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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

共 14633 条记录，以下是331-340 订阅

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Split Computing based Quantized PoseNet Model for Distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th International conference on Communication systems and Networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling image edge detection Computer architecture Bandwidth Inference algorithms Partitioning algorithms

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Research on Traditional Art image Reconstruction Method Based on Computer Vision Analysis 2

Research on Traditional Art Image Reconstruction Method Base...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Supriya, M. Anvesh, Kuncham Muruganandham, B. Manasa Krishnan, K. Muthu, R. Murugan, K. Kpr Institute of Engineering and Technology Department of Ece India

ISBN: (纸本)9798350369960

In the framework of the digital era, the technology of image processing is one of the technologies that is being used increasingly often in all aspects of modern life. image correction may be handled using algorithms based on computer vision, which can increase the correction's influence on the image as a whole. The conventional method of identifying structural systems collects data via the use of wired or wireless measuring systems. The process of establishing sensors and the equipment used for data collection is laborious and takes a lot of time. The process has two stages: the first is the extraction of the line drawing, and the second is the rendering of the line drawing. An edge detection approach that integrates low-level and middle-level visual information is used to extract the line drawing in the process of on-line drawing extraction. This method is used to extract the drawing. The edges are then simplified and linked in accordance with the computer vision theory. Additionally, the image processing technology is used as the research object in order to develop a scientific model for the distorted image. These steps are based on the findings of traditional edge detection. image restoration can be helped along by filtering samples of the image and getting rid of any noise that is there. The fundamentals of computer vision and the technologies that are connected to it are broken down in detail, and the behavioral patterns of visual interaction are investigated. The image of Chinese traditional art serves both the role of art communication and as a medium and symbol for cultural communication. At the same time, it carries on the legacy of a fine traditional culture that has been passed down from generation to generation in China. The image processing technology that is based on computer vision algorithms provides a bigger number of benefits, in addition to a better degree of accuracy. © 2023 IEEE.

关键词： Computer Vision feedback image reconstruction Traditional art Visual analysis

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Modular Sumcheck Proofs with Applications to Machine Learning and image processing 23

Modular Sumcheck Proofs with Applications to Machine Learnin...

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30th ACM SIGSAC conference on Computer and Communications Security (ACM CCS)

作者： Balbas, David Fiore, Dario Gonzalez Vasco, Maria Isabel Robissout, Damien Soriente, Claudio IMDEA Software Inst Madrid Spain Univ Politecn Madrid Madrid Spain Univ Carlos III Madrid Madrid Spain NEC Labs Europe Madrid Spain

ISBN: (纸本)9798400700507

Cryptographic proof systems provide integrity, fairness, and privacy in applications that outsource data processing tasks. However, general-purpose proof systems do not scale well to large inputs. At the same time, ad-hoc solutions for concrete applications-e.g., machine learning or image processing-are more efficient but lack modularity, hence they are hard to extend or to compose with other tools of a data-processing pipeline. In this paper, we combine the performance of tailored solutions with the versatility of general-purpose proof systems. We do so by introducing a modular framework for verifiable computation of sequential operations. The main tool of our framework is a new information-theoretic primitive called Verifiable Evaluation Scheme on Fingerprinted Data (VE) that captures the properties of diverse sumcheck-based interactive proofs, including the well-established GKR protocol. Thus, we show how to compose VEs for specific functions to obtain verifiability of a data-processing pipeline. We propose a novel VE for convolution operations that can handle multiple input-output channels and batching, and we use it in our framework to build proofs for (convolutional) neural networks and image processing. We realize a prototype implementation of our proof systems, and show that we achieve up to 5x faster proving time and 10x shorter proofs compared to the state-of-the-art, in addition to asymptotic improvements.

关键词： Proof systems Verifiable Computation Zero-Knowledge Proofs Machine Learning Convolutional Neural Networks image processing

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image Based Classification of Methods-Time Measurement Operations in Assembly Using Recurrent Neuronal Networks 6th

Image Based Classification of Methods-Time Measurement Opera...

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6th International conference on System-Integrated Intelligence (SysInt)

作者： Rueckert, Patrick Birgy, Katrin Tracht, Kirsten Univ Bremen Bremen Inst Mech Engn BIME Badgasteiner Str 1 D-28359 Bremen Germany

ISBN: (纸本)9783031162817;9783031162800

image based classification enables the acquisition and transfer of data from manual assembly workstations into a digital environment. Based on the Methods-Time Measurement method, assembly processes are transformed into short, discrete basic operations that are recognised by means of image processing and used as input data for a multilayer neural network. A recurrent neural network algorithm is investigated for its applicability in combination with the sensor data. The five basic MTM operations reaching, grasping, bringing, releasing, and positioning are classified and additional influencing factors, as well as the implementation of an object recognition, are investigated. The following paper addresses the question of the extent to which manual assembly processes can be reliably derived from visual sensor data and classified by machine learning algorithms.

关键词： Neuronal networks Assembly image processing

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Digital image processing for the Diagnosis of the Cold Inducible Urticaria by Photoplethysmography-Based Methods

Digital image Processing for the Diagnosis of the Cold Induc...

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2023 Wave Electronics and its Application in Information and Telecommunication systems, WECONF 2023

作者： Guryleva, Anastasia Fomina, Daria Volkov, Mikhail Serdoteckova, Sofia Danilycheva, Inna Russian Academy of Sciences Acousto-Optic Spectroscopy Lab Scientific and Technological Center of Unique Instrumentation Moscow Russia Moscow State Scientific and Practical Center for Allergology and Immunology City Clinical Hospital No 52 Ministry of Healthcare of Moscow Russia Sechenov University Department of Clinical Immunology and Allergology Moscow Russia ITMO University Faculty of Physics and Technology Saint-Petersburg Russia NRC Institute of Immunology FMBA of Russia Moscow Russia

ISBN: (纸本)9798350348293

The objectivity and accuracy of the diagnosis of the inducible urticaria can be improved through the analysis of microcirculation parameters. Photoplethysmography is an easily implemented non-invasive method able to collect valuable diagnostic data on the state and function of the vessels. Existing descriptions of photoplethysmography image processing algorithms are limited to the basic principles and general mathematical expressions. At the same time, the algorithm options can vary significantly and affect the data quality. In this paper, we study the optimal characteristics of the algorithms for photoplethysmography-based skin inspection for the diagnosis of the cold inducible urticaria. Presented approach provides automatic, fast and repeatable processing under various input data. © 2023 IEEE.

关键词： image processing

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Building Structures image Matching Using Convolutional Neural Network

Building Structures Image Matching Using Convolutional Neura...

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2024 systems of Signals Generating and processing in the Field of on Board Communications, SOSG 2024

作者： Andriyanov, N.A. Magdeev, R.G. Dementyiev, V.E. Financial University under the Government of the Russian Federation Moscow Russia Ulyanovsk State Technical University Ulyanovsk Russia National Research Mordovia State University Saransk Russia

ISBN: (纸本)9798350383614

The paper examines the features of combining multi-temporal and multi-angle images of building structures in order to identify critical changes. It is proposed to carry out such a combination on the basis of high-speed pseudogradient algorithms. The focus of the work is the problem of identifying areas of attention that are the source of combined points. It is proposed to perform such selection using convolutional neural networks of the YOLO family. The consistency of this approach is shown and estimates of the resulting gains are also calculated. It is also suggested to use different optimization methods for improving the processing speed. So the YOLO detector will be very high performance and efficient real time detector. ©2024 IEEE.

关键词： Object detection

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Language Is Not All You Need: Aligning Perception with Language Models 37

Language Is Not All You Need: Aligning Perception with Langu...

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37th conference on Neural Information processing systems (NeurIPS)

作者： Huang, Shaohan Dong, Li Wang, Wenhui Hao, Yaru Singhal, Saksham Ma, Shuming Lv, Tengchao Cui, Lei Mohammed, Owais Khan Patra, Barun Liu, Qiang Aggarwal, Kriti Chi, Zewen Bjorck, Johan Chaudhary, Vishrav Som, Subhojit Song, Xia Wei, Furu Microsoft Redmond WA 98052 USA

ISBN: (纸本)9781713899921

A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence. In this work, we introduce KOSMOS-1, a Multimodal Large Language Model (MLLM) that can perceive general modalities, learn in context (i.e., few-shot), and follow instructions (i.e., zero-shot). Specifically, we train KOSMOS- 1 from scratch on web-scale multimodal corpora, including arbitrarily interleaved text and images, image-caption pairs, and text data. We evaluate various settings, including zero-shot, few-shot, and multimodal chain-of-thought prompting, on a wide range of tasks without any gradient updates or finetuning. Experimental results show that KOSMOS- 1 achieves impressive performance on (i) language understanding, generation, and even OCR-free NLP (directly fed with document images), (ii) perception-language tasks, including multimodal dialogue, image captioning, visual question answering, and (iii) vision tasks, such as image recognition with descriptions (specifying classification via text instructions). We also show that MLLMs can benefit from cross-modal transfer, i.e., transfer knowledge from language to multimodal, and from multimodal to language. In addition, we introduce a dataset of Raven IQ test, which diagnoses the nonverbal reasoning capability of MLLMs.

关键词： image recognition

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SuperTA-Net: A Supervised Transmission-Augmented Network for image Compressive Sensing Reconstruction 6

SuperTA-Net: A Supervised Transmission-Augmented Network for...

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6th International conference on Data-Driven Optimization of Complex systems (DOCS)

作者： Zhang, Zhijie Bai, Huang Stankovic, Ljubisa Sun, Junmei Li, Xiumei Hangzhou Normal Univ Sch Informat Sci & Technol Hangzhou Peoples R China Univ Montenegro Fac Elect Engn Podgorica Montenegro

ISBN: (纸本)9798350377859;9798350377842

Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression procedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iterative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission-augmented network (SuperTA-Net) is proposed. Based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through experiments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.

关键词： Compressive sensing deep unfolding network multi-channel transmission attention based supervision alternating optimization

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Impact of Histological processing on the Polarimetric Properties of Healthy and Neoplastic Brain Tissue 3

Impact of Histological Processing on the Polarimetric Proper...

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conference on Tissue Optics and Photonics iii

作者： Gros, Romane Rodriguez-Nunez, Omar Felger, Leonard Moriconi, Stefano McKinley, Richard Pierangelo, Angelo Novikova, Tatiana Vassella, Erik Schucht, Philippe Hewer, Ekkehard Maragkou, Theoni Univ Bern Inst Tissue Med & Pathol CH-3010 Bern Switzerland Univ Bern Grad Sch Cellular & Biomed Sci CH-3010 Bern Switzerland Univ Bern Bern Univ Hosp Dept Neurosurg CH-3010 Bern Switzerland Univ Bern Bern Univ Hosp Inselspital Univ Inst Diagnost & Intervent RadiolSCAN CH-3010 Bern Switzerland Ecole polytech CNRS LPICM IP Paris F-91128 Palaiseau France Lausanne Univ Hosp Inst Pathol CH-1011 Lausanne Switzerland Univ Lausanne CH-1011 Lausanne Switzerland

ISBN: (纸本)9781510673380;9781510673397

Neurosurgical treatment is the primary approach for brain cancer, particularly gliomas, posing challenges due to their invasiveness and the imperative to maintain neurological function. Precise delineation of tumor margins becomes crucial to prevent neurological deficits and improve prognosis in neuro-oncological surgery. Intraoperative tumor border visualisation during neurosurgery finds a promising solution in imaging Mueller polarimetry. The development of tumor segmentation algorithms using polarimetric data requires a large and curated database of polarimetric measurement associated with the co-registered ground truth. We developed a neuropathology protocol to gather both histological and polarimetric data. Moreover, we implemented an image processing pipeline to obtain a precise mapping between histological and polarimetric data, allowing histological data to serve as a reliable ground truth for tissue characterisation. However, the histological processing steps, such as the freezing, cryosectioning and thawing of the samples, might alter the tissue microstructure and the polarimetric parameters of brain tissue. In this study, we extend the description of the neuropathology protocol by analysing the effect of the histological processing steps on the polarimetric properties of fresh thick brain specimens. We evaluated and compared polarimetric properties of fresh healthy and neoplastic brain tissue before and after applying the histological processing steps. We found a moderate effect of the latter on the polarimetric properties of both brain tissue types. The contrast in polarimetric parameters observed between different brain tissue types is conserved, as well as the ability to perform fiber tracking. Thus, the protocol facilitates a database of co-registered histological and polarimetric data.

关键词： Mueller Polarimetry Brain Tumors Neuropathology image processing Neuro-Oncology

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Utilising Machine Learning for Modulation and Signal processing 2

Utilising Machine Learning for Modulation and Signal Process...

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2nd IEEE World conference on Communication and Computing (WCONF)

作者： Singh, Daljeet Pal Solanki, Madhav Singh Jyothi, Renuka S. Maharishi Univ Informat Technol Maharishi Sch Engn & Technol Uttar Pradesh India Sanskriti Univ Dept Comp Sci & Engn Uttar Pradesh India Jain Deemed Be Univ Dept Biotechnol & Genet Sch Sci Bangalore Karnataka India

ISBN: (纸本)9798350395334;9798350395327

Device studying (ML) is an effective device that has been used in many packages, consisting of virtual sign processing, communications structures, and modulation reputation. In particular, ML has been extensively studied for modulation recognition, which is a hard hassle because of the always-changing and unpredictable nature of the sign. With the aid of leveraging ML strategies, which can be used to quantify and check specific houses of a signal, reliable modulation popularity and signal processing strategies can be evolved. For instance, ML can be used to automatically discover modulation class when the image shape of the signal is unknown. It is achieved via reading the sign with device learning techniques consisting of Fourier transform;waveletbased totally function extraction, and convolutional neural networks. Different applications encompass channel estimation, signal detection, and optimization of verbal exchange networks. Further to recognizing modulation codecs, ML-based algorithms may be used to enhance signal processing and communique systems. It could be done with the aid of developing smarter systems that are capable of studying global data. ML can be used to robotically apprehend anomalous alerts and allow networks to quickly adapt to converting situations. ML techniques can also be used to optimize community parameters, which include power management, coding schemes, and modulation codecs, so as to maximize their performance.

关键词： Consisting Communications Modulation Techniques Parameters Management

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