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GPU based real time stereoscopic ray tracing

GPU based real time stereoscopic ray tracing

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22nd International symposium on computer and Information Sciences

作者： Es, Alphan Isler, Veysi TUBITAK UZAY METU Ankara Turkey METU Dept Comp Engn Ankara Turkey

ISBN: (纸本)9781424413638

Over the last couple of years graphics processing units (GPU) found in graphics cards evolved into general purpose parallel stream processors. This evolution allows for using GPUs not only for traditional rasterization based rendering but also for global illumination techniques including ray tracing. Fast generation of stereo images is very important for virtual reality applications. Rendering stereo image pairs for left and right eye separately doubles the frame time. This might be a problem for interactive applications especially if computationally expensive rendering techniques such as ray-tracing are employed. It is possible to reduce the stereo image generation time by ray tracing the scene for one eye and then reprojecting the image into the second eye. In this case, only problematic pixels will be ray traced for the second eye. GPUs are designed to write pixel color values at unique screen locations defined by projected geometry. However reprojection may require writing pixel information to multiple screen locations. This is one-to-many dynamic scattering problem in which GPUs perform relatively badly. In this work we devised efficient stereo reprojection methods running fully on the GPU. We demonstrated the technique in our GPU based interactive ray tracer and showed that reprojection method can reduce the stereo frame time considerably. We also propose a GPU based approach to handle missing object problem if an object is seen only by the reprojected eye. Additionally, reprojections of reflection/refraction rays to create approximate images are discussed.

关键词： graphics processing unit

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Face Recognition Using LBP on an image Transformation Based on Complex Network Degrees 32

Face Recognition Using LBP on an Image Transformation Based ...

引用

32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： Boas da Costa, Murilo Villas Villar Couto, Cynthia Martins Couto, Leandro Nogueira Univ Fed Uberlandia Fac Comp Uberlandia MG Brazil Univ Sao Paulo USP Sao Carlos Inst Phys IFSC Sao Paulo Brazil

ISBN: (纸本)9781728152271

Automated visual face recognition involves acquiring descriptive features from the image. Local Binary Patterns (LBP) is a powerful method to that end, capably characterizing local features. An crucial limitation of LBP, however, is that the feature vector's size becomes unmanageable when the method employed on even moderately large regions. In order to describe larger scale features, this work proposes a descriptor based on applying the LBP histogram applied to an image transformation based on node degree data derived from a complex network representation of the original image. The complex network generation heuristic and parameters are discussed. The complex network representation is shown to be able to condense larger scale image patterns into a local value that can be handled by LBP. LBP applied to this image transformation yields results that outperform LBP. We validate our proposed approach by applying our method to a face recognition task using three challenging databases. Results demonstrate that, for a large enough complex network generation radius, our method consistently outperforms LBP, while using a feature vector of the same size.

关键词： Complex networks Local binary patterns Texture recognition

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Energy Embedded Gauss-Seidel Iteration for Soft Body Simulations 32

Energy Embedded Gauss-Seidel Iteration for Soft Body Simulat...

引用

32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： Cetinaslan, Ozan Chaves, Rafael Oliveira Univ Porto Inst Telecomunicacoes Porto Portugal Univ Porto Fac Ciencias Porto Portugal Fed Univ Para Belem Para Brazil State Univ Para Belem Para Brazil

ISBN: (纸本)9781728152271

In this paper, we present our novel energy embedded Gauss-Seidel iteration to simulate soft objects. Our algorithm is inspired by the equality of the kinetic and potential energy changes, and employs the extended Position-Based Dynamics algorithm (XPBD) as the base algorithm. The proposed method does not aim to conserve the total energy of the system, only alters the position constraints based on the kinetic and potential energy balances within the Gauss-Seidel process of the XPBD algorithm. Our algorithm provides an implicit solution for relatively better visual results during the simulation of soft bodies. Since we apply our solution within the Gauss-Seidel iteration, it is not dependent to both simulation step-size and integration methods. We demonstrate the benefits of our method with many position constraints such as geometric deformation constraints, strain based constraints and continuous materials.

关键词： Animation computer graphics Physical Simulation

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CV-C3D: Action Recognition on Compressed Videos with Convolutional 3D Networks 32

CV-C3D: Action Recognition on Compressed Videos with Convolu...

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32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： dos Santos, Samuel Felipe Sebe, Nicu Almeida, Jurandy Univ Fed Sao Paulo UNIFESP Inst Ciencia & Tecnol BR-12247014 Sao Jose Dos Campos SP Brazil Univ Trento UniTn Dept Informat Engn & Comp Sci I-38123 Trento TN Italy

ISBN: (纸本)9781728152271

Action recognition in videos has gained substantial attention from the computer vision community due to the wide range of possible applications. Recent works have addressed this problem with deep learning methods. The main limitation of existing approaches is their difficulty to learn temporal dynamics due to the high computational load demanded for processing huge amounts of data required to train a model. To overcome this problem, we propose a Compressed Video Convolutional 3D network (CV-C3D). It exploits information from the compressed representation of a video in order to avoid the high computational cost for fully decoding the video stream. The speed up of the computation enables our network to use 3D convolutions for capturing the temporal context efficiently. Our network has the lowest computational complexity among all the compared approaches. Results of our approach in the task of action recognition on two public benchmarks, UCF-101 and HMDB-51, were comparable to the baselines, with the advantage of running at faster inference speed.

关键词： Action Recognition Compressed Domain computer Vision Deep Learning Efficiency

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Video Audience Analysis using Bayesian Networks and Face Demographics 32

Video Audience Analysis using Bayesian Networks and Face Dem...

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32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： Oliveira, Italo de P. Interaminense, Carlos D. O. Pereira, Eanes T. Gomes, Herman M. Fed Univ Campina Grande UFCG BR-58429900 Campina Grande PB Brazil

ISBN: (纸本)9781728152271

In this paper, we propose an approach to study and to model audience attention to videos within digital signage scenarios. An experimental setup was conceived to simultaneously display videos of various categories and to capture videos from the audience and surrounding environment. Face analysis via a deep neural network is performed to estimate gender and age groups. In the proposed approach, a Bayesian Network is built to model possible relationships between the audience's age, gender and face size (which is indicative of the distance to the display) and the video content types. A publicly available video dataset of 152 videos was created for displaying purposes. An experimental evaluation indicated varying degrees of attention to different videos, depending on age and gender. The area under the ROC curve of the built Bayesian Network was 0.82. The proposed approach allows to better understand the possible relationships between audience demographics and video contents, which may, in turn, be useful for displaying the most appropriate content to a particular audience, help with the automatic insertion of ads (based on audience categories), among other applications.

关键词： Digital Signage computer Vision Audience Analysis Bayesian Networks Face Analysis.

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Low-Dose CT Dental image Denoising by Morphological Operators and 3D Filtering 32

Low-Dose CT Dental Image Denoising by Morphological Operator...

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32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： Stringhini, Romulo Marconato Welfer, Daniel d'Ornellas, Marcos Cordeiro Tello Gamarra, Daniel Fernando Univ Fed Santa Maria Dept Appl Comp Santa Maria RS Brazil Univ Fed Santa Maria Dept Elect Energy Proc Santa Maria RS Brazil

ISBN: (纸本)9781728152271

The impact in reducing the radiation dose in computed tomography (CT) exams is directly related to the quality of the images obtained in these exams. Such images are degraded by undesirable artifacts, known as noise. In order to improve the quality of these images and provide an accurate medical diagnosis, it is necessary to apply noise reduction techniques. In this study, a method based on structural segmentation and filtering through morphological operators along with a BM3D filtering is proposed to reduce noise and preserve details in low-dose CT dental images. Experimental results of the proposed method were compared with several existing methods and validated using the PSNR, SSIM, MSE and EPI metrics. Our method demonstrated superior performance among the evaluated filters. In comparison to the filter that obtained the best results, our method had a gain of 12.46% on PSNR, 11.11% on SSIM, 14.5% on MSE and 9.63% on EPI metrics.

关键词： BM3D Computed Tomography Low dose Mathematical morphology Noise reduction PSNR

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Rapid Prototyping of OpenCV image processing Applications using ASP

Rapid Prototyping of OpenCV Image Processing Applications us...

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22nd IEEE International symposium on Rapid System Prototyping (RSP)

作者： Muehlbauer, Felix Grosshans, Michael Bobda, Christophe Univ Potsdam Chair Comp Engn Potsdam Germany Univ Arkansas CSCE Fayetteville AR 72701 USA

ISBN: (纸本)9781457706608

image processing is becoming more and more present in our everyday life. With the requirements of miniaturization, low-power, performance in order to provide some intelligent processing directly into the camera, embedded camera will dominate the image processing landscape in the future. While the common approach of developing such embedded systems is to use sequentially operating processors, image processing algorithms are inherently parallel, thus hardware devices like FPGAs provide a perfect match to develop highly efficient systems. Unfortunately hardware development is more difficult and there are less experts available compared to software. Automatizing the design process will leverage the existing infrastructure, thus providing faster time to market and quick investigation of new algorithms. We exploit ASP (answer set programming) for system synthesis with the goal of genarating an optimal hardware software partitioning, a viable communication structure and the corresponding scheduling, from an image processing application.

关键词： computer architecture Delay FPGA Hardware image processing OpenCV image processing Program processors Resource management answer set programming embedded camera embedded systems field programmable gate arrays hardware software partitioning image processing rapid prototyping sequentially operating processors

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Exploring hierarchy simplification for non-significant region removal 32

Exploring hierarchy simplification for non-significant regio...

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32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： Barcelos, Isabela Borlido da Fonseca, Gabriel Barbosa Najman, Laurent Kenmochi, Yukiko Perret, Benjamin Cousty, Jean do Patrocinio, Zenilton K. G., Jr. Guimaraes, Silvio Jamil F. Univ Paris Est LIGM UPEMLV ESIEE ParisENPCCNRSUMR 8049 F-93162 Noisy Le Grand France Pontificia Univ Catolica Minas Gerais Audio Visual Proc Lab VIPLAB BR-31980110 Belo Horizonte MG Brazil

ISBN: (纸本)9781728152271

image segmentation is a classic subject in the field of digital image processing, and it can be used to solve a large variety of problems or serve as preprocessing for other methods of image analysis. Hierarchical image segmentation methods provide a multiscale representation, therefore they produce a nested set of image segmentations in which a result at a given level can be produced by merging regions of the segmentation at its previous level. However, a hierarchical representation may produce small components at its coarser levels, leading to oversegmentations on such scales. To solve this problem, we explore strategies to simplify hierarchies in order to remove non-significant regions, in terms of area, while trying to preserve the hierarchical structure. We evaluate the proposed simplification strategies with different hierarchical segmentation methods on the Pascal Context dataset by using precision-recall measures and fragmentation curves, along with a qualitative assessment showing that the simplification of hierarchies can lead to visually better image segmentations.

关键词： Hierarchical image segmentation Hierarchy simplification Non significant region removal

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Alignment of Local and Global Features from Multiple Layers of Convolutional Neural Network for image Classification 32

Alignment of Local and Global Features from Multiple Layers ...

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32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： dos Santos, Fernando Pereira Ponti, Moacir Antonelli Univ Sao Paulo Inst Math & Comp Sci ICMC Sao Carlos SP Brazil

ISBN: (纸本)9781728152271

Convolutional networks have been extensively applied to obtain features spaces for classification tasks. Although those achieve high accuracy in many scenarios, typically only the top layers of the network are explored. Hence, a relevant question arises from this fact: are initial layers useful in terms of discriminative ability? In this paper, we leverage the complementary description offered by such first layers. Our method consists of features extraction in multiple layers, followed by feature selection, fusion of feature maps from the different layers, and space alignment. Through an extensive experimentation with different datasets and studying different training strategies, our results show that local information, coming from the first layers, may significantly improve the classification performance when merged with a global descriptor extracted from a top layer of the network. We report different methods for reducing the dimensionality of the local descriptors, and guidelines on how to align them so that to perform fusion. Our study encourages future studies on combining feature maps from multiple layers, which may be relevant in particular for transfer learning scenarios.

关键词： Convolutional networks Feature learning Fusion multiple maps Manifold alignment

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Skeleton image Representation for 3D Action Recognition based on Tree Structure and Reference Joints 32

Skeleton Image Representation for 3D Action Recognition base...

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32nd SIBGRAPI Conference on graphics, Patterns and images (SIBGRAPI) / symposium on Virtual and Augmented Reality (SVR)

作者： Caetano, Carlos Bremond, Francois Schwartz, William Robson Univ Fed Minas Gerais Dept Comp Sci Smart Sense Lab Belo Horizonte MG Brazil INRIA Valbonne France

ISBN: (纸本)9781728152271

In the last years, the computer vision research community has studied on how to model temporal dynamics in videos to employ 3D human action recognition. To that end, two main baseline approaches have been researched: (i) Recurrent Neural Networks (RNNs) with Long-Short Term Memory (LSTM);and (ii) skeleton image representations used as input to a Convolutional Neural Network (CNN). Although RNN approaches present excellent results, such methods lack the ability to efficiently learn the spatial relations between the skeleton joints. On the other hand, the representations used to feed CNN approaches present the advantage of having the natural ability of learning structural information from 2D arrays (i.e., they learn spatial relations from the skeleton joints). To further improve such representations, we introduce the Tree Structure Reference Joints image (TSRJI), a novel skeleton image representation to be used as input to CNNs. The proposed representation has the advantage of combining the use of reference joints and a tree structure skeleton. While the former incorporates different spatial relationships between the joints, the latter preserves important spatial relations by traversing a skeleton tree with a depth-first order algorithm. Experimental results demonstrate the effectiveness of the proposed representation for 3D action recognition on two datasets achieving state-of-the-art results on the recent NTU RGB+D 120 dataset.

关键词： 3D action recognition Convolutional Neural Network (CNN) Skeleton image representation Skeleton joints

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