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brazilian symposium on Computing System Engineering (SBESC)

作者： Tiago Almeida Isaías Felzmann Lucas Wanner Department of Computer Science Universidade Federal do Tocantins (UFT) Palmas Brazil Institute of Computing University of Campinas (UNICAMP) Campinas Brazil

The approximate hardware design is a suitable candidate for newer custom designs to improve performance, resource usage, and energy efficiency. Many researchers have used different approaches to creating simulated experiments, such as FPGA and ASIC. This fact can imply deviation in the independent variable under observation, i.e., the area utilization, and power dissipation, which can vary asymmetrically. However, how asymmetrical is this variation? What if we used a high-level synthesis approach? Can a characterization of the ASIC design be transferred to an FPGA design? Thus, this research has analyzed power dissipation, and resource utilization (LUT+FF) in approximate adders to characterize the resource usage of design accelerators in FPGA and ASIC from the EvoApprox library. The application to be tested was the Sobel filter for image processing. We used Polarfire, Arty 35T, and Alveo U250 FPGAs. The hypotheses of the experiment were evaluated using three different statistical tests, One-Way ANOVA, Tukey’s test, and Bartlett’s test. We found that even though ASICs and FPGAs are commonly considered with different resource usage, approximated designs in ASIC can be statistically similar to FPGAs depending on the application, and the difference between them could be negligible for power and mostly for the area.

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Multifocal Stereoscopic Projection Mapping

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IEEE TRANSACTIONS ON VISUALIZATION AND computer graphics 2021年第11期27卷 4256-4266页

作者： Kimura, Sorashi Iwai, Daisuke Punpongsanon, Parinya Sato, Kosuke Osaka Univ Grad Sch Engn Sci Suita Osaka Japan Japan Sci & Technol Agcy PRESTO Kawaguchi Saitama Japan

Stereoscopic projection mapping (PM) allows a user to see a three-dimensional (3D) computer-generated (CG) object floating over physical surfaces of arbitrary shapes around us using projected imagery. However, the current stereoscopic PM technology only satisfies binocular cues and is not capable of providing correct focus cues, which causes a vergence-accommodation conflict (VAC). Therefore, we propose a multifocal approach to mitigate VAC in stereoscopic PM. Our primary technical contribution is to attach electrically focus-tunable lenses (ETLs) to active shutter glasses to control both vergence and accommodation. Specifically, we apply fast and periodical focal sweeps to the ETLs, which causes the "virtual image" (as an optical term) of a scene observed through the ETLs to move back and forth during each sweep period. A 3D CG object is projected from a synchronized high-speed projector only when the virtual image of the projected imagery is located at a desired distance. This provides an observer with the correct focus cues required. In this study, we solve three technical issues that are unique to stereoscopic PM: (1) The 3D CG object is displayed on non-planar and even moving surfaces;(2) the physical surfaces need to be shown without the focus modulation;(3) the shutter glasses additionally need to be synchronized with the ETLs and the projector. We also develop a novel compensation technique to deal with the "lens breathing" artifact that varies the retinal size of the virtual image through focal length modulation. Further, using a proof-of-concept prototype, we demonstrate that our technique can present the virtual image of a target 3D CG object at the correct depth. Finally, we validate the advantage provided by our technique by comparing it with conventional stereoscopic PM using a user study on a depth-matching task.

关键词： Stereo image processing Three-dimensional displays Optical imaging Adaptive optics Glass Lenses Observers Stereoscopic projection mapping multifocal display vergence-accommodation conflict

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Single-channel, ultraportable, real-time imaging system based on deep learning: a proof-of-concept

Single-channel, ultraportable, real-time imaging system base...

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IEEE symposium (IUS) Ultrasonics

作者： Valentino Meacci Edoardo Bosco Alessandro Ramalli Enrico Boni Piero Tortoli Daniele Mazierli Edoardo Spairani Giulia Matrone Department of Information Engineering University of Florence Florence Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

There is a growing demand for user-friendly technologies that may empower individuals to independently monitor their physiological parameters. Ultrasound systems are highly promising for healthcare self-monitoring. To address these challenges, in this work, we present the proof of concept (PoC) of a real-time ultraportable system with a single-channel transmission and reception strategy and a deep learning-based image reconstruction method. The PoC uses the ULA-OP 256 scanner, employing a monostatic synthetic aperture focusing technique, along with a convolutional neural network (CNN) trained to generate B-mode images. Testing was carried out using a CNN running on a mid-range graphics processing unit (GPU), both on phantoms and in vivo scenarios, comparing the image quality achieved by the CNN with that of a delay-and-sum (DAS) beamformer. The results highlight that the CNN outperformed the DAS, showing a better image quality. Furthermore, the CNN achieved a real-time frame rate of 37.0 frames per second, proving that ultraportable ultrasound systems are highly promising for the future of self-monitoring instruments.

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PaletteGaussian: 3D Photorealistic Color Editing with Gaussian Splatting

PaletteGaussian: 3D Photorealistic Color Editing with Gaussi...

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International symposium on Mixed and Augmented Reality (ISMAR)

作者： Chenqu Ren Haolei Qiu Yeheng Shao Zherui Qiu Kaiwen Song East China Normal University Changchun University of Science and Technology University of Science and Technology of China

ISBN: (数字)9798331516475

ISBN: (纸本)9798331516482

3D editing, particularly involving realistic color editing, plays a crucial role in various multimedia domains, such as augmented reality and filmmaking. Traditional 3D reconstruction methods encounter challenges in achieving high-fidelity reconstruction for complex scenes. In recent years, methods based on implicit 3D representations, like Neural Radiance Fields (NeRF), have demonstrated effectiveness in rendering complex scenes. However, these methods face difficulties in interactively editing scene colors and often exhibit slow processing speeds. Addressing these challenges, we propose the PaletteGaussian framework for interactive color editing and real-time rendering based on a palette and 3D Gaussian Splatting (3DGS). First, we introduce a two-stage training strategy to ensure rendering quality and enhance the accuracy of object extraction in the scene. Next, we present an image-driven learning-based approach, I-learning, for convenient interactive color editing driven by both images and text. Finally, we perform parameter baking to achieve real-time rendering. In summary, PaletteGaussian supports two editing levels, scene-level and object-level, offering three interaction modes: manual, image-driven, and text-driven editing. It enables high-resolution real-time rendering. Our comprehensive experiments demonstrate that PaletteGaussian exhibits efficient performance, diverse interaction modes, and realistic color editing.

关键词： Training Three-dimensional displays Accuracy image color analysis Semantics Rendering (computer graphics) Neural radiance field Real-time systems image reconstruction Augmented reality

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Multi-selection Attention for Multimodal Aspect-level Sentiment Classification 13

Multi-selection Attention for Multimodal Aspect-level Sentim...

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13th IEEE International symposium on Parallel Architectures, Algorithms and Programming, PAAP 2022

作者： Miao, Yuqing Luo, Ronghai Liu, Tonglai Zhang, Wanzhen Cai, Guoyong Zhou, Ming Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Zhongkai University of Agriculture and Engineering College of Information Science and Technology Guangzhou China Guilin Hivision Technology Company Guilin China

ISBN: (纸本)9781665452182

Multimodal aspect-level sentiment classification aims to utilize images to recognize the sentiment polarity of target aspects in text. To address the issues of low utilization of inter-modal complementary information and vanishing gradients, a multimodal aspect-level sentiment based on multi-selection attention mechanism is proposed. Multi-selection attention mechanism explicitly considers the contribution of different modalities to aspects and utilizes shared features and private features of image modality to enhance sentiment expression of target aspects. On this basis, inspired by residual connections in ResNet and encoder-decoders in U-Net, a simple and effective residual encoder-decoder is proposed to mine deep information and avoid vanishing gradients. The experimental results on two public sentiment datasets show that the proposed model can better utilize images to supplement textual modality and improve the accuracy of sentiment classification. © 2022 IEEE.

关键词： Classification (of information)

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Crack Detection and 3D Scene Creation of Road Bridge Using Deep Learning and image processing

Crack Detection and 3D Scene Creation of Road Bridge Using D...

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International symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Kotaro Ishida Yasutoshi Nomura Graduate school of science and engineering Ritsumeikan University Kusatsu Japan Civil and environmental engineering Ritsumeikan University Kusatsu Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

In Japan, the number of social infrastructure facilities, such as bridges and tunnels, which were constructed during a period of high economic growth and have been in existence for 50 years, has been increasing, and inspection is important for managing them. In 2014, it became obligatory to inspect structures once every 5 years. From the above decision, the inspection of structures by engineers using close-up visual observation and visible light images has been carried out periodically. However, damage inspection by visual inspection has become a problem from the viewpoint of cost such as human cost, the viewpoint of labor, and time cost. The periodical inspection is determined to utilize close-up visual observation by engineers or the equivalent diagnosis, and digitization and sharing of the inspection results are important in the management of infrastructure. Therefore, the construction of a system that carries out the diagnosis equivalent to close-up visual observation and digitizes the result is tried.

关键词： YOLO Visualization Three-dimensional displays Costs Accuracy Structural panels Transfer learning Training data Inspection Rendering (computer graphics)

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TrafficEd: Deployment and Management System of Edge AI Cameras

TrafficEd: Deployment and Management System of Edge AI Camer...

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IEEE symposium on Network Operations and Management

作者： Guan-Wen Chen Yi-Hsiu Lin Tsì-Uí İk Department of Computer Science College of Computer Science National Yang Ming Chiao Tung University Hsinchu City Taiwan

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Artificial intelligence (AI) cameras are edge devices with embedded graphics processing units that can run lightweight deep learning models. In traffic management applications, traffic flow and traffic incidents can be detected from roadside images with the use of AI cameras, and only detected high-level information is sent to the server to minimize the use of network bandwidth and server resources. However, because edge devices are computationally limited, models should be optimized before they are deployed to these AI cameras. In addition, environment-related parameters must be configured appropriately after model deployment. Thus, an AI camera management system is required. Consequently, in this study, we designed a deployment and management system for AI cameras; this system can perform model optimization and parameter configuration with ease. The main functions of this system involve 1) automatic modeling and code transfer, 2) the remote deployment of deep learning models, 3) the remote configuration of relevant applications, and 4) the presentation of analytical results on a graphical user interface. The performance of the developed system was investigated by using it to deploy traffic analysis models and visualize analysis results. The experimental results indicate that this system achieved all of its design goals.

关键词： Deep learning Analytical models Computational modeling image edge detection Cameras Turning Software

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Efficient Construction of Delaunay Triangulations on Riemannian Manifolds with the Bowyer-Watson Method

Efficient Construction of Delaunay Triangulations on Riemann...

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International Conference on Digital Home (ICDH)

作者： Dejiangmo Qian Wang Li Zhou Xinyun Zhang Dawei Luo Northwest Minzu University Colleage of Mathematics and Computer Science Lanzhou China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

This paper explores the application of the Bowyer-Watson algorithm for constructing Delaunay triangulations on Riemannian manifolds, with a particular focus on karst terrain and channel detection scenarios. We define geodesic disks with sufficiently small radii and demonstrate the existence of a net within these disks that allows for the construction of triangles satisfying the Delaunay condition. By analyzing the insertion of points both inside and outside of triangles, we ensure proper point placement and perform edge flips as necessary to maintain the Delaunay property. Compared to other algorithms, the Bowyer-Watson approach offers notable advantages for achieving Delau-nay triangulation on Riemannian manifolds due to its flexibility and locality. This method effectively manages complex geometric structures by adaptively adjusting the triangulation, ensuring both accuracy and efficiency in non-Euclidean spaces. It is particularly well-suited for modeling karst terrain features and detecting channels, providing a crucial theoretical foundation and practical guidance for future numerical computations and geometric modeling on curved surfaces.

关键词： Manifolds graphics Accuracy Data analysis Scientific computing image edge detection Geometric modeling Focusing Feature extraction Numerical models

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Proceeding - 27th IEEE International symposium on High Performance computer Architecture, HPCA 2021

Proceeding - 27th IEEE International Symposium on High Perfo...

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27th Annual IEEE International symposium on High Performance computer Architecture, HPCA 2021

ISBN: (纸本)9780738123370

The proceedings contain 69 papers. The topics discussed include: mix and match: a novel FPGA-centric deep neural network quantization framework;BRIM: bistable resistively-coupled Ising machine;systematic approaches for precise and approximate quantum state runtime assertion;DepGraph: a dependency-driven accelerator for efficient iterative graph processing;Chopin: scalable graphics rendering in multi-GPU systems via parallel image composition;new models for understanding and reasoning about speculative execution attacks;ultra-elastic CGRAs for irregular loop specialization;analyzing and leveraging decoupled L1 caches in GPUs;and operating liquid-cooled large-scale systems: long-term monitoring, reliability analysis, and efficiency measures.

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17th International symposium on Visual Computing, ISVC 2022

17th International Symposium on Visual Computing, ISVC 2022

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17th International symposium on Visual Computing, ISVC 2022

ISBN: (纸本)9783031207150

The proceedings contain 69 papers. The special focus in this conference is on Visual Computing. The topics include: Recognition of Aquatic Invasive Species Larvae Using Autoencoder-Based Feature Averaging;subspace Analysis for Multi-temporal Disaster Mapping Using Satellite imagery;open-Set Plankton Recognition Using Similarity Learning;sensor Fusion Operators for Multimodal 2D Object Detection;learning When to Say "I Don’t Know";Multi-class Detection and Tracking of Intracorporeal Suturing Instruments in an FLS Laparoscopic Box Trainer Using Scaled-YOLOv4;A New Approach to Visual Classification Using Concatenated Deep Learning for Multimode Fusion of EEG and image Data;deep Learning-Based Classification of Plant Xylem Tissue from Light Micrographs;vampNet: Unsupervised Vampirizing of Convolutional Networks;learning Representations for Masked Facial Recovery;RSI-Grad-CAM: Visual Explanations from Deep Networks via Riemann-Stieltjes Integrated Gradient-Based Localization;Deep Labeling of fMRI Brain Networks Using Cloud Based processing;semantic Segmentation Using Neural Ordinary Differential Equations;graphing the Future: Activity and Next Active Object Prediction Using Graph-Based Activity Representations;detecting Fall Actions of Videos by Using Weakly-Supervised Learning and Unsupervised Clustering Learning;multi-property Tensor-Based Learning for Abnormal Event Detection;depth-Based vs. Color-Based Pose Estimation in Human Action Recognition;Cross-Domain Learning in Deep HAR Models via Natural Language processing on Action Labels;visualizing Data Flows in computer graphics Programs for Code Comprehension and Debugging;a Practical Algorithm for Degree-k Voronoi Domains of Three-Dimensional Periodic Point Sets;deep Learning Based Shrimp Classification;end-to-End Deep Neural Network for Illumination Consistency and Global Illumination;pruning-Based Topology Refinement of 3D Mesh Using a 2D Alpha Mask;ConnectedUNets++: Mass Segmentation from Whole Mammographic images

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