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

共 13657 条记录，以下是11-20 订阅

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Real Time Performance Evaluation of Deep Learning algorithms in image Recognition under the Pytorch Framework

Real Time Performance Evaluation of Deep Learning Algorithms...

引用

2024 International conference on Intelligent algorithms for Computational Intelligence systems, IACIS 2024

作者： Lu, Di Liu, Shengzhi Xinjiang College of Science & Technology Korla China

ISBN: (纸本)9798350360660

With the rapid development of artificial intelligence technology, deep learning has become one of the key technologies in the field of image recognition. PyTorch has become the preferred framework for researchers due to its flexibility, efficiency, and ease of use. This study focuses on the real-time performance of deep learning algorithms in image recognition under the PyTorch framework, and its effectiveness is verified through system experiments. The experiment revealed that the algorithm under this framework exhibits excellent real-time performance in image recognition tasks, with an average frame rate of up to 59.46 FPS and an image processing delay as low as 59.44 milliseconds, fully meeting the demand for efficient processing in a wide range of application scenarios. This discovery demonstrates the powerful potential and practicality of PyTorch in the field of image recognition. © 2024 IEEE.

关键词： Adversarial machine learning

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Liquid Filling Quality Control System for Beverage Industry Using image processing (CNN) 24

Liquid Filling Quality Control System for Beverage Industry ...

引用

24th International conference on Control, Automation and systems

作者： Chatchapol, B. Autanan, W. Anan, W. Rajamangala Univ Technol Lanna Fac Elect Engn Dept Elect Engn Chiang Mai 50300 Thailand Rajamangala Univ Technol Lanna Fac Engn Dept Elect Engn & Automat Control Syst Chiang Mai Thailand Chiang Mai Univ Fac Engn Robot Engn & AI Chiang Mai 50200 Thailand

ISBN: (纸本)9798331517939;9788993215380

The beverage industry has been leveraging automation to enhance production efficiency. An essential advancement involves the use of AI and image processing to ensure the high quality of products. image processing systems employ sophisticated algorithms to meticulously inspect specific aspects of the production process (liquid level, lid sealing, text print). The CNN algorithm is primarily utilized to scrutinize water bottle images to verify their quality. Maintaining consistent production speed poses a challenge, underscoring the importance of examining the image processing system from various perspectives. The research proposed implementing a system that oversees liquid filling quality through an image processing algorithm, a camera module, and conveyor belt speed control to address. This system monitors liquid levels and bottle capping while the production line is operational. On average, the system achieves a 98.89 percent accuracy rate in detecting liquid levels and a 100 percent accuracy rate in sealing the lids. Furthermore, the conveyor speed control can adjust automatically to detect variations in product movement speed.

关键词： Beverage Liquid Filling image processing CNN algorithm

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REMOVING REFLECTivE FLARE IN REAL-WORLD CONDITIONS 31

REMOVING REFLECTIVE FLARE IN REAL-WORLD CONDITIONS

引用

2024 International conference on image processing

作者： Lan, Fengbo Chen, Chang Wen Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9798350349405;9798350349399

The increasing prevalence of mobile devices has led to significant advancements in mobile camera systems and improved image quality. Nonetheless, mobile photography still grapples with flare corruptions such as reflective flare. The absence of a comprehensive real image dataset tailored for mobile phones hinders the development of effective flare mitigation techniques. To address this issue, we present a novel real image dataset specifically designed for mobile camera systems, focusing on flare removal. Capitalizing on the distinct properties of real images, this dataset serves as a solid foundation for developing advanced flare removal algorithms. The dataset comprises over 1,100 pairs of high-quality, full-resolution images for reflective flare, which generate 2,200 paired patches, ensuring broad adaptability across various imaging conditions. Experimental results demonstrate that networks trained with synthesized data struggle to cope with the complex lighting settings present in this real image dataset. Our dataset is expected to enable an array of new research in flare removal and contribute to substantial improvements in mobile image quality, benefiting mobile photographers and end-users alike.

关键词： flare removal reflective flare real image dataset

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Application of Various Novel Computer image processing (CIP) algorithms in Digital Media Art images

Application of Various Novel Computer Image Processing (CIP)...

引用

2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Du, Xian Shanghai Institute of Visual Arts Shanghai201620 China

ISBN: (纸本)9798331509828

With the continuous development of digital image processing algorithms, its application scenarios have been integrated from the simple research of a single image and a single algorithm to a multi-algorithm fusion analysis paradigm. Therefore, this study proposes a digital media art image analysis framework that combines multiple computer image processing (CIP) algorithms. This breakthrough covers three core architectures: image edge extraction, image style transfer, and image compression. In the image edge extraction module, this study designed an improved edge detection algorithm based on de-noising auto-encoder. This algorithm improves the accuracy of edge detection through multi-directional feature extraction and (I, O)-fuzzy rough set optimization, while maintaining global stability. In the image style transfer module, this study proposed a dual-module network. The network includes a texture translation network and a perceptual loss network, and achieves cross-domain style transfer through a fusion model. At the same time, the algorithm optimizes the perceptual loss function to enhance semantic matching capabilities. In the image compression module, this study uses chaotic systems to construct an optimized measurement matrix. Specifically, through a distributed data parallel training framework, a compression method based on the LPAC gradient sparse algorithm is constructed. By testing the three modules separately, the experimental results confirm the effectiveness of the proposed algorithm. © 2025 IEEE.

关键词： image compression

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Impacts of image Compression on the Detection Quality of a Novel Real-Time image processing Platform 26

Impacts of Image Compression on the Detection Quality of a N...

引用

IEEE 26th International conference on Intelligent Transportation systems (ITSC)

作者： Mehrke, Jannik Volk, Georg Stumpp, Yannik Bringmann, Oliver Terzis, Anestis Ulm Univ Appl Sci Inst Commun Technol Ulm Germany Univ Tubingen Dept Comp Engn Tubingen Germany

ISBN: (纸本)9798350399462

Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred data without reducing the quality of the data. When using data compression algorithms, it is important to validate the impact of these algorithms on the detection quality. This work evaluates the effects of image compression and transmission over wireless interfaces on state of the art neural networks. Therefore, a modern image processing platform for next generation automotive processing architectures, as used in software defined vehicles, is introduced. The impacts of different image encoders as well as data transmission parameters are investigated and discussed.

关键词： image compression

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A Compact Real-Time Thermal Imaging System Based on Heterogeneous System-on-Chip 30

A Compact Real-Time Thermal Imaging System Based on Heteroge...

引用

30th IEEE International conference on Embedded and Real-Time Computing systems and Applications (RTCSA)

作者： Oh, Hyun Woo Choi, Cheol-Ho Cha, Jeong Woo Choi, Hyunmin Shin, Jung-Ho Han, Joon Hwan Hanwha Syst Infra Technol R&D Ctr Seongnam South Korea

ISBN: (纸本)9798350387964;9798350387957

This paper presents a real-time embedded thermal imaging system architecture for compact, energy-efficient, high-quality imaging utilizing heterogeneous system-on-chip (SoC) and uncooled infrared focal plane arrays (IRFPAs). Unlike previous systems that organized separate devices for complex image processing, our system provides integrated image processing support for robust sensor-to-surveillance. The image processing organizes two algorithm stacks: a non-uniformity correction stack to mitigate the distinctive noise vulnerabilities of uncooled IRFPAs, and an image enhancement stack including contrast enhancement and temporal noise filters. We optimized these algorithms for domain-specific factors, including asymmetric multiprocessing (AMP), cache organization, single instruction multiple data (SIMD) instructions, and very long instruction word (VLIW) architectures. The implementation on the TI TDA3x SoC demonstrates that our system can process 640x480, 60 frames per second (FPS) videos at a peak core load of 57.5% while consuming power less than 2.2 W for the entire system, denoting the possibility of processing the 1280x1024, 30 FPS videos from the cutting-edge uncooled IRFPAs. Additionally, our system improves power efficiency by 9.42% and 9.96% at 30 and 60 FPS, respectively, compared to the state-of-the-art when executing similar image processing algorithms.

关键词： Thermal Imaging image processing Heterogeneous Computing SIMD Real-Time Embedded systems

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Advancing Industry 4.0 with Real-Time Machine Vision Integration in Cyber-Physical systems 3

Advancing Industry 4.0 with Real-Time Machine Vision Integra...

引用

3rd International conference on Computing and Machine Intelligence (ICMI)

作者： Hamza, Syed Amir Jesser, Alexander Heilbronn Univ HHN Inst Intelligent Cyber Phys Syst ICPS Heilbronn Germany

ISBN: (纸本)9798350372977;9798350372984

This study introduces a machine vision system integrated into cyber-physical systems (CPS) for enhanced industrial control. The system employs a specialized script for real-time image processing and edge detection, with a focus on precision and speed. Results showcase the system's rapid processing capabilities and high-accuracy feature detection, facilitated by machine learning algorithms that enable adaptability and iterative improvement. The system distinguishes itself by not only providing rapid and accurate feature recognition but also by outputting precise coordinates, crucial for micron-level manufacturing precision. An intuitive human-machine interface ensures seamless operation within industrial workflows. This integration significantly improves automated quality control and operational efficiency, demonstrating the system's potential to advance smart manufacturing in line with Industry 4.0 standards.

关键词： Cyber-Physical systems (CPS) Machine Vision systems Real-Time image processing Edge Detection Smart Manufacturing Industry 4.0 Automated Quality Control Machine Learning algorithms Precision Manufacturing

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Enhanced K-means Color Clustering Based on SLIC Superpixels Merging incorporated within the Entomology Software: AInsectID

Enhanced K-means Color Clustering Based on SLIC Superpixels ...

引用

IEEE conference on Evolving and Adaptive Intelligent systems (IEEE EAIS)

作者： Sadia, Haleema Alam, Parvez Univ Edinburgh Sch Engn Edinburgh Scotland

ISBN: (纸本)9798350366235;9798350366242

Superpixel-based segmentation is an important preprocessing step for the simplification of image processing. The subjective nature behind the determination of optimal cluster numbers in segmentation algorithms can result in either under-or over-segmentation burdens, depending on the image type. Insect wings, with their intricate color patterns, pose significant challenges for the accurate capture of color diversity in clustering algorithms, assuming a spherical and isotropic cluster distribution is used. This paper introduces a hybrid approach for color clustering in insect wings, integrating the Simple Linear Iterative Clustering (SLIC) method to generate the initial superpixels, and a DeltaE 2000 function the precisely discriminated merging of superpixels. Color differences between superpixels serve to measure homogeneity during the merging process. The proposed new algorithm demonstrates enhanced segmentation as it overcomes the issue of over-segmentation and under-segmentation, as evidenced by the results derived from the Boundary Recall, Rand index, Under-segmentation Error, and Bhattacharyya distance using ground truth data. The Silhouette score and Dunn Index are also used to quantitatively evaluate the efficacy of our new proposed clustering technique.

关键词： image processing Superpixels SLIC Color Merging K-means Clustering

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image processing Method Based on MATLAB in the Application of Belt Tracking with Industrial Robot 13th

Image Processing Method Based on MATLAB in the Application o...

引用

13th International conference on Computer Engineering and Networks (CENet)

作者： Tang Youan Zhao Lingyan Chen Gandong Shandong Jiaotong Univ Sch Construct Machinery Jinan Peoples R China

ISBN: (纸本)9789819992454;9789819992430;9789819992423

At present, the application of industrial robots combined with visual systems to achieve dynamic grasping materials of the belt is becoming increasingly widespread. Unlike the behavior of industrial robots grabbing after the belt stops, industrial robots dynamically tracking and grabbing materials on the belt can greatly improve production efficiency. For vision, processing distorted material images during high-speed movement and to obtain accurate coordinate points of materials is a key task to improve the accuracy of the belt tracking applications with industrial robot. Developing image processing algorithms based on MATLAB can, on the one hand, utilize existing software and hardware interface functions to improve development efficiency;On the other hand, autonomous and controllable image processing algorithms can be developed based on application requirements to maximize system accuracy.

关键词： Belt Dynamical tracking Industrial robots image processing

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Design Of Convolution Neural Network For Crack Detection 7

Design Of Convolution Neural Network For Crack Detection

引用

7th International conference on Devices, Circuits and systems (ICDCS)

作者： Malathi, D. Gautham, S. Dineshkumar, M. Balakrishnan, K. Kongu Engn Coll Dept Elect & Commun Engn Erode India

ISBN: (纸本)9798350350470;9798350350487

Crack is an important factor to consider when assessing the quality of concrete structures since it impacts the structure's longevity, application, and safety. Convolutional neural networks are increasingly the best option to replace manual crack detection because of the advancement of methods for deep learning. Machine learning algorithms known as artificial neural networks (ANNs) imitate how the human brain functions. These Neural Networks can be implemented in software. However, these neural networks require large computations. Hardware implementation of these neural networks has higher processing speeds than their software implementations. CNN is a particular kind of artificial neural network that is used to interpret pixel data and is utilised in image detection and processing. Computer Vision applications including object identification, image segmentation, and image classification work well with convolutional neural networks. employed for categorization The proposed method uses a configurable convolution neural network system for crack detection. An accuracy of 97.5% is achieved over 200 images. By detecting the crack effectively using the method, the quality of the concrete structures will be ensured using dedicated hardware shortly.

关键词： crack detection deep learning convolution neural network image segmentation CNN system image processing image classification

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