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

共 12948 条记录，以下是411-420 订阅

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Braille-to-Voice Converter Using image processing 2nd

Braille-to-Voice Converter Using Image Processing

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2nd International conference on Advances in Computational Intelligence and Informatics, ICACII 2023

作者： Prasad, P. Rajendra Ahmed, Roshan Zameer Lingamurthy, Chethan Raju Chaitra, A.M. Deepak, H.R. Ramaiah Institute of Technology Bangalore560054 India Presidency University Bangalore India

ISBN: (纸本)9789819747269

Approximately 2.2 billion people in the world are visually impaired and rely on assistance to meet their basic needs. Education is a fundamental human need and plays a crucial role in empowering individuals. For visually impaired individuals, education can not only integrate them into society but also enhance their professional and academic abilities. To facilitate better education for the visually impaired, specialized books, educational materials, and devices are necessary. Braille is a tactile system designed for people who are blind, enabling them to read and write through touch. Proposed work aims to improve the education of visually impaired individuals by developing a braille-to-voice converter. The system will use image processing techniques to capture an image of braille text, which will then be analyzed using computer vision algorithms to extract the text and convert it to speech. The ultimate goal is to create a cost-effective, accurate, and user-friendly tool that can be easily integrated into the daily lives of visually impaired individuals. With this system, visually impaired individuals will be able to easily and quickly convert braille text to speech in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Intelligent Frame and Table Segmentation in Blueprint Documents: Method and Implementation 6th

Intelligent Frame and Table Segmentation in Blueprint Docume...

引用

6th conference on Computational Methods in systems and Software (CoMeSySo)

作者： Hasan, Fudail Kashevnik, Alexey ITMO Univ St Petersburg Russia SPC RAS St Petersburg Russia Petrozavodsk State Univ PetrSU Petrozavodsk Russia

ISBN: (纸本)9783031214370;9783031214387

Blueprints are documents that contain the drawing of a design and the information that explains this design. Recently, the task to automatically recognize the information in the blueprint documents has become required. Segmenting the frame and tables in the blueprint is the first step of understanding the blueprint document. In this paper, we explain an automated method for frame and table segmentation. The proposed method processes the blueprint as an image and defines all the parts (pixels) of the blueprint that belongs to the frame or tables. It finds all the lines in the blueprint's image and decide which combinations of lines construct frame and tables. It can process blueprints that have high resolution. The proposed method allows to isolate frame and tables in the blueprint document. We achieved an accuracy of 99% for excellent quality documents.

关键词： Frame and table segmentation Blueprint processing Table detection Lines segmentation

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The Impact of Preprocessing on Face Recognition using Pseudorandom Pixel Placement 29

The Impact of Preprocessing on Face Recognition using Pseudo...

引用

29th International conference on systems, Signals and image processing (IWSSIP)

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Nurzaenab Kanazawa Univ Dept Elect Engn & Comp Sci Kanazawa Japan Kumamoto Univ Ctr Management Informat Technol Kumamoto Japan STMIK AKBA Study Program Informat Engn Makassar Indonesia

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

The condition of face images, data processing algorithms, and hardware capabilities can influence the accuracy of face recognition. Several studies have been conducted to increase performance of face recognition. One of the steps is to create or even improve the methods in preprocessing as one of the essential steps that can affect accuracy. This paper proposed a pseudorandom pixel placement method applied to the preprocessing step in face recognition to know the impact on accuracy. Eight face objects were used in this study. One face image for one object as training data was taken via a single-lens digital reflex camera and a smartphone. One video for one object was taken from Closed Circuit Television with two different placement angle conditions for testing data. The experiment was carried out with four variations of the basic resolution size of the face image in the testing data to see the performance of the proposed method. The result is five of eight face objects have improved accuracy than without using pseudorandom pixel placement. The best average accuracy result using the proposed method is 63.76% higher than without using the proposed method with a value of 60.09%, so preprocessing using the pseudorandom pixel placement on face recognition can increase accuracy.

关键词： Face Recognition Pseudorandom Pixel Placement One image Training Data image processing Computer Vision

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Post-processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological images

Post-Processing of Thresholding or Deep Learning Methods for...

引用

18th International Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： image segmentation

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Advanced HLS for Reconfigurable image processing in Big Data systems 12th

Advanced HLS for Reconfigurable Image Processing in Big Da...

引用

12th International conference on Big Data and Artificial Intelligence, BDA 2024

作者： Bhagirath, K. Pant, Dheeraj Tiwari, Abhishek Khaneja, Vivek Noida201309 India

ISBN: (纸本)9783031818202

This paper introduces novel HLS techniques for reconfigurable and memory-efficient image processing within deep learning frameworks, addressing inherent limitations of current deep learning accelerators (DLAs) due to their dependency on the host processor, leading to pipeline delays and reduced throughput. The proposed approach emphasizes parallelization, on-chip buffering, and scalability, enhancing computational efficiency and throughput for AI applications in big data and edge computing environments. Methodologically, the paper emphasizes mathematical analysis, hardware architecture specification, HLS modeling, Verilog RTL implementation. By addressing these challenges, this work contributes to advancing the performance capabilities of DLAs in deep learning inference tasks. The algorithms integrates seamlessly with DLA IP subsystems, such as AXI interfaces, external (DRAM) and internal (SRAM) memory interfaces, and activation/pooling engines. Key contributions include advanced algorithm design to enhance memory efficiency and reconfigurability, essential for adapting to diverse hardware architectures. Experimental validation demonstrates substantial improvements in computational speed and resource utilization compared to traditional methods, making it suitable for intensive AI tasks in big data analytics. In conclusion, this research presents a framework for integrating HLS techniques into hardware accelerators, facilitating efficient image processing across diverse applications in big data and edge computing. These advancements aim to bolster DLAs’ capabilities in handling complex AI workloads effectively, promising enhanced solutions for AI-driven domains. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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RestoreAgent: Autonomous image Restoration Agent via Multimodal Large Language Models 38

RestoreAgent: Autonomous Image Restoration Agent via Multimo...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Chen, Haoyu Li, Wenbo Gu, Jinjin Ren, Jingjing Chen, Sixiang Ye, Tian Pei, Renjing Zhou, Kaiwen Song, Fenglong Zhu, Lei The Hong Kong University of Science and Technology Guangzhou China Huawei Noah's Ark Lab Canada The University of Sydney Australia The Hong Kong University of Science and Technology Hong Kong

Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual selection of specific tasks, algorithms, and execution sequences, which is time-consuming and may yield suboptimal results. All-in-one models, though capable of handling multiple tasks, typically support only a limited range and often produce overly smooth, low-fidelity outcomes due to their broad data distribution fitting. To address these challenges, we first define a new pipeline for restoring images with multiple degradations, and then introduce RestoreAgent, an intelligent image restoration system leveraging multimodal large language models. RestoreAgent autonomously assesses the type and extent of degradation in input images and performs restoration through (1) determining the appropriate restoration tasks, (2) optimizing the task sequence, (3) selecting the most suitable models, and (4) executing the restoration. Experimental results demonstrate the superior performance of RestoreAgent in handling complex degradation, surpassing human experts. Furthermore, the system's modular design facilitates the fast integration of new tasks and models. © 2024 Neural information processing systems foundation. All rights reserved.

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Fingerprint image Enhancement Method Based on U-Net Model 1st

Fingerprint Image Enhancement Method Based on U-Net Model

引用

1st International conference on AI-generated Content, AIGC 2023

作者： Cheng, Yu-Hsin Su, Sheng-Gui Lin, Yih-Lon Hsu, Hsiang-Chen National Yunlin University of Science and Technology Yunlin Taiwan I-Shou University Kaohsiung Taiwan

ISBN: (纸本)9789819975860

Fingerprint image enhancement is a vital image processing technology that finds applications in fingerprint identification, matching, and biometric authentication. Its objective is to improve the performance and accuracy of fingerprint identification systems by enhancing the quality, clarity, and contrast of fingerprint images while reducing noise. Traditional algorithms often require adjusting parameters based on individual fingerprint images, resulting in inconsistent enhancement results. To address this challenge, this paper proposes a deep learning-based approach for fingerprint image enhancement. Experimental results demonstrate the effectiveness of U-Net architecture with different depths and the impact of incorporating attention gate. Actually, a U-Net model with a depth of six achieves superior performance, surpassing other depths, and the inclusion of attention gate further enhances the results. The proposed method not only retains more fingerprint features but also produces smoother structures and reduces noise surrounding the fingerprint image. These findings contribute to advancing fingerprint image enhancement techniques, bolstering the accuracy and performance of fingerprint identification systems in applications such as forensic analysis and biometric authentication. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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COVID-19 and Pneumonia Detection using X-ray image-Based Deep Learning Methods 2

COVID-19 and Pneumonia Detection using X-ray Image-Based Dee...

引用

2nd International conference on Advanced algorithms and Signal image processing, AASIP 2022

作者： Li, Yuxiao McGill University Montreal Canada

ISBN: (纸本)9781510660571

Globally, pneumonia is the leading cause of death for young people and children. An X-ray of the chest is usually used to diagnose pneumonia by a trained specialist. However, the process is tedious and can result in disagreements among radiologists. It is possible to improve diagnostic accuracy through the use of computer-aided diagnostic systems. In this work, the ResNet model was selected to work as the covid-19 and pneumonia detector based on X-ray image. Several experiments are conducted on to achieve an optimal results. © 2022 SPIE.

关键词： Deep learning

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A Study on Operational Techniques of Preliminary Refinement of images in Camouflaged Enemy Detection 8th

A Study on Operational Techniques of Preliminary Refinement ...

引用

8th International conference on Information and Communication Technology for Intelligent systems, ICTIS 2024

作者： Suneel, J.S. Dileep, M.R. Department of Master of Computer Applications Visvesvaraya Technological University—Research Centre Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of Master of Computer Applications Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819766833

This study investigates the crucial significance of image processing in the domain of image analysis for the identification and detection of potential threats in advanced defense systems. Real-time images, captured over a wide range of terrains, introduce specific challenges and possibilities for precise target differentiation. The success of subsequent analyses is heavily dependent on the meticulous preprocessing of obtained images, aiming to improve pertinent features and reduce noise interference. It involves a series of operations applied to an image to enhance its quality, correct imperfections, or prepare it for further analysis. Some of the common image preprocessing techniques are Resizing and Rescaling, Normalizing the pixel values, Gray-scale conversion, Cropping, Noise reduction, Contrast enhancement, Edge detection, Thresholding, etc. Also, a wide analysis and survey have been conducted by considering variant parameters that reflect the outcome. By conducting an examination of the literature and carrying out empirical assessments, this study showcases the effectiveness of the proposed preprocessing techniques in enhancing capabilities for detecting adversaries. The outcomes highlight significant enhancements in the precision, robustness, sensitivity, and speed of algorithms designed for enemy detection when employed in conjunction with the specified preprocessing methods. This paper discusses various techniques that influence image analysis and explores the utilization of these techniques across multiple computer vision domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image analysis

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A REVIEW OF PHOTOREALISTIC image STYLIZATION TECHNIQUES 5

A REVIEW OF PHOTOREALISTIC IMAGE STYLIZATION TECHNIQUES

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5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Qazi, Hassaan A. Univ Waterloo Dept Appl Math Waterloo ON Canada

ISBN: (纸本)9781665462198

Rendering photorealistic images from the image stylization technique is still considered as a challenging task. In this paper, we compare three recent state-of-the-art approaches. All three algorithms are mainly driven by Convolution Neural Network (CNN) technique. A brief discussion of the selected approaches is followed by some comparisons and results. Both Structural Similarity Index (SSIM) and Learned Perceptual image Patch Similarity (LPIPS) metrics are used to generate new findings of the methodologies. Finally, subjective analysis is also presented to gauge the efficacy of the algorithms in discussion.

关键词： image stylization Photorealistic Artistic framework Deep Neural Network Wavelet transform Perceptual similarity metrics

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