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  • Psychological potential field and human eye fixation on binary line-drawing images: A comparative experimental study
    Comp. Visual Media Pub Date : 2020-06-10
    Naoyuki Awano, Yuki Hayashi

    Quantitatively evaluating the psychological and perceptual effects of objects is an important issue, but is difficult. In cognitive studies, the psychological potential field (PPF), which represents psychological intensities in vision and can be calculated by applying computational algorithms to digital images, may help with this issue. Although studies have reported using the PPF to evaluate psychological

  • A survey on deep geometry learning: From a representation perspective
    Comp. Visual Media Pub Date : 2020-06-10
    Yun-Peng Xiao, Yu-Kun Lai, Fang-Lue Zhang, Chunpeng Li, Lin Gao

    Researchers have achieved great success in dealing with 2D images using deep learning. In recent years, 3D computer vision and geometry deep learning have gained ever more attention. Many advanced techniques for 3D shapes have been proposed for different applications. Unlike 2D images, which can be uniformly represented by a regular grid of pixels, 3D shapes have various representations, such as depth

  • 3D hypothesis clustering for cross-view matching in multi-person motion capture
    Comp. Visual Media Pub Date : 2020-06-10
    Miaopeng Li, Zimeng Zhou, Xinguo Liu

    We present a multiview method for markerless motion capture of multiple people. The main challenge in this problem is to determine cross-view correspondences for the 2D joints in the presence of noise. We propose a 3D hypothesis clustering technique to solve this problem. The core idea is to transform joint matching in 2D space into a clustering problem in a 3D hypothesis space. In this way, evidence

  • S4Net: Single stage salient-instance segmentation
    Comp. Visual Media Pub Date : 2020-06-10
    Ruochen Fan, Ming-Ming Cheng, Qibin Hou, Tai-Jiang Mu, Jingdong Wang, Shi-Min Hu

    In this paper, we consider salient instance segmentation. As well as producing bounding boxes, our network also outputs high-quality instance-level segments as initial selections to indicate the regions of interest. Taking into account the category-independent property of each target, we design a single stage salient instance segmentation framework, with a novel segmentation branch. Our new branch

  • Saliency-based image correction for colorblind patients
    Comp. Visual Media Pub Date : 2020-06-10
    Jinjiang Li, Xiaomei Feng, Hui Fan

    Improper functioning, or lack, of human cone cells leads to vision defects, making it impossible for affected persons to distinguish certain colors. Colorblind persons have color perception, but their ability to capture color information differs from that of normal people: colorblind and normal people perceive the same image differently. It is necessary to devise solutions to help persons with color

  • JMNet: A joint matting network for automatic human matting
    Comp. Visual Media Pub Date : 2020-04-14
    Xian Wu, Xiao-Nan Fang, Tao Chen, Fang-Lue Zhang

    We propose a novel end-to-end deep learning framework, the Joint Matting Network (JMNet), to automatically generate alpha mattes for human images. We utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module, which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network

  • A detail preserving neural network model for Monte Carlo denoising
    Comp. Visual Media Pub Date : 2020-04-02
    Weiheng Lin, Beibei Wang, Lu Wang, Nicolas Holzschuch

    Monte Carlo based methods such as path tracing are widely used in movie production. To achieve low noise, they require many samples per pixel, resulting in long rendering time. To reduce the cost, one solution is Monte Carlo denoising, which renders the image with fewer samples per pixel (as little as 128) and then denoises the resulting image. Many Monte Carlo denoising methods rely on deep learning:

  • G2MF-WA: Geometric multi-model fitting with weakly annotated data
    Comp. Visual Media Pub Date : 2020-04-02
    Chao Zhang, Xuequan Lu, Katsuya Hotta, Xi Yang

    In this paper we address the problem of geometric multi-model fitting using a few weakly annotated data points, which has been little studied so far. In weak annotating (WA), most manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. SuchWA data can naturally arise through interaction in various tasks. For example, in the case of homography estimation, one can easily

  • What and where: A context-based recommendation system for object insertion
    Comp. Visual Media Pub Date : 2020-04-02
    Song-Hai Zhang, Zheng-Ping Zhou, Bin Liu, Xi Dong, Peter Hall

    We propose a novel problem revolving around two tasks: (i) given a scene, recommend objects to insert, and (ii) given an object category, retrieve suitable background scenes. A bounding box for the inserted object is predicted in both tasks, which helps downstream applications such as semiautomated advertising and video composition. The major challenge lies in the fact that the target object is neither

  • WaterNet: An adaptive matching pipeline for segmenting water with volatile appearance
    Comp. Visual Media Pub Date : 2020-03-23
    Yongqing Liang, Navid Jafari, Xing Luo, Qin Chen, Yanpeng Cao, Xin Li

    We develop a novel network to segment water with significant appearance variation in videos. Unlike existing state-of-the-art video segmentation approaches that use a pre-trained feature recognition network and several previous frames to guide segmentation, we accommodate the object’s appearance variation by considering features observed from the current frame. When dealing with segmentation of objects

  • Learning local shape descriptors for computing non-rigid dense correspondence
    Comp. Visual Media Pub Date : 2020-03-23
    Jianwei Guo, Hanyu Wang, Zhanglin Cheng, Xiaopeng Zhang, Dong-Ming Yan

    A discriminative local shape descriptor plays an important role in various applications. In this paper, we present a novel deep learning framework that derives discriminative local descriptors for deformable 3D shapes. We use local “geometry images” to encode the multi-scale local features of a point, via an intrinsic parameterization method based on geodesic polar coordinates. This new parameterization

  • VR content creation and exploration with deep learning: A survey
    Comp. Visual Media Pub Date : 2020-03-23
    Miao Wang, Xu-Quan Lyu, Yi-Jun Li, Fang-Lue Zhang

    Virtual reality (VR) offers an artificial, computer generated simulation of a real life environment. It originated in the 1960s and has evolved to provide increasing immersion, interactivity, imagination, and intelligence. Because deep learning systems are able to represent and compose information at various levels in a deep hierarchical fashion, they can build very powerful models which leverage large

  • A practical path guiding method for participating media
    Comp. Visual Media Pub Date : 2020-03-23
    Hong Deng, Beibei Wang, Rui Wang, Nicolas Holzschuch

    Rendering translucent materials is costly: light transport algorithms need to simulate a large number of scattering events inside the material before reaching convergence. The cost is especially high for materials with a large albedo or a small mean-free-path, where higher-order scattering effects dominate. In simple terms, the paths get lost in the medium. Path guiding has been proposed for surface

  • Real-time rendering of layered materials with anisotropic normal distributions
    Comp. Visual Media Pub Date : 2020-01-27
    Tomoya Yamaguchi, Tatsuya Yatagawa, Yusuke Tokuyoshi, Shigeo Morishima

    This paper proposes a lightweight bidirectional scattering distribution function (BSDF) model for layered materials with anisotropic reflection and refraction properties. In our method, each layer of the materials can be described by a microfacet BSDF using an anisotropic normal distribution function (NDF). Furthermore, the NDFs of layers can be defined on tangent vector fields, which differ from layer

  • Efficient ray casting of volumetric images using distance maps for empty space skipping
    Comp. Visual Media Pub Date : 2020-01-23
    Lachlan J. Deakin, Mark A. Knackstedt

    Volume and isosurface rendering are methods of projecting volumetric images to two dimensions for visualisation. These methods are common in medical imaging and scientific visualisation. Head-mounted optical see-through displays have recently become an affordable technology and are a promising platform for volumetric image visualisation. Images displayed on a head-mounted display must be presented

  • InSocialNet: Interactive visual analytics for role—event videos
    Comp. Visual Media Pub Date : 2020-01-17
    Yaohua Pan, Zhibin Niu, Jing Wu, Jiawan Zhang

    Role–event videos are rich in information but challenging to be understood at the story level. The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events. Understanding them requires analysis of the video contents for a long duration, which is beyond the ability of current algorithms designed for analyzing short-time dynamics.

  • Mixed reality based respiratory liver tumor puncture navigation
    Comp. Visual Media Pub Date : 2020-01-17
    Ruotong Li, Weixin Si, Xiangyun Liao, Qiong Wang, Reinhard Klein, Pheng-Ann Heng

    This paper presents a novel mixed reality based navigation system for accurate respiratory liver tumor punctures in radiofrequency ablation (RFA). Our system contains an optical see-through head-mounted display device (OST-HMD), Microsoft HoloLens for perfectly overlaying the virtual information on the patient, and a optical tracking system NDI Polaris for calibrating the surgical utilities in the

  • Interactive modeling of lofted shapes from a single image
    Comp. Visual Media Pub Date : 2019-12-04
    Congyue Deng, Jiahui Huang, Yong-Liang Yang

    Modeling the complete geometry of general shapes from a single image is an ill-posed problem. User hints are often incorporated to resolve ambiguities and provide guidance during the modeling process. In this work, we present a novel interactive approach for extracting high-quality freeform shapes from a single image. This is inspired by the popular lofting technique in many CAD systems, and only requires

  • Evaluation of modified adaptive k -means segmentation algorithm
    Comp. Visual Media Pub Date : 2019-07-24
    Taye Girma Debelee, Friedhelm Schwenker, Samuel Rahimeto, Dereje Yohannes

    Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means (MAKM) approach is proposed and evaluated using images

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