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检索条件"任意字段=Conference on Real-Time Image and Video Processing 2010"

共 8702 条记录，以下是21-30 订阅

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Lithium-niobate photonic integrated circuits for GHz, sub-picojoule/bit optical image processing

Lithium-niobate photonic integrated circuits for GHz, sub-pi...

引用

conference on real-time processing of image, Depth, and video Information

作者： Bankwitz, J. Rasmus Dijkstra, Jelle Pradip, Ravi Schultzen, Philipp McRae, Liam Roemer, Julius Varri, Akhil Lenzini, Francesco Pernice, Wolfram H. P. Heidelberg Univ Kirchhoff Inst Phys Neuenheimer Feld 227 D-69120 Heidelberg Germany Univ Munster Inst Phys Wilhelm Klemm Str 10 D-48149 Munster Germany

ISBN: (纸本)9781510673199;9781510673182

Optical image processing, which capitalizes on the distinctive characteristics of light, facilitates the manipulation of visual data in real-time and at a high speed. This technology is instrumental in performing tasks such as enhancing edges, recognizing patterns, and extracting features, all of which are crucial in fields like medical imaging, surveillance, and industrial automation. In this study, we present the successful demonstration of a photonic integrated circuit (PIC) made of Lithium niobate on insulator, enabling matrix-vector multiplications for image classification. By surpassing an electrical bandwidth of 15 GHz, our experiment showcases the PIC's ability live edge detection and video streaming. Remarkably, its energy efficiency surpasses the limit imposed by electronic systems for each operation by consuming < 10 fJ/bit.

关键词： Lithium niobate photonic computing integrated photonics programmable optical materials neuromorphic computing edge detection

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Efficient and consistent zero-shot video generation with diffusion models

Efficient and consistent zero-shot video generation with dif...

引用

conference on real-time image processing and Deep Learning

作者： Frakes, Ethan Khalid, Umar Chen, Chen Univ Cent Florida 4000 Cent Florida Blvd Orlando FL 32816 USA

ISBN: (纸本)9781510673878;9781510673861

Recent diffusion-based generative models employ methods such as one-shot fine-tuning an image diffusion model for video generation. However, this leads to long video generation times and suboptimal efficiency. To resolve this long generation time, zero-shot text-to-video models eliminate the fine-tuning method entirely and can generate novel videos from a text prompt alone. While the zero-shot generation method greatly reduces generation time, many models rely on inefficient cross-frame attention processors, hindering the diffusion model's utilization for real-time video generation. We address this issue by introducing more efficient attention processors to a video diffusion model. Specifically, we use attention processors (i.e. xFormers, FlashAttention, and HyperAttention) that are highly optimized for efficiency and hardware parallelization. We then apply these processors to a video generator and test with both older diffusion models such as Stable Diffusion 1.5 and newer, high-quality models such as Stable Diffusion XL. Our results show that using efficient attention processors alone can reduce generation time by around 25%, while not resulting in any change in video quality. Combined with the use of higher quality models, this use of efficient attention processors in zero-shot generation presents a substantial efficiency and quality increase, greatly expanding the video diffusion model's application to real-time video generation.

关键词： Attention processor zero-shot diffusion model Stable Diffusion Flash attention real-time video generation real-time video editing cross-frame attention

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Robust Point Tracking Based On image Matching And Machine Learning On video images Taken from Fast Moving Camera 32

Robust Point Tracking Based On Image Matching And Machine Le...

引用

32nd IEEE Signal processing and Communications Applications conference (SIU)

作者： Guven, Ali Yetik, Imam Samil TOBB Ekon & Teknol Univ Elekt & Elekt Muhendisligi Bolumu Ankara Turkiye

ISBN: (纸本)9798350388978;9798350388961

Recently, providing real-time navigation of unmanned aerial vehicles independent of global positioning systems has become of great importance. The state-of-the-art methods based on deep learning, which give good results in certain datasets, and the existing methods can not provide real-time and good solutions on images with dynamic and fast moving. Moreover, the methods, were developed so far, were focused on object-based tracking algorithms. In this paper, the tracking of the points belonging to the target pattern, found by image matching, was performed with the machine learning model we developed for 10 sequential video images. The features extracted for the machine learning model are: (i) the change between the points of the previous image and the image before that, (ii) the points of interest in the previous image, (iii) the changes found with the homography matrix between sequential images. It was experimentally shown that, point tracking can be achieved with the least error, on avarage about 23 pixels for a 2 mega-pixel resolution image, among the algorithms in the literature that can process more than 30 images per second in a CPU environment of 2 GHz or above.

关键词： image matching image homography image-object tracking image-point tracking apattern detection machine learning image processing feature extraction

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Energy-efficient real-time Computer Vision Applications in Practice

Energy-efficient Real-time Computer Vision Applications in P...

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conference on real-time processing of image, Depth, and video Information

作者： Kramer, Mark A. M. Roth, Peter M. Univ Vet Med Vet Pl 1 A-1210 Vienna Austria

ISBN: (纸本)9781510673199;9781510673182

For many practical applications, we face the problem that computer vision systems must be installed in the wild, without or with a limited permanent power supply. Therefore, computationally and energy efficient solutions are needed. In particular, in this work, we show that the meaningful use of single-board computers (SBCs) can help achieve these goals. This is in line with the goals of Green AI. In particular, we show that the computer vision algorithms adopted on SBCs yield competitive results compared to high-performance computing devices. To this end, in addition to quantitative performance evaluations, we also measured and compared the power consumption of the algorithmic and technical setup used for various practical problems. These examples demonstrate the practical sustainability of SBCs. They show their performance, reduced power consumption, and lower environmental impact, while still providing real-time performance.

关键词： Computer vision Green AI Medical information systems real-time video SBC Sustainable IT

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The Research of image Interpolation Algorithm of video on FPGA 7

The Research of Image Interpolation Algorithm of Video on FP...

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7th International conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Li, Shaowei Liao, Liqing School Of Automation Central South University Changsha China

ISBN: (纸本)9798350361445

image interpolation algorithm is an important branch of image processing research. When traditional interpolation is applied, it is easy to cause problems such as edge aliasing and details blurring. In order to solve these problems, this paper combines Canny edge detection to improve the interpolation algorithm, which has better interpolation effect when dealing with images with more details. At the same time, the increase of algorithm complexity makes the traditional implementation method difficult to meet the real-time requirements of video image processing. Field Programmable Gate Array (FPGA) has excellent parallel processing ability. This design constructs and implements a real-time video image interpolation algorithm combined with edge operator on the platform. © 2024 IEEE.

关键词： Interpolation

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MoReSo: A DNN Framework Expediting Content-based video image Retrieval (CBVIR) 32

MoReSo: A DNN Framework Expediting Content-based Video Image...

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32nd European Signal processing conference (EUSIPCO)

作者： Li, Sinian Profeta, Doruk Barokas Dauwels, Justin Delft Univ Technol Signal Proc Syst Dept Microelect Delft Netherlands

ISBN: (纸本)9789464593617;9798331519773

With the exponential growth of video data, individuals, particularly scholars in the fields of history and sociology, are increasingly reliant on video materials. However, the task of locating specific frames within videos remains a laborious and time-consuming endeavor. Advanced machine learning-assisted video processing techniques have emerged, including text-based video searches, video summarization, real-time object detection, and person re-identification. However, distinct from these, the main challenge of retrieving video frames based on given visual content is how to efficiently and accurately pinpoint the instance occurrences. To expedite the process while maintaining retrieval performance, we propose a two-stage approach, combining KeyFrame Extraction (KFE) and Content-based image Retrieval (CBIR), underpinned a DNN-empowered framework called MoReSo. Our innovations include 1) the integration of improved statistical features with dynamic clustering in the KFE stage and 2) the development of the MoReSo framework, which consists of MobileNet and ResNet backbones with SOA layer to jointly represent video frames, achieving 2.67x increase in efficiency compared to existing solutions. Our framework is evaluated on two datasets: the annotated EHM Historical Database provided by digital history researchers and the widely-used image retrieval benchmark datasets, the Oxford and Paris datasets. The experimental results showcase that the proposed framework and scheme excel among other models in the CBVIR task. We make our code available for further exploration through our GitHub repository. This repository contains the implementation of our model and CBVIR system with a GUI prototype.

关键词： Content-Based video image Retrieval Content-Based image Retrieval Key Frame Extraction image Retrieval from video

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A real-time Demonstrator for image Classification using FPGA-based Logic Neural Networks

A Real-time Demonstrator for Image Classification using FPGA...

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conference on real-time processing of image, Depth, and video Information

作者： Concha, David Garcia-Espinosa, Francisco J. Ramirez, Ivan Alberto Aranda, Luis Univ Rey Juan Carlos CAPO Res Grp Madrid 28933 Spain

ISBN: (纸本)9781510673199;9781510673182

real-time image processing is a key area of focus, but computationally intensive. Neural networks effectively address classification tasks, but they are not always a viable option, particularly in environments where high power consumption or computational requirements are limiting factors. Hardware devices such as Field-Programmable Gate Arrays (FPGAs) offer significant parallelization capabilities that can be fully exploited when the implemented circuit is composed solely of logic gates. In addition, FPGAs are also interesting alternatives to traditional GPU-based implementations in terms of power consumption and reconfiguration capabilities. They can be used as a demonstration platform to validate a hardware design that can be later manufactured, creating the final Application-Specific Integrated Circuit (ASIC). This paper introduces a practical demonstration platform based on an FPGA that highlights the great capabilities of logic neural networks, a type of neural network constructed exclusively with logic gates. By harnessing FPGA parallelization and logic gates, we have achieved a balance between computational power and real-time performance. This approach ensures that image classification occurs at speeds on the order of nanoseconds. This ultra-fast processing is well-suited for real-time image analysis applications across various domains. Industries that rely on quality control, such as manufacturing, will benefit from rapid and precise assessments. In the field of medical image processing, where quick diagnoses are crucial, this technology promises transformative advancements. The demonstration platform developed serves as a proof of concept for logic neural networks, offering a solution to the challenge of real-time image processing and representing the first step towards the implementation of future architectures of logic networks in hardware.

关键词： Deep Learning Field-Programmable Gate Array (FPGA) Logic Neural Network (LNN) real-time image Classification

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real time video Stitching Using Fixed Camera Configuration 8th

Real Time Video Stitching Using Fixed Camera Configuration

引用

8th International conference on Computer Vision and image processing (CVIP)

作者： Singhal, Bhavesh Mazhar, Saquib Bhuyan, M. K. Indian Inst Technol Gauhati Assam India

ISBN: (纸本)9783031581731;9783031581748

video stitching is crucial in multi-camera-based systems that provide 360-degree surveillance and monitoring applications. Homography estimation is the most important step in video/image stitching. All the existing methods mainly focus on homography estimation through traditional keypoint detection or a more recent deep learning approach. These estimation methods are primarily based on multiple homography calculations and fail for featureless images, which lack keypoints, such as the plain sky. To overcome these limitations, we propose a novel real-time video stitching method based on homography estimation through camera calibration using fixed camera configuration. As the proposed method is based on the relative position of two cameras, the overall image/video stitching process is independent of the world scene. In addition, our method calculates the homography matrix only once during the video stitching process. Thus reducing the per-frame stitching time by about 30% compared to multiple homography estimations. As camera calibration information is unavailable with the existing datasets, we record a novel dataset for benchmarking our method.

关键词： video stitching Object detection Homography estimation

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Immersive Hybrid real-time video Communication using Mixed Camera Setups

Immersive Hybrid Real-time Video Communication using Mixed C...

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conference on real-time processing of image, Depth, and video Information

作者： Zepp, M. Chen, D. Hilsmann, A. Eisert, P. Schaefer, R. Prebeck, K. Schreer, O. Feldmann, I Fraunhofer Heinrich Hertz Inst HHI Einsteinufer 37 D-10587 Berlin Germany Fraunhofer Inst Integrated Circuits IIS Am Wolfsmantel 33 D-91058 Erlangen Germany

ISBN: (纸本)9781510673199;9781510673182

With the increase of hybrid meetings, innovative strategies are needed to enable both local and remote participants to interact and communicate in the same way as in real meetings. In the context of a standing table scenario, we present an end-to-end real-time 3D video processing workflow and demonstrator setup. This system offers on a single display two different perspectives of a remote participant for two local participants. The system consists of a 3D capture setup and processing workflow at the remote site and a novel dual-view display on the local site. The standing table scenario requires highly realistic, high resolution rendering quality on the receiving side. Hence, low-resolution depth cameras and neural network-based depth enhancement in conjunction with 8K cameras are used to achieve high quality perspectively corrected views, allowing direct eye contact for individual users. Distinct real-time view rendering is achieved without holes to provide high-quality novel views. On the local site, a novel dual-view display is used that is capable to present two different perspectives of the remote participant at two different positions in space. The display is based on a lenticular lens that is specifically designed for our use case. The prototype setup is optimized towards low latency processing and transmission in order to fulfill the constraints given from our video communication scenario.

关键词： 3D videocommunication real-time eye contact gesture awareness

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Lightweight Stochastic video Prediction via Hybrid Warping

Lightweight Stochastic Video Prediction via Hybrid Warping

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2024 conference on Visual Communications and image processing

作者： Kotoyori, Kazuki Hirose, Shota Sun, Heming Katto, Jiro Waseda Univ Sch Fundamental Sci & Engn Tokyo Japan Yokohama Natl Univ Fac Engn Yokohama Kanagawa Japan

ISBN: (纸本)9798331529543;9798331529550

Accurate video prediction by deep neural networks, especially for dynamic regions, is a challenging task in computer vision for critical applications such as autonomous driving, remote working, and telemedicine. Due to inherent uncertainties, existing prediction models often struggle with the complexity of motion dynamics and occlusions. In this paper, we propose a novel stochastic long-term video prediction model that focuses on dynamic regions by employing a hybrid warping strategy. By integrating frames generated through forward and backward warpings, our approach effectively compensates for the weaknesses of each technique, improving the prediction accuracy and realism of moving regions in videos while also addressing uncertainty by making stochastic predictions that account for various motions. Furthermore, considering real-time predictions, we introduce a MobileNet-based lightweight architecture into our model. Our model, called SVPHW, achieves state-of-the-art performance on two benchmark datasets.

关键词： stochastic video prediction optical flow warping MobileNet lightweight architecture

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