a compact set of salient features, followed by feature selection via Fisher ranking. Experiments are conducted using two benchmark dynamic scene recognition datasets: Maryland “in-the-wild” and YUPPEN dynamic scenes. Experimental results show that the proposed approach outperforms several existing methods in terms of recognition accuracy and achieves a performance comparable with the state-of-the-art deep learning methods. The proposed approach achieves classification rates of 89.2% for Maryland and 98.1% for YUPPEN datasets.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Discriminative Spatio-Temporal Pattern Discovery for 3D Action Recognition
    • Authors: Junwu Weng;Chaoqun Weng;Junsong Yuan;Zicheng Liu;
      Pages: 1077 - 1089
      Abstract: Despite the recent success of 3D action recognition using depth sensor, most existing works target how to improve the action recognition performance, rather than understanding how different types of actions are performed. In this paper, we propose to discover discriminative spatio-temporal patterns for 3D action recognition. Discovering these patterns can not only help to improve the action recognition performance but also help us to understand and differentiate between the action category. Our proposed method takes the spatio-temporal structure of 3D action into consideration and can discover essential spatio-temporal patterns that play key roles in action recognition. Instead of relying on an end-to-end network to learn the 3D action representation and perform classification, we simply present each 3D action as a series of temporal stages composed by 3D poses. Then, we rely on nearest neighbor matching and bilinear classifiers to simultaneously identify both critical temporal stages and spatial joints for each action class. Despite using raw action representation and a linear classifier, experiments on five benchmark data sets show that the proposed spatio-temporal naïve Bayes mutual information maximization can achieve a competitive performance compared with the state-of-the-art methods that use sophisticated end-to-end learning, and has the advantage of finding discriminative spatio-temporal action patterns.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • A Novel Weight-Shared Multi-Stage CNN for Scale Robustness
    • Authors: Ryo Takahashi;Takashi Matsubara;Kuniaki Uehara;
      Pages: 1090 - 1101
      Abstract: Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification for benchmark tasks and practical applications. The CNNs with deeper architectures have achieved even higher performance recently thanks to their robustness to the parallel shift of objects in images and their numerous parameters and the resulting high expression ability. However, CNNs have a limited robustness to other geometric transformations such as scaling and rotation. This limits the performance improvement of the deep CNNs, but there is no established solution. This paper focuses on scale transformation and proposes a network architecture called the weight-shared multi-stage network (WSMS-Net), which consists of multiple stages of CNNs. The proposed WSMS-Net is easily combined with existing deep CNNs such as residual network and densely connected convolutional network and enables them to acquire robustness to object scaling. Experimental results on the CIFAR-10, CIFAR-100, and ImageNet datasets demonstrate that existing deep CNNs combined with the proposed WSMS-Net achieve higher accuracies for image classification tasks with only a minor increase in the number of parameters and computation time.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Multi-Focus Image Fusion With a Natural Enhancement via a Joint
           Multi-Level Deeply Supervised Convolutional Neural Network
    • Authors: Wenda Zhao;Dong Wang;Huchuan Lu;
      Pages: 1102 - 1115
      Abstract: Common non-focused areas are often present in multi-focus images due to the limitation of the number of focused images. This factor severely degrades the fusion quality of multi-focus images. To address this problem, we propose a novel end-to-end multi-focus image fusion with a natural enhancement method based on deep convolutional neural network (CNN). Several end-to-end CNN architectures that are specifically adapted to this task are first designed and researched. On the basis of the observation that low-level feature extraction can capture low-frequency content, whereas high-level feature extraction effectively captures high-frequency details, we further combine multi-level outputs such that the most visually distinctive features can be extracted, fused, and enhanced. In addition, the multi-level outputs are simultaneously supervised during training to boost the performance of image fusion and enhancement. Extensive experiments show that the proposed method can deliver superior fusion and enhancement performance than the state-of-the-art methods in the presence of multi-focus images with common non-focused areas, anisotropic blur, and misregistration.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Dictionary Learning-Based, Directional, and Optimized Prediction for
           Lenslet Image Coding
    • Authors: Rui Zhong;Ionut Schiopu;Bruno Cornelis;Shao-Ping Lu;Junsong Yuan;Adrian Munteanu;
      Pages: 1116 - 1129
      Abstract: In this paper, a novel approach to encode lenslet (LL) images is proposed. The method departs from traditional block-based coding structures and employs a hexagonal-shaped pixel cluster, called macro-pixel, as an elementary coding unit. A novel prediction mode based on dictionary learning is proposed, whereby macro-pixels are represented by a sparse linear combination of atoms from a generic dictionary. Additionally, an optimized linear prediction mode and a directional prediction mode specifically designed for macro-pixels are proposed. Rate-distortion optimization is utilized to select the best intra prediction mode for each macro-pixel. Experimental results on the light field image data set show that the proposed coding system outperforms HEVC and the state-of-the-art in LL image coding with an average peak signal to noise ratio gain of 3.33 and 1.41 dB, respectively, and with rate savings of 67.13% and 34.30%, respectively.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Adaptive Streaming in Interactive Multiview Video Systems
    • Authors: Xue Zhang;Laura Toni;Pascal Frossard;Yao Zhao;Chunyu Lin;
      Pages: 1130 - 1144
      Abstract: Multiview applications endow final users with the possibility to freely navigate within 3D scenes with minimum-delay. A real feeling of scene navigation is enabled by transmitting multiple high-quality camera views, which can be used to synthesize additional virtual views to offer a smooth navigation. However, when network resources are limited, not all camera views can be sent at high quality. It is therefore important, yet challenging, to find the right tradeoff between coding artifacts (reducing the quality of camera views) and virtual synthesis artifacts (reducing the number of camera views sent to users). To this aim, we propose an optimal transmission strategy for interactive multiview HTTP adaptive streaming. We propose a problem formulation to select the optimal set of camera views that the client requests for downloading, such that the navigation quality experienced by the user is optimized while the bandwidth constraints are satisfied. We show that our optimization problem is NP-hard, and we therefore develop an optimal solution based on the dynamic programming algorithm with polynomial time complexity. To further simplify the deployment, we present a suboptimal greedy algorithm with effective performance and lower complexity. The proposed controller is evaluated in theoretical and realistic settings characterized by realistic network statistics estimation, buffer management, and server-side representation optimization. Simulation results show significant improvement in terms of navigation quality compared with alternative baseline multiview adaptation logic solutions.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Multi-Script-Oriented Text Detection and Recognition in Video/Scene/Born
           Digital Images
    • Authors: K. S. Raghunandan;Palaiahnakote Shivakumara;Sangheeta Roy;G. Hemantha Kumar;Umapada Pal;Tong Lu;
      Pages: 1145 - 1162
      Abstract: Achieving good text detection and recognition results for multi-script-oriented images is a challenging task. First, we explore bit plane slicing in order to utilize the advantage of the most significant bit information to identify text components. A new iterative nearest neighbor symmetry is then proposed based on shapes of convex and concave deficiencies of text components in bit planes to identify candidate planes. Further, we introduce a new concept called mutual nearest neighbor pair components based on gradient direction to identify representative pairs of texts in each candidate bit plane. The representative pairs are used to restore words with the help of edge image of the input one, which results in text detection results (words). Second, we propose a new idea by fixing window for character components of arbitrary oriented words based on angular relationship between sub-bands and a fused band. For each window, we extract features in contourlet wavelet domain to detect characters with the help of an SVM classifier. Further, we propose to explore HMM for recognizing characters and words of any orientation using the same feature vector. The proposed method is evaluated on standard databases such as ICDAR, YVT video, ICDAR, SVT, MSRA scene data, ICDAR born digital data, and multi-lingual data to show its superiority to the state of the art methods.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Highly Paralleled Low-Cost Embedded HEVC Video Encoder on TI KeyStone
           Multicore DSP
    • Authors: Hongxu Jiang;Rui Fan;Yongfei Zhang;Gang Wang;Zhe Li;
      Pages: 1163 - 1178
      Abstract: Although HEVC, the emerging video coding standard, has doubled the coding performance of its predecessor H.264/AVC, its significantly increased computational complexity imposes great obstacles for HEVC encoders to be employed in real-time applications with embedded processors, such as digital signal processors (DSPs). In this paper, a TI Keystone multicore TMS320C6678 DSP-based highly paralleled low-cost fast HEVC encoding solution is well designed and implemented. First, the overall structure of HEVC encoder with CTU-level parallelism is re-designed to well support the encoding parallelism, with full consideration of the hardware characteristics. Second, a low-delay and low-memory multicore data transmission mechanism is proposed to reduce the latency of data access between internal L2 memory and external DDR3. Third, the encoding bottlenecks, i.e., the most time-consuming encoding modules, are identified and optimized for acceleration with TI powerful C6000 SIMD instructions. Experimental results show that our proposed HEVC encoder on TI TMS320C6678 DSPs can significantly improve the real-time capacity with tolerable performance loss, 0.93 dB performance loss under on average 465.50 times speedup as compared to CPU-based HM reference software, more specifically, which makes it desirable in power-constrained real-time video applications.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • A Real-Time High-Quality Complete System for Depth Image-Based Rendering
           on FPGA
    • Authors: Yanzhe Li;Luc Claesen;Kai Huang;Menglian Zhao;
      Pages: 1179 - 1193
      Abstract: Depth image-based rendering (DIBR) techniques have recently drawn more attention in various 3D applications. In this paper, a real-time high-quality DIBR system that consists of disparity estimation and view synthesis is proposed. For disparity estimation, a local approach that focuses on depth discontinuities and disparity smoothness is presented to improve the disparity accuracy. For view synthesis, a method that contains view interpolation and extrapolation is proposed to render high-quality virtual views. Moreover, the system is designed with an optimized parallelism scheme to achieve a high throughput, and can be scaled up easily. It is implemented on an Altera Stratix IV FPGA at a processing speed of 45 frames per second for 1080p resolution. Evaluated on selected image sets of the Middlebury benchmark, the average error rate of the disparity maps is 6.02%; the average peak signal to noise ratio and structural similarity values of the virtual views are 30.07 dB and 0.9303, respectively. The experimental results indicate that the proposed DIBR system has the top-performing processing speed and its accuracy performance is among the best of state-of-the-art hardware implementations.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Parking Space Status Inference Upon a Deep CNN and Multi-Task Contrastive
           Network With Spatial Transform
    • Authors: Hoang Tran Vu;Ching-Chun Huang;
      Pages: 1194 - 1208
      Abstract: Deep learning methods, especially CNNs, have achieved many promising results in a wide range of computer vision applications. However, few studies focused on designing suitable deep learning methods for parking space status inference. As we have known, it is challenging to detect parking spaces in an outdoor environment due to dynamic lighting variations, weather changes, and perspective distortion. By off-the-shelf CNNs, lighting variations might be handled well. However, to realize a practical and robust inference system, we also need to address troublesome problems, such as parking displacements, non-unified car sizes, inter-object occlusion, and perspective distortion. These problems may become even challenging if also considering the difference of space sizes. To overcome the problems, we proposed a custom–tailored deep convolutional and contrastive network with three contributions. First, we introduced a Siamese architecture to learn the contrastive and robust feature descriptor. This helps to reduce the effects owing to the variety of inter-object occlusion. Second, we integrated a convolutional Spatial Transformer Network (STN) to adaptively transform a 3-space input patch according to vehicle sizes and parking displacement. STN also helps to overcome the perspective distortion problem. Third, a multi-task loss function was designed to train the network by simultaneously considering the accuracy of inferring the status of the target space and the semantic smoothness of high-level features. Thereby, the errors caused by inter-object occlusion could be alleviated. To verify the proposed network, we visualized the learned features and analyzed their functionality. Experiments and evaluations have shown the robustness of our system in parking status inference. The real-time system currently running in public parking lots also demonstrates the effectiveness of the proposed deep network.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Adaptive Polar Active Contour for Segmentation and Tracking in Ultrasound
           Videos
    • Authors: Ebrahim Karami;Mohamed S. Shehata;Andrew Smith;
      Pages: 1209 - 1222
      Abstract: Detection of relative changes in circulating blood volume is important to guide resuscitation and manage a variety of medical conditions, including sepsis, trauma, dialysis, and congestive heart failure. Recent studies have shown that estimates of circulating blood volume can be obtained from the cross-sectional area of the internal jugular vein (IJV) from ultrasound images. However, accurate segmentation and tracking of the IJV in ultrasound imaging is a challenging task and is significantly influenced by a number of parameters, such as the image quality, shape, and temporal variation. In this paper, we propose a novel adaptive polar active contour (Ad-PAC) algorithm for the segmentation and tracking of the IJV in ultrasound videos. In the proposed algorithm, the parameters of the Ad-PAC algorithm are adapted based on the results of segmentation in previous frames. The Ad-PAC algorithm is applied to 65 ultrasound videos captured from 13 healthy subjects, with each video containing 450 frames. The results show that spatial and temporal adaptation of the energy function significantly improves segmentation performance when compared with the current state-of-the-art active contour algorithms.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • A Novel Patch Variance Biased Convolutional Neural Network for
           No-Reference Image Quality Assessment
    • Authors: Lai-Man Po;Mengyang Liu;Wilson Y. F. Yuen;Yuming Li;Xuyuan Xu;Chang Zhou;Peter H. W. Wong;Kin Wai Lau;Hon-Tung Luk;
      Pages: 1223 - 1229
      Abstract: Deep convolutional neural networks (CNNs) have been successfully applied on no-reference image quality assessment (NR-IQA) with respect to human perception. Most of these methods deal with small image patches and use the average score of the test patches for predicting the whole image quality. We discovered that image patches from homogenous regions are unreliable for both neural network training and final image quality score estimation. In addition, image patches with complex structures have much higher chances of achieving better image quality prediction. Based on these findings, we enhanced the conventional CNN-based NR-IQA algorithm to avoid homogenous patches for the network training and quality score estimation. Moreover, we also use a variance-based weighting average to bias the final image quality score to the patches with complex structure. The experimental results show that this simple approach can achieve state-of-the-art performance compared with well-known NR-IQA algorithms.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • An Efficient Rate-Distortion Optimization Method for Low-Delay
           Configuration in H.265/HEVC Based on Temporal Layer Rate and Distortion
           Dependence
    • Authors: Kaifang Yang;Yanchao Gong;Miao Ma;Hong Ren Wu;
      Pages: 1230 - 1236
      Abstract: Currently, as one of the most important video coding structures, the low-delay (LD) configuration has been widely adopted in the video communication systems, such as video surveillance and video conferencing systems. The rate-distortion optimization (RDO) is also a crucial technique for improving the performance of video coding. The RDO performance of H.265/HEVC with the LD configuration is significantly affected by the temporal dependence. This paper examines the dependence of rate and distortion among temporal layers in H.265/HEVC with the LD configuration. An efficient RDO method is then proposed based on temporal dependence, where the optimized Lagrangian multipliers are estimated by adaptively selecting the scaling factors according to the video motion characteristics pertaining to different temporal layers. The experimental results show that compared with the RDO method adopted in the H.265/HEVC test model (i.e., HM), the proposed RDO method can achieve the average BD-rate reductions of 6.4% and 5.8% for the LD P and LD B configurations, respectively.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
  • Special Issue on Large-scale Visual Sensor Networks: Architectures and
           Applications
    • Pages: 1237 - 1238
      Abstract: Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
      PubDate: April 2019
      Issue No: Vol. 29, No. 4 (2019)
       
 
 
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