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检索条件"任意字段=Conference on Applications of Digital Image Processing XIX"

共 10226 条记录，以下是11-20 订阅

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Optimizing Fabric Welding Using image Processed Laser Inspection 5th

Optimizing Fabric Welding Using Image Processed Laser Inspec...

引用

5th International conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Ansari, Tabish Sekhar, Malepati Chandra Alam, Intekhab Bargavi, Manju Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Material welding is used in expanding industrial and industrial applications and depends on accuracy and precision to ensure satisfaction and protection. Laser inspection technology has been developed to help enhance the procedure's accuracy and performance by presenting rapid, reliable imaging that may be processed to decide where changes are wished. Through photograph processing, particular modifications to cloth welding can be recognized quickly and effectively. Additionally, photograph-processed laser inspection can hit upon any irregularities or defects in the welding method that can cause premature failure or decreased functionality. With this generation, designers and manufacturers can also develop higher control structures for his or her tasks, making more accurate and price-effective use of their assets. Sooner or later, the laser inspection era can offer actual-time remarks for automatic tactics, allowing changes to be made quickly with minimal disruption. Using this image processing technology, fabric welding groups can improve exceptional accuracy and value across merchandise. Optimizing material welding through image-processed laser inspection is a valuable method for controlling the pleasantness of welds. This technique uses a laser scanner, mild source, and a digital camera to seize detailed photos of the welds before and after welding. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inspection

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Hardware-Aware Depth image Classification Model for In-Sensor AI Integration

Hardware-Aware Depth Image Classification Model for In-Sens...

引用

International conference on applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2024

作者： Fasolino, Andrea Licciardo, Gian Domenico Vitolo, Paola Liguori, Rosalba Di Benedetto, Luigi Rubino, Alfredo Pau, Danilo Department of Industrial Engineering University of Salerno Fisciano Salerno84084 Italy System Research and Applications STMicroelectronics Agrate Brianza20041 Italy

ISBN: (纸本)9783031840999

The shift of computing capabilities towards edge sensing elements in image classification tasks is leading to the substitution of cameras in some industrial and consumer tasks with ultra-low-resolution (ULR) Time-of-Flight (ToF) sensors, thanks to their compactness and ultra-low power consumption. To effectively integrate classification capabilities into edge devices, a hardware-aware design of the classification algorithm and a careful custom Hardware (HW) post-processing core are needed. This work proposes a new compact and ultra-low power Neural Network (NN) that enables the implementation of a HW post-processing core for real-time classification tasks inside the sensor packaging. The resulting HW architecture has been prototyped on an AMD Xilinx Artix-7 FPGA, reaching an Energy per Inference consumption of 65.6 nJ and a power consumption of 1.095 μW at the maximum Output Data Rate of the sensor. Moreover, the implementation in Skywater 130 nm technology reveals an area occupation below 1 mm2, with a total power compared to the sensor consumption of about 11%. These results are significantly lower than the typical energy and power consumption of the sensor, enabling real-time post-processing of depth images and encouraging research in integrating classification capabilities next to sensing elements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： digital storage

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Conceptualisation of a Parameterizable Low-Pass Filter for Resolving Measurement Data 6

Conceptualisation of a Parameterizable Low-Pass Filter for R...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Schmid, Michael Eisenmann, Bastian Aksu, Osman Radosavac, Misel Bierwirth, Florian Herzog, Hans-Georg Concepts Power Supplies BMW AG Department of Functional Safety Munich Germany Low Voltage Distributors BMW AG Department of Active Munich Germany Associate Professorship of Energy Conversion Technology Munich Germany

ISBN: (纸本)9798331506520

The data acquisition process through measurements translates continuous physical quantities, such as temperature or magnetic flux, into discrete digital data sets. Different noise sources, such as quantization or Gaussian noise, distort the sampled points and inhibit subsequent processing during this process. Filtering the measured data is, thereby, a necessary step. In contrast to conventional filter design, this paper describes a novel discrete low-pass filter with a variable shape. It is configured by two independent parameters, allowing it to be sensitive to time-critical peaks at high levels while providing adequate noise suppression at low measured quantities. The theoretical background of this work's filter approach is explained, and guidelines for parameter selection are suggested. In the utilized test data set, the novel approach results in a 19 % better peak preservation while providing the same noise suppression as a conventional low-pass filter. © 2025 IEEE.

关键词： discrete digital filter disturbance and response behavior Gaussian filter measurement data parameterizable low-pass signal processing

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning 6

A Machine Learning Framework for Skin Cancer Detection using...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Augustine, Mary Linda Panakal Spanogianopoulos, Sotirios Sharif, Mhd Saeed University of East London School of Architecture Computing and Engineering Department of Computer Science and Digital Technologies London United Kingdom University of Portsmouth School of Electrical and Mechanical Engineering Portsmouth United Kingdom

ISBN: (纸本)9798331506520

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach 6

Low-Data Classification of Historical Music Manuscripts: A F...

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Shatri, Elona Raymond, Daniel Fazekas, Gyorgy Queen Mary University of London Centre for Digital Music London United Kingdom Queen Mary University of London Big Data Science London United Kingdom

ISBN: (纸本)9798331506520

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods. We overcome this challenge by training a neural-based feature extractor on unlabelled data, enabling effective classification with minimal samples. Key contributions include optimising crop preprocessing for a self-supervised Convolutional Neural Network and evaluating classification methods, including SVM, multilayer perceptrons, and prototypical networks. Our experiments yield an accuracy of 87.66%, showcasing the potential of AI-driven methods to ensure the survival of historical music for future generations through advanced digital archiving techniques. © 2025 IEEE.

关键词： Self-supervised learning

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Performance Evaluation of Edge Detectors for Automated Book Spine Segmentation in Library Bookshelf images 16th

Performance Evaluation of Edge Detectors for Automated Book ...

引用

16th International conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Maung, Myat Min Zu Aung, Zu Nwe, Than Than University of Information Technology Hlaing Campus Parami Road Yangon Myanmar

ISBN: (纸本)9789819615346

A well-maintained library is a valuable resource for educational institutions and research facilities. Efficient management of libraries requires a reliable system for cataloging books. This paper evaluates four prominent edge detectors - Canny, Sobel, Prewitt and Roberts, analyzing their performance using metrics such as mean square error (MSE), root mean square error (RMSE) and peak signal-to-noise ratio (PSNR). Why we study on the evaluation of edge detectors initiated from the studying of the University of Information Technology’s Library, which houses over 100,000 books. We aim to address the challenges of efficiently cataloging and retrieving books in the digital library of this extensive collection to collaborate with an IT research center for future improvements. In these days, optimizing edge detection for book spine recognition is essential for efficient cataloging, inventory management and quick retrieval in digital library applications. Thus, this paper presents a comparative analysis of the edge detection techniques to support in solving the challenges of a digital library in future. These techniques are within computer vision and image processing to enhance book spine detection accuracy, reduce manual labor and minimize errors in robotic libraries. Our evaluation results indicate that the Canny edge detector consistently outperforms the others, demonstrating superior performance with lower MSE, RMSE and higher PSNR values. Integrating the Canny edge detector into existing systems will enhance book organization and retrieval in the digital library. This accurate and noise-resistant detector addresses book spine segmentation issues by solving cataloging inefficiencies and improving overall management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image segmentation

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Medical image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain 6

Medical Image Authentication and Self-Recovery Using Fragile...

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Bouarroudj, Riadh Harrou, Fouzi Zerrouki, Nabil Souami, Feryel Bellala, Fatma Zohra Sun, Ying University of Sciences and Technology Houari Boumedienne Department of Computer Science Algiers Algeria King Abdullah University of Science and Technology CEMSE Division Thuwal Saudi Arabia Center for Development of Advanced Technology DI2M Laboratory Algiers Algeria University of Algiers Benyoucef Benkhedda Department of Computer Science Algiers Algeria

ISBN: (纸本)9798331506520

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%. © 2025 IEEE.

关键词： Frequency domain analysis

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Efficient Algorithm Analysis Of Kidney images On FPGA

Efficient Algorithm Analysis Of Kidney Images On FPGA

引用

2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Thanuja, G.V. Ramya, S. Manipal Institute of Technology Manipal Academy of Higher Education Dept. Electronics and Communication Engineering Karnataka Manipal576104 India

ISBN: (纸本)9798331529833

The field-programmable gate array (FPGA) offers an effective solution to meet the high-performance requirements of real-time digital signal processors. IP cores developed on FPGAs benefit from the programmable logic's flexibility, efficient timing, and adaptability in algorithm modification, coupled with the processing power provided by the embedded processor. Integrating image processing into this sector is an ideal addition, especially in the growing field of edge detection, which is crucial in areas like image pattern recognition, machine learning, and data processing. This paper presents a case study focused on kidney CT scan images for edge detection, utilizing the Sobel and Canny edge detection techniques on the target FPGA device, xc7z020clg484-1. An IP core was designed and generated using VIVADO software, converting image pixels into binary form for pre-processing through these edge detection algorithms. The algorithms were simulated and synthesized on the xc7z020clg484-1 FPGA, with the entire design implemented in Verilog code to create the IP, which connects to the DMA controller throughout the system. This IP core can process multiple CT scan images simultaneously, making it valuable for various biomedical applications. The primary aim of this research is to compare the Sobel and Canny edge detection algorithms to identify the best approach for developing an IP core, evaluated by metrics such as total power consumption, CPU time, execution time, LUT count, and FF usage. The resulting IP core is efficient and conserves resources, making it suitable for other embedded applications. © 2025 IEEE.

关键词： image analysis

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Waste Sorting System Using AI: Development of An Embedded Solution for Efficient Waste Management 6

Waste Sorting System Using AI: Development of An Embedded So...

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6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Remili, Amal Mahdi, Mouna Zouari Elleuch, Jihen Frikha Fakhfakh, Ahmed Sellami, Dorra National Engineering School of Sfax Electrical Engineering Department Sfax Tunisia Sfax Digital Research Center Sfax Tunisia

ISBN: (纸本)9798331506520

Waste is causing a serious environmental problem. Waste management and circular economy offers a best alternative for preserving the environment and protecting nature resources. This paper presents an AI-based system for waste identification and sorting, particularly focusing on plastic, which is the less decomposed waste, using computer vision techniques. The data, used for system validation, comprises publicly available datasets and a locally collected dataset, devoted to plastic classification and detection. Performance metrics demonstrate that YOLOv8 outperforms other deep learning solutions, in real-time waste detection and is well suited for implementation on a Raspberry Pi 4 Model B. The system outperforms also existing methods in terms of efficiency, accuracy, and speed, and is a promising solution for addressing the global waste management challenge. © 2025 IEEE.

关键词： AI models Computer vision Waste detection Waste management Waste sorting

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InVideo Search: Scene Description Clustering and Integrating image and Audio Captioning for Enhanced Video Search 21st

InVideo Search: Scene Description Clustering and Integrating...

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21st International conference on Distributed Computing and Intelligent Technology

作者： Khan, Almira Asif Muhammed Shaji, Asher Mathews Sujith, Devika Nath, Aneesh G. Udmale, Sandeep S. TKM Coll Engn Dept Comp Sci & Engn Kollam Kerala India Veermata Jijabai Technol Inst VJTI Dept Comp Engn & Informat Technol Mumbai 400019 Maharashtra India

ISBN: (纸本)9783031814037;9783031814044

In the digital era, the vast amount of long videos highlights the need for an advanced system to efficiently find specific segments based on text descriptions. The proposed solution aims to develop a video content retrieval system by utilizing established tools in video analysis, image/video captioning, and information retrieval. The work is organized into three modules: Keyframe extraction, Captioning, and Query-based searching. Our primary focus is on effectively identifying key elements within these videos, achieving a semantic understanding of these videos in the form of captions, and establishing precise matches of user queries. In short, users input descriptive text prompts, and the application, equipped with pre-trained models, promptly and accurately identifies and retrieves the video segments. This application carries significant potential across various domains, from enabling efficient scene location in movies to helping extract knowledge from educational lectures. Furthermore, this research fundamentally departs from conventional video search methods that heavily rely on metadata or manually assigned tags, which often fall short of capturing the nuances of video content. This work also focuses on a multimodal captioning strategy to integrate more semantic content. Clustering of captions was also incorporated to improve search performance in the case of long videos. This approach allows us to deliver a robust and efficient solution, taking advantage of the extensive knowledge and research underlying these established technologies. Beyond the immediate applications, this research has broader applications in fields like natural language understanding, multimedia analysis, and information retrieval.

关键词： Clustering Keyframe extraction Natural language processing Video content retrieval system

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