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  • Fuzzy local intensity clustering (FLIC) model for automatic medical image segmentation
    Vis. Comput. (IF 1.415) Pub Date : 2020-06-03
    Asieh Khosravanian, Mohammad Rahmanimanesh, Parviz Keshavarzi, Saeed Mozaffari

    Intensity inhomogeneity is one of the main challenges in automatic medical image segmentation. In this paper, fuzzy local intensity clustering (FLIC), which is based on the combination of level set algorithm and fuzzy clustering, is proposed to mitigate the effect of intensity variation and noise contamination. For the FLIC method, the segmentation and bias modification are carried out in a fully automatic

  • Compressing animated meshes with fine details using local spectral analysis and deformation transfer
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-28
    Chengju Chen, Qing Xia, Shuai Li, Hong Qin, Aimin Hao

    Geometry-centric shape animation, usually represented as dynamic meshes with fixed connectivity and time-deforming geometry, is becoming ubiquitous in digital entertainment and other relevant graphics applications. However, digital animation with fine details, which requires more diversity of texture on meshed geometry, always consumes a significant amount of storage space, and compactly storing and

  • A comprehensive survey on automatic facial action unit analysis
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-26
    Ruicong Zhi, Mengyi Liu, Dezheng Zhang

    Facial Action Coding System is the most influential sign judgment method for facial behavior, and it is a comprehensive and anatomical system which could encode various facial movements by the combination of basic AUs (Action Units). AUs define certain facial configurations caused by contraction of one or more facial muscles, and they are independent of interpretation of emotions. However, automatic

  • A review of monocular visual odometry
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-25
    Ming He, Chaozheng Zhu, Qian Huang, Baosen Ren, Jintao Liu

    Monocular visual odometry provides more robust functions on navigation and obstacle avoidance for mobile robots than other visual odometries, such as binocular visual odometry, RGB-D visual odometry and basic odometry. This paper describes the problem of visual odometry and also determines the relationships between visual odometry and visual simultaneous localization and mapping (SLAM). The basic principle

  • Glioma extraction from MR images employing Gradient Based Kernel Selection Graph Cut technique
    Vis. Comput. (IF 1.415) Pub Date : 2019-05-14
    Jyotsna Dogra, Shruti Jain, Meenakshi Sood

    Medical imaging is one of the most daunting, challenging, and emerging research topics in image processing. Segmenting the glioma from the brain magnetic resonance images (MRI) is an important and demanding task, as it assists the medical experts for the disease diagnosis process. Recent research methods in image segmentation have highlighted the prospective of graph-based techniques for medical applications

  • Air-writing recognition system for Persian numbers with a novel classifier
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-19
    Shahram Mohammadi, Reza Maleki

    Air-writing through hand or fingertip is a functional and attractive mechanism. Since there is usually no pen-up and pen-down in air-writing, trajectory of numbers and words in the air-writing will be a connected series of characters (ligature Stroke). Identification of legitimate characters such as digits or letters inside a ligature Stroke is one of the most important challenges faced in this area

  • ISPH–PBD: coupled simulation of incompressible fluids and deformable bodies
    Vis. Comput. (IF 1.415) Pub Date : 2019-05-18
    Nadine Abu Rumman, Prapanch Nair, Patric Müller, Loïc Barthe, David Vanderhaeghe

    We present an efficient and stable method for simulating the two-way coupling of incompressible fluids and deformable bodies. In our method, the fluid is represented by particles, and simulated using divergence-free incompressible smoothed-particle hydrodynamics (ISPH). The deformable bodies are represented by polygonal meshes, where the elastic deformations are simulated using a position-based dynamics

  • 4D facial expression recognition using multimodal time series analysis of geometric landmark-based deformations
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-14
    Payam Zarbakhsh, Hasan Demirel

    One of the main challenges in dynamic facial expression recognition is how to capture temporal information in the system. In this study, a novel approach based on time series analysis is adapted for this problem. The proposed dynamic facial expression recognition system comprises four phases: head pose correction and normalization, feature extraction, feature selection and classification. Head pose

  • Survey of cube mapping methods in interactive computer graphics
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-12
    M. Lambers

    The standard cube mapping technique implemented in graphics pipelines, while useful in many scenarios, has significant shortcomings for important application areas in interactive computer graphics, e.g., dynamic environment mapping, omnidirectional shadow maps, or planetary-scale terrain rendering. Many alternative mapping methods have been proposed over the years with the purpose of reducing area

  • A new multi-secret image sharing scheme based on DCT
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-12
    Oinam Bidyapati Chanu, Arambam Neelima

    Multi-secret image sharing scheme (MSIS) is a technique to share multiple secret images over the internet. Normally, most of the secret image sharing schemes can share only a single secret image. However, due to the rapid development of internet technology, the necessity of sharing multiple images arises. An (n, n) MSIS is employed to share n images to n authorized participants. All the n participants

  • CityCraft: 3D virtual city creation from a single image
    Vis. Comput. (IF 1.415) Pub Date : 2019-05-20
    Suzi Kim, Dodam Kim, Sunghee Choi

    This paper introduces a method to generate a three-dimensional (3D) virtual model of an imaginary city from a single street-view image to represent the appearance of the city in a given input photograph. The proposed approach differs from reconstruction approaches, which generate a city model by guessing the city name from the input photograph. In contrast, we use machine learning to identify where

  • Interactive influences of color attributes on color perception bias
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-12
    Huan Yang, Yi-Na Li, Kang Zhang

    Graphic user interfaces and information visualization use color to represent qualitative or quantitative information. The interaction between adjacent colors leads to perceptual bias, known as simultaneous color contrast, and implicitly distort the understanding of visualized information presentation. To investigate the effect of simultaneous color contrast, we conduct two empirical experiments, in

  • Segmentation of crowd flow by trajectory clustering in active contours
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-18
    Sonu Lamba, Neeta Nain

    Crowd analysis has become an important topic of research for visual surveillance community. This paper proposes an active contour-based trajectory clustering approach for crowd flow segmentation. To this end, the active contour method is applied to segment the foreground crowd region with an aim to optimize further tracking. From the segmented foreground region, spatiotemporal interest points are detected

  • Using pseudo voxel octree to accelerate collision between cutting tool and deformable objects modeled as linked voxels
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-20
    Shiyu Jia, Weizhong Zhang, Zhenkuan Pan, Guodong Wang, Xiaokang Yu

    For deformable objects modeled as a uniform grid of voxels connected by links, an octree for the voxels is constructed. Cutting is performed by disconnecting links swept by the cutting tool and reconstructing cut surface mesh using the dual contour method. The cubes of the voxel octree are not directly used because their edges generally do not remain straight when the objects deform. Instead, the voxel

  • Noise-tolerant texture feature extraction through directional thresholded local binary pattern
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-18
    Sayed Mohamad Tabatabaei, Abdolah Chalechale

    Local binary pattern (LBP) is a multi-applicable texture descriptor applied in machine vision. Despite its outstanding abilities in revealing textural properties of image, it is sensitive to noise, due to its thresholding mechanism. To make LBP robust against noise, a directional thresholded LBP (DTLBP) is developed in this article which applies the directional neighboring pixels average values for

  • Optical effects on HDR calibration via a multiple exposure noise-based workflow
    Vis. Comput. (IF 1.415) Pub Date : 2020-04-18
    Brian A. Karr, Kurt Debattista, Alan G. Chalmers

    High dynamic range (HDR) technology allows more of the lighting in a specific scene to be captured at a set point in time, and thus is capable of delivering an overall view of the scene that more closely correlates with our visual experience in the real world, compared to standard, or low dynamic range (LDR) technology. Although HDR capabilities of single exposure capture systems are improving, the

  • Efficient object tracking using hierarchical convolutional features model and correlation filters
    Vis. Comput. (IF 1.415) Pub Date : 2020-04-18
    Mohammed Y. Abbass, Ki-Chul Kwon, Nam Kim, Safey A. Abdelwahab, Fathi E. Abd El-Samie, Ashraf A. M. Khalaf

    Visual object tracking is a very important task in computer vision. This paper develops a method based on the convolutional neural network (CNN) and correlation filters for visual object tracking. To implement a superior tracking method, we develop a multiple correlation tracker. This paper presents an effective method to track an object based on a combination of feature hierarchies of CNNs. We combine

  • A self-supervised method of single-image depth estimation by feeding forward information using max-pooling layers
    Vis. Comput. (IF 1.415) Pub Date : 2020-04-09
    Jinlong Shi, Yunhan Sun, Suqin Bai, Zhengxing Sun, Zhaohui Tian

    We propose an encoder–decoder CNN framework to predict depth from one single image in a self-supervised manner. To this aim, we design three kinds of encoder based on the recent advanced deep neural network and one kind of decoder which can generate multiscale predictions. Eight loss functions are designed based on the proposed encoder–decoder CNN framework to validate the performance. For training

  • Character motion in function space
    Vis. Comput. (IF 1.415) Pub Date : 2020-04-04
    Innfarn Yoo, Marek Fišer, Kaimo Hu, Bedrich Benes

    We address the problem of animated character motion representation and approximation by introducing a novel form of motion expression in a function space. For a given set of motions, our method extracts a set of orthonormal basis (ONB) functions. Each motion is then expressed as a vector in the ONB space or approximated by a subset of the ONB functions. Inspired by the static PCA, our approach works

  • Structure revealing of low-light images using wavelet transform based on fractional-order denoising and multiscale decomposition
    Vis. Comput. (IF 1.415) Pub Date : 2020-04-03
    Ziaur Rahman, Yi-Fei Pu, Muhammad Aamir, Samad Wali

    Images captured in low-light environment often lower its quality due to low illumination and high noise. Hence, the low visibility of images notably degrades the overall performance of multimedia and vision systems that are typically designed for high-quality inputs. To resolve this problem, numerous algorithms have been proposed in extant literature to improve the visual quality of low-light images

  • Object Tracking Based On Huber Loss Function.
    Vis. Comput. Pub Date : 2019-11-20
    Yong Wang,Shiqiang Hu,Shandong Wu

    In this paper we present a novel visual tracking algorithm, in which object tracking is achieved by using subspace learning and Huber loss regularization in a particle filter framework. The changing appearance of tracked target is modeled by Principle Component Analysis (PCA) basis vectors and row group sparsity. This method takes advantage of the strengths of sub-space representation and explicitly

  • Analysis of reported error in Monte Carlo rendered images.
    Vis. Comput. Pub Date : 2017-01-01
    Joss Whittle,Mark W Jones,Rafał Mantiuk

    Evaluating image quality in Monte Carlo rendered images is an important aspect of the rendering process as we often need to determine the relative quality between images computed using different algorithms and with varying amounts of computation. The use of a gold-standard, reference image, or ground truth is a common method to provide a baseline with which to compare experimental results. We show

  • Dynamic 3-D computer graphics for designing a diagnostic tool for patients with schizophrenia.
    Vis. Comput. Pub Date : 2016-12-19
    Attila Farkas,Thomas V Papathomas,Steven M Silverstein,Hristiyan Kourtev,John F Papayanopoulos

    We introduce a novel procedure that uses dynamic 3-D computer graphics as a diagnostic tool for assessing disease severity in schizophrenia patients, based on their reduced influence of top-down cognitive processes in interpreting bottom-up sensory input. Our procedure uses the hollow-mask illusion, in which the concave side of the mask is misperceived as convex, because familiarity with convex faces

  • Visual analytics and rendering for tunnel crack analysis: A methodological approach for integrating geometric and attribute data.
    Vis. Comput. Pub Date : 2016-01-01
    Thomas Ortner,Johannes Sorger,Harald Piringer,Gerd Hesina,Eduard Gröller

    The visual analysis of surface cracks plays an essential role in tunnel maintenance when assessing the condition of a tunnel. To identify patterns of cracks, which endanger the structural integrity of its concrete surface, analysts need an integrated solution for visual analysis of geometric and multivariate data to decide if issuing a repair project is necessary. The primary contribution of this work

  • An Evaluation of 3-D Scene Exploration Using a Multiperspective Image Framework.
    Vis. Comput. Pub Date : 2012-06-05
    Paul Rosen,Voicu Popescu

    Multiperspective images (MPIs) show more than what is visible from a single viewpoint and are a promising approach for alleviating the problem of occlusions. We present a comprehensive user study that investigates the effectiveness of MPIs for 3-D scene exploration. A total of 47 subjects performed searching, counting, and spatial orientation tasks using both conventional and multiperspective images

  • Tracking and frame-rate enhancement for real-time 2D human pose estimation
    Vis. Comput. (IF 1.415) Pub Date : 2019-09-21
    Madhawa Vidanpathirana, Imesha Sudasingha, Jayan Vidanapathirana, Pasindu Kanchana, Indika Perera

    We propose a near real-time solution for frame-rate enhancement that enables the use of existing sophisticated pose estimation solutions at elevated frame rates. Our approach couples a keypoint human pose estimator with optical flow using a multistage system of queues operating in a multi-threaded environment. As additional contributions, we propose a pose tracking solution and an approach to overcome

  • Action matching network: open-set action recognition using spatio-temporal representation matching
    Vis. Comput. (IF 1.415) Pub Date : 2019-09-18
    Jongmin Yu, Du Yong Kim, Yongsang Yoon, Moongu Jeon

    In this paper, we address an open-set action recognition problem. While the closed-set action recognition classifies test samples into the same classes of actions used for model training, the problem of the open-set action recognition is more challenging because there is a possibility that the trained model has to recognize actions which do not appear in the training set. To address this issue, we

  • OP-MR: the implementation of order picking based on mixed reality in a smart warehouse
    Vis. Comput. (IF 1.415) Pub Date : 2019-09-18
    Ummi Khaira Latif, Soo Young Shin

    This paper presents a mixed-reality (MR) application called order picking with mixed reality (OP-MR) for the order-picking activities in a smart warehouse. OP-MR is a set of applications operated by an administrator through a computer server and by the staff using the HoloLens MR device. OP-MR is built to reduce the operational time of an order-picking activity by providing the shortest route to the

  • Support vector regression-based 3D-wavelet texture learning for hyperspectral image compression
    Vis. Comput. (IF 1.415) Pub Date : 2019-09-18
    Nadia Zikiou, Mourad Lahdir, David Helbert

    Hyperspectral imaging is known for its rich spatial–spectral information. The spectral bands provide the ability to distinguish substances spectra which are substantial for analyzing materials. However, high-dimensional data volume of hyperspectral images is problematic for data storage. In this paper, we present a lossy hyperspectral image compression system based on the regression of 3D wavelet coefficients

  • Human position and head direction tracking in fisheye camera using randomized ferns and fisheye histograms of oriented gradients
    Vis. Comput. (IF 1.415) Pub Date : 2019-09-17
    Veerachart Srisamosorn, Noriaki Kuwahara, Atsushi Yamashita, Taiki Ogata, Shouhei Shirafuji, Jun Ota

    This paper proposes a system for tracking human position and head direction using fisheye camera mounted to the ceiling. This is believed to be the first system to estimate head direction from ceiling-mounted fisheye camera. Fisheye histograms of oriented gradients descriptor is developed as a substitute to the histograms of oriented gradients descriptor which has been widely used for human detection

  • Saliency bagging: a novel framework for robust salient object detection
    Vis. Comput. (IF 1.415) Pub Date : 2019-09-14
    Vivek Kumar Singh, Nitin Kumar

    Salient object detection is a challenging task, and several methods have been proposed for the same in the literature. The problem lies in that most of the methods perform good on a particular set of images but fail when exposed to a variety of different set of images. Here, we address this problem by proposing a novel framework called saliency bagging for detecting salient object(s) in digital images

  • Depth image upsampling based on guided filter with low gradient minimization
    Vis. Comput. (IF 1.415) Pub Date : 2019-09-13
    Hang Yang, Zhongbo Zhang

    In this paper, we present a novel upsampling framework to enhance the spatial resolution of the depth image. In our framework, the upscaling of a low-resolution depth image is guided by a corresponding intensity images; we formulate it as a cost aggregation problem with the guided filter. However, the guided filter does not make full use of the information of the depth image. Since depth images have

  • 3D human pose estimation by depth map
    Vis. Comput. (IF 1.415) Pub Date : 2019-09-03
    Jianzhai Wu, Dewen Hu, Fengtao Xiang, Xingsheng Yuan, Jiongming Su

    We present a new approach for 3D human pose estimation from a single image. State-of-the-art methods for 3D pose estimation have focused on predicting a full-body pose of a single person and have not given enough attention to the challenges in application: incompleteness of body pose and existence of multiple persons in image. In this paper, we introduce depth maps to solve these problems. Our approach

  • Deep generative smoke simulator: connecting simulated and real data
    Vis. Comput. (IF 1.415) Pub Date : 2019-08-29
    Jinghuan Wen, Huimin Ma, Xiong Luo

    We propose a novel generative adversarial architecture to generate realistic smoke sequences. Physically based smoke simulation methods are difficult to match with real-captured data since smoke is vulnerable to disturbance. In our work, we design a generator that takes into account the temporal movement of smoke as well as detailed structures. With the help of convolutional neural networks and long

  • Automatic darkest filament detection (ADFD): a new algorithm for crack extraction on two-dimensional pavement images
    Vis. Comput. (IF 1.415) Pub Date : 2019-08-28
    Wissam Kaddah, Marwa Elbouz, Yousri Ouerhani, Ayman Alfalou, Marc Desthieux

    Pavement condition information is a significant component in pavement management systems. Precise extraction of road degradations particularly cracks is a critical task for surface safety. Manual surveys, which are labor intensive and costly, have induced several researchers to investigate the use of image processing to achieve automated pavement distress ratings. In the context of fine structures

  • Correction to: Physics-preserving fluid reconstruction from monocular coupling with SFS and SPH
    Vis. Comput. (IF 1.415) Pub Date : 2019-08-12
    Xiaoying Nie, Yong Hu, Xukun Shen

    The Acknowledgements section is missing in the original article. It is given below.

  • Image saliency detection via multi-scale iterative CNN
    Vis. Comput. (IF 1.415) Pub Date : 2019-08-06
    Kun Huang, Shenghua Gao

    Salient object detection has received increasingly more attention and achieved significant progress lately due to the powerful features learned by deep convolutional neural networks (CNNs). In this work, we propose a multi-scale iterative CNN for salient object detection, which has two complementary subnetworks at different spatial scales. For each subnetwork, we augment the CNN structures with an

  • Literature Explorer: effective retrieval of scientific documents through nonparametric thematic topic detection
    Vis. Comput. (IF 1.415) Pub Date : 2019-08-02
    Shaopeng Wu, Youbing Zhao, Farzad Parvinzamir, Nikolaos Th. Ersotelos, Hui Wei, Feng Dong

    Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through

  • Learning semantic dependencies with channel correlation for multi-label classification
    Vis. Comput. (IF 1.415) Pub Date : 2019-08-01
    Lixia Xue, Di Jiang, Ronggui Wang, Juan Yang, Min Hu

    Multi-label image classification is a fundamental and challenging task in computer vision. Although remarkable success has been achieved by applying CNN–RNN pattern, such method has a slow convergence rate due to the existence of RNN module. Instead of utilizing the RNN modules, this paper proposes a novel channel correlation network which is fully based on convolutional neural network (CNN) to model

  • Automatic semantic style transfer using deep convolutional neural networks and soft masks
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-31
    Hui-Huang Zhao, Paul L. Rosin, Yu-Kun Lai, Yao-Nan Wang

    This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout and ruining the transfer result. In order to reduce or avoid such effects

  • Integrating global and local image features for enhanced loop closure detection in RGB-D SLAM systems
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-29
    Oguzhan Guclu, Ahmet Burak Can

    Loop closure detection is essential for simultaneous localization and mapping systems to decrease accumulating drift of trajectory estimations. Robust loop closure detection is specifically important in large-scale mapping, but it gets more challenging as the mapping environment grows. This paper proposes a SLAM system utilizing a two-pass loop closure detection method to improve mapping accuracy in

  • 3D RANs: 3D Residual Attention Networks for action recognition
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-25
    Jiahui Cai, Jianguo Hu

    In this work, we propose 3D Residual Attention Networks (3D RANs) for action recognition, which can learn spatiotemporal representation from videos. The proposed network consists of attention mechanism and 3D ResNets architecture, and it can capture spatiotemporal information in an end-to-end manner. Specifically, we separately add the attention mechanism along channel and spatial domain to each block

  • Deep motion templates and extreme learning machine for sign language recognition
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-25
    Javed Imran, Balasubramanian Raman

    Sign language is a visual language used by persons with hearing and speech impairment to communicate through fingerspellings and body gestures. This paper proposes a framework for automatic sign language recognition without the need of hand segmentation. The proposed method first generates three different types of motion templates: motion history image, dynamic image and our proposed RGB motion image

  • Physics-preserving fluid reconstruction from monocular video coupling with SFS and SPH
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-25
    Xiaoying Nie, Yong Hu, Xukun Shen

    We propose a joint method to reconstruct dynamic fluid volume sequences from a monocular video. Compared with previous methods, sophisticated equipment or careful experimental setups are not required. In order to recover the surface detail and maintain its physical property, a joint reconstruction method coupled with shape from shading (SFS) and smoothed particle hydrodynamics (SPH) algorithms is proposed

  • End-to-end deep metric network for visual tracking
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-24
    Shengjing Tian, Shuwei Shen, Guoqiang Tian, Xiuping Liu, Baocai Yin

    In this paper, we propose an end-to-end deep metric network (DMN) for visual tracking, where any target can be accurately tracked given only a bounding box of the first frame. Our main motivation is to make the network learn to learn a deep distance metric by following the philosophy of one-shot learning. Instead of utilizing a hand-crafted distance metric like Euclidean distance, our DMN focuses on

  • A personalized traffic simulation integrating emotion using a driving simulator
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-24
    Xinmiao Fan, Gaofeng Pan, Yan Mao, Wu He

    Since driver’s behavior is affected by driving environment and driver’s individual characteristics, it is important to understand driver’s behavior from these viewpoints. We present a novel driver’s behavior simulation that considers both the stable personality of individuals and the changes in driving behavior when the environment changed. In this study, driving styles are classified as aggressive

  • Fast two-stage segmentation model for images with intensity inhomogeneity
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-18
    Yangyang Song, Guohua Peng

    Based on the local correntropy-based K-means clustering active contour model, this paper proposes a fast two-stage segmentation method for intensity inhomogeneous images. Under our framework, the segmentation process is split into two stages. In the first stage, we preliminary segment the down-sampled images by the proposed relaxed anisotropic–isotropic local correntropy-based K-means clustering (AILCK)

  • Visual tracking tracker via object proposals and co-trained kernelized correlation filters
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-17
    Jimmy T. Mbelwa, Qingjie Zhao, Fasheng Wang

    Visual tracking is a challenging task in the field of computer vision with wide applications in intelligent and surveillance systems. Recently, correlation trackers have shown great achievement in visual tracking due to its high efficiency. However, such trackers have a problem of handling fast motion, motion blur, illumination variations, background clutter and drifting away caused by occlusion and

  • Classification of priors and regularization techniques appurtenant to single image super-resolution
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-17
    Garima Pandey, Umesh Ghanekar

    Image super-resolution (SR) is the process of restoration of high-resolution (HR) image from its degraded images/image. Exigency of high-quality images in different technical fields has led it to be one of the prominent research domains in the area of digital image processing. In SR process, image reconstruction from single low-resolution (LR) image is more onerous process than obtaining it from multi-LR

  • Fast and sub-pixel precision target tracking algorithm for intelligent dual-resolution camera
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-13
    Zhuang He, Qi Li, Huajun Feng, Zhihai Xu

    The intelligent dual-resolution camera can provide large imaging field and high-resolution for the parts in which we are interested simultaneously. It has important applications in visual tracking. Though many trackers show great robustness on recent benchmarks, few of them make high precision and run in real-time, which is harmful to practical applications. In this paper, we propose a fast and sub-pixel

  • Pattern understanding and synthesis based on layout tree descriptor
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-12
    Xinwei Zhang, Jin Wang, Guodong Lu, Xusheng Zhang

    Synthesis from existing examples is a promising way to generate new patterns. However, pattern synthesis is challenging because it is difficult to understand and generate complex structures in patterns. In this paper, we propose an approach based on the layout tree descriptor (LTD) to understand and synthesize patterns from existing ones. The LTD is a binary tree that parametrically describes all primitives

  • DTW-CNN: time series-based human interaction prediction in videos using CNN-extracted features
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-11
    Mahlagha Afrasiabi, Hassan khotanlou, Muharram Mansoorizadeh

    Recently, the prediction of interactions in videos has been an active subject in computer vision. Its goal is to deduce interactions in their early stages. Many approaches have been proposed to predict interaction, but it still remains a challenging problem. In the present paper, features are optical flow fields extracted from video frames using convolutional neural networks. This feature, which is

  • HDR image retrieval by using color-based descriptor and tone mapping operator
    Vis. Comput. (IF 1.415) Pub Date : 2019-07-02
    Raoua Khwildi, Azza Ouled Zaid

    Various methods have been performed for the purpose of Low Dynamic Range (LDR) image retrieval. However, no major work concerning the High Dynamic Range (HDR) image indexing has been widely diffused yet. We therefore propose a method that tackles the problem of efficiently and accurately retrieving HDR images. The proposed system is based on a hybrid descriptor which combines two color features. The

  • Cancelable multi-biometric recognition system based on deep learning
    Vis. Comput. (IF 1.415) Pub Date : 2019-06-29
    Essam Abdellatef, Nabil A. Ismail, Salah Eldin S. E. Abd Elrahman, Khalid N. Ismail, Mohamed Rihan, Fathi E. Abd El-Samie

    In this paper, we propose a cancelable multi-biometric face recognition method that uses multiple convolutional neural networks (CNNs) to extract deep features from different facial regions. We also propose a new CNN architecture that exploits batch normalization, depth concatenation and a residual learning framework. The proposed method adopts a region-based technique in which face, eyes, nose and

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