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

共 15550 条记录，以下是51-60 订阅

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Real-Time Data processing for Embedded Communicating systems: a Hardware/Software Co-Design Approach 31

Real-Time Data Processing for Embedded Communicating Systems...

引用

31st conference on Electronics Circuits and systems

作者： Saussereau, Jonathan Jego, Christophe Leroux, Camille Begueret, Jean-Baptiste Univ Bordeaux Bordeaux INP CNRS UMR 5218Lab IMS Bordeaux France

ISBN: (纸本)9798350377217;9798350377200

Embedded systems typically require the transmission of significant amounts of data to small-scale CPUs for applications such as radar signal processing, image processing, and embedded AI. Ensuring data integrity during transmission is typically managed using Cyclic Redundancy Check (CRC) algorithms. However, achieving real-time CRC calculation and data storage poses challenges, often necessitating large FIFO memories and multiple clock domains. These additional resources involve a greater hardware complexity. This paper presents an approach aimed at synchronizing the CPU frequency with data transmission. This enables having a single clock domain and a reduction of power consumption. Using hardware/software co-design, it is possible to achieve real-time data storage and CRC calculation without data loss and with a low power consumption.

关键词： Real-Time Hardware Software Co-Design CRC Embedded systems CPU RISC-V Low-Power SPI

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FPGA Implementation of Various Division algorithms for image processing Applications - A Comparative Analysis

FPGA Implementation of Various Division Algorithms for Image...

引用

2024 International conference on Signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Jayanthi, B. Kumar, L.S. Someshwaran, A. Sandya, M. Bharadwaj, Nandhini National Institute of Technology Puducherry Department of ECE Karaikal India

ISBN: (纸本)9798331540685

Division is one of the most commonly sort after algorithm for performing image processing operations such as normalization, filtering, enhancement, deconvolution etc. Hence, the design of efficient division algorithm is highly essential in order to obtain a better image processing algorithm The first and foremost step in the implementation of any algorithm in hardware is to identify the number systems used to represent the inputs and outputs. The inputs, outputs and complexity of the algorithm varies based on the number system used. This paper proposes a recursive subtraction based fixed-point division algorithm. This work also provides a comparative analysis on various aspects such as area, power, delay and throughput, on comparing the proposed recursive subtraction based fixed-point division algorithm with the existing floating-point division, restoring integer division and non-restoring integer division algorithms and operator based fixed-point division algorithm. © 2024 IEEE.

关键词： image enhancement

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Medical image Segmentation Techniques: Advances and Challenges 6

Medical Image Segmentation Techniques: Advances and Challeng...

引用

6th IEEE International conference on image processing, Applications and systems, IPAS 2025

作者： Boulehmi, Hela Filali, Rania Linda DGET ISET de Bizerte Bizerte Tunisia

ISBN: (纸本)9798331506520

Medical image segmentation plays a pivotal role in computer-aided diagnosis by facilitating the extraction of essential features necessary for disease detection and treatment strategies. The continuous progress in image processing technologies has led to the development of numerous segmentation methods, encompassing traditional algorithms, machine learning (ML)-driven approaches, and cutting-edge deep learning (DL) techniques. This study undertakes a comparative evaluation of these methods, focusing on their efficiency, accuracy, and suitability across different medical imaging modalities. It also delves into prominent segmentation techniques like thresholding, region-based methods, edge detection, graph cuts, active contour models, and convolutional neural networks (CNNs). Additionally, the paper explores ongoing challenges and prospective advancements aimed at enhancing segmentation efficacy in medical imaging. © 2025 IEEE.

关键词： Convolutional neural networks

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A Novel visual Communication Scenario Based on image processing algorithms 2

A Novel Visual Communication Scenario Based on Image Process...

引用

2nd International conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Yang, Jinhua Hou, Li Xijing University Shaanxi Xi'an710123 China

ISBN: (纸本)9798331505745

In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on image processing algorithms. The framework aims to optimize traditional key technologies such as the image generation, editing, style transfer and image compression. First, as the foundation of visual communication, this study proposes a generative adversarial network model based on text semantic information for image generation and editing. The model achieves stable image generation and efficient editing from a theoretical level through paired training of text and image pairs. Secondly, for image style transfer, this study designed an improved VGG19 convolutional neural network. At the same time, the adaptive instance normalization technology was combined to optimize the effect of style transfer. Finally, in terms of image compression, the study proposed an improved generative adversarial network (REviSED-GAN) model. This model can dynamically adjust the compression error based on structured information to improve image compression efficiency. Through comparative tests, the proposed image style transfer and image compression algorithms have shown excellent performance in terms of structural similarity, image quality and compression ratio. © 2025 IEEE.

关键词： visual communication

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Scheduling algorithms on Heterogeneous Architectures for Efficient vision systems 47

Scheduling Algorithms on Heterogeneous Architectures for Eff...

引用

conference on Applications of Digital image processing XLviI

作者： Ali, Teymoor Paul, George Nicol, Robert Bhowmik, Deepayan Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England STMicroelectronics 1 Tanfield Edinburgh Midlothian Scotland

ISBN: (纸本)9781510679344;9781510679351

Deep Neural Network (DNN) algorithms have become ubiquitous within the vision domain, encompassing various tasks, including object detection, segmentation, and classification. However, executing complex DNNs in real-time systems demands improved energy efficiency, runtime, and accuracy. Traditional embedded imaging designs, typically implemented on homogeneous architectures, face hardware limitations, prompting the need for heterogeneous computing architectures. These architectures combine CPUs, GPUs, FPGAs, and other accelerators, enabling applications to use the most efficient architecture for a given algorithm. The challenge lies in scheduling and partitioning algorithms across accelerators with different computing paradigms and tool-sets. This requires balancing computational power, memory bandwidth, and communication overhead. Effective scheduling involves considering task dependencies, resource availability, and synchronisation. Current deep learning libraries often target single architectures and lack mechanisms to intelligently partition sub-operations across the most suitable processors. This paper introduces a scheduler for heterogeneous vision systems that finely partitions and maps sub-operations of convolutional neural networks and image processing algorithms. Leveraging state-of-the-art compiler frameworks such as PyTorch, TVM, and ONNX, the proposed scheduler optimally distributes tasks across heterogeneous components. Experimental results show that the heterogeneous platform achieves on average 1.12x & 1.08x improvements in kernel runtime and energy consumption, compared to the best-performing discrete hardware counterparts, GPU and FPGA. The study demonstrates that partitioning algorithms based on their runtime and energy properties and optimally scheduling them improves energy and runtime efficiency compared to homogeneous components executing the complete algorithm.

关键词： Heterogeneous Computing Computer vision FPGA-GPU Heterogeneous Scheduler PyTorch TVM ONNX

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An Investigation on Classical Methods and Unsupervised Deep Learning algorithms for image Segmentation in High Variance images for Industrial Application 35

An Investigation on Classical Methods and Unsupervised Deep ...

引用

35th Irish systems and Signals conference, ISSC 2024

作者： Henry, Nathan Reiter, Tamas

ISBN: (纸本)9798350352986

This paper investigates classical image processing techniques and unsupervised deep learning algorithms for segmenting images with high variance for an under researched industrial problem, focusing on beam burns generated by Focused Ion Beam (FIB) technology. Classical methods (CM) include preprocessing techniques such as edge detection, while Deep Learning (DL) employs Convolutional Neural Networks (CNNs) for unsupervised image segmentation. Experimental results demonstrate the superior accuracy of DL, but with increased computational overhead, in contrast to the efficiency and controllability of CM. Combining both approaches offers a potential solution for optimizing accuracy and efficiency in image segmentation. © 2024 IEEE.

关键词： image segmentation

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Classification of Red Watermelon Varieties Using Canny Edge Detection and CNN

Classification of Red Watermelon Varieties Using Canny Edge ...

引用

IEEE International conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Martinez, Milca Rejaiah B. Dayrit, Kian Matthew D. Yumang, Analyn N. Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

ISBN: (纸本)9798350372113;9798350372106

Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most sought-after fruits globally. With the watermelon fruit consisting of different varieties, consumers usually have difficulty in classifying watermelons due to their similar external appearances, especially when labeled under the same name. This study is conducted to implement a system that detects and classifies three red watermelon varieties, Red Export, Orchid Sweet, and Dixie Queen, with image processing algorithms. The researchers utilized Canny Edge Detection for the dataset's preprocessing phase and Convolutional Neural Network (CNN) for its classification. The Raspberry Pi is also applied to this study. Moreover, the researchers created and collected their datasets for testing and validation data. The model used in this study has acquired 84.71% overall accuracy.

关键词： Watermelon Classification image Classification Canny Edge Detection CNN image processing Raspberry Pi

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The application of deep learning algorithms in power system load forecasting 6

The application of deep learning algorithms in power system ...

引用

6th International conference on Wireless Communications and Smart Grid, ICWCSG 2024

作者： Wang, Shilin Cui, Wei Yang, Yang Meng, Xiangliang State Grid Information & Telecommunication Group Co. Ltd. No. 10 Xuanwumen Outer Street Xicheng District 95555-0345 Beijing100029 China

ISBN: (纸本)9781510682962

This article explores the application of deep learning (DL) algorithms in power system load forecasting. With the continuous advancement of the construction of new power systems, traditional load forecasting models designed based on time series analysis are no longer in line with the actual needs of the current power system. To improve the accuracy of load forecasting, this paper attempts to transform time series data into images for analysis, and then use deep learning methods widely used in the field of image processing for power load forecasting. Convolutional neural network (CNN), as a commonly used image processing algorithm, has been applied in time series data processing. However, data is still processed as a sequence matrix, and the advantages of CNN algorithm in processing image matrices have not been fully utilized. Therefore, this article proposes a CNN algorithm based on sequence to image conversion (STI-CNN). Firstly, the load sequence is converted into a load image. Then, a dual branch deep network model is used to accurately cluster the input data. Finally, the STI-CNN model is used for load prediction. Matlab simulation experiments show that the proposed STI-CNN model has excellent performance in different prediction metrics and has higher prediction accuracy. © 2024 SPIE.

关键词： Data modeling Data conversion image processing systems modeling Convolutional neural networks Evolutionary algorithms Statistical modeling Education and training Support vector machines

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Identification of a Person Based on Block processing of a Signature image

Identification of a Person Based on Block Processing of a Si...

引用

2024 International Russian Smart Industry conference, SmartIndustryCon 2024

作者： Rizaev, Ildus Takhavova, Elza Shleimovich, Mikhail Kazan National Research Technical University Named after A.N. Tupolev-KAI Automated Systems for Information Processing and Control Department Kazan Russia

ISBN: (纸本)9798350395044

The development of computer vision systems stimulates the development of various applications in the field of image recognition. Methods and algorithms for image recognition in document processing systems play a crucial role. There is a lot of research in this direction, and one of the most difficult problems is handwritten text recognition. This includes the tasks of signature identification, which the authors of the paper consider. The article considers the main stages of signature analysis to establish authorship. They are the preprocessing of the signature, representation in a form convenient for applying author identification. A dataset containing sample signatures of various people is used. The described tools became means to develop software for preprocessing images of signatures, extracting characteristics and identifying the author of the signature. © 2024 IEEE.

关键词： Textures

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The Disease Prediction System based on Symptom Identification and image processing

The Disease Prediction System based on Symptom Identificatio...

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2024 International conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Swathi, M. Prithika, N.G. Sri Aishwariya, N. Jayaram, B. Dept.of.Computer Science And Business Systems Rmk Engineering College Thiruvallur India Dept.of.Computer Science And Business Systems Rmk Engineering College Thiruvallur India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366846

This paper aims to investigate a system that uses various machine-learning algorithms to predict symptoms and deep-learning techniques for image processing that leads to early disease prediction, an essential aspect of today's healthcare industry. The main objective of this system is to enable both the healthcare professionals and the patients to interpret the results at a faster rate. Existing systems provide results based on only symptoms which is not very effective in real-time but our system works well with both symptoms and image-based specifications. This investigation explores several algorithms like Random Forest, Decision tree, XGBoost, Logistic regression, Long-term short memory (LSTM), and Naïve Bayes for symptom-based prediction [14]. CNN (Convolutional Neural Network) deep learning techniques, notably DenseNet121, are used for image processing. The use of image processing bridges the gap between the user who feels symptom-based prediction to be a bit difficult. Furthermore, diverse experiments are done on multiple datasets to establish more accurate results. This system also ensures the safeguarding of sensitive information. The proposed disease prediction system helps empower healthcare professionals and individuals with proactive insights into potential health risks by giving accurate predictions for early intervention of the disease. © 2024 IEEE.

关键词： Forecasting

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