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

共 14657 条记录，以下是441-450 订阅

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Chinese image Description Generation Model Based on Recurrent Fusion Encoding 5th

Chinese Image Description Generation Model Based on Recurren...

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5th International conference on 3D Imaging Technologies-Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Zhou, Shiyu Donghua University Shanghai China

ISBN: (纸本)9789819751839

The image description job is a crucial area of research in the nexus of computer vision and applications for natural language processing. The objective is to produce accurate text descriptions using the picture attributes retrieved by the model. The conventional encoder-decoder model produces an inadequate encoding feature because it does less direct dimensionality reduction on images and is deficient in sequence feature information. This research suggests a recurrent fusion encoding-based Chinese picture description generation model to solve the aforementioned issues. R-CNN is used to extract the image's features first. Then, at the encoding side, gated recurrent units and a soft attention mechanism are used to build a recurrent fusion network for full sequence fusion of the image features. Finally, the sequence feature information is combined with a self-attention mechanism for multi-level interaction before being sent to the encoding side. I carried out enough tests on the Flickr_8k dataset and the Ai_Challenger dataset to confirm the model’s validity. According to the experimental data, the model performs better than some Chinese image description algorithms in each index by 0.2–4.2% points, and the generated statements are more accurate and sufficient for image description. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Encoding (symbols)

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Secure Vision: Enhancing Data Security in Wireless Sensor Networks through image processing 8

Secure Vision: Enhancing Data Security in Wireless Sensor Ne...

引用

8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Swamy, B. Venkata Gopi, S. Sudhakar, Kattupalli Girish, Pechetti Rani, M. Janaki Upadhyay, Shrikant B V Raju Institute of Technology Department of Chemistry Telangana State India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai Avadi India Lakireddy Bali Reddy College of Engineering Department of Ai & Ds Mylavaram India Bonam Venkata Chalamayya Institute of Technology and Science Batlapalem Amalapuram India Dr. Mgr Educational and Research Institute Department of Ece Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350376425

The secure Wireless Sensor Network (WSN) architecture was designed in such a way that the tradeoffs among efficiency, scalability and security were balanced. It was consisted of several sensor nodes, cluster heads, base station as well as the control center all of them played a vital role in functioning and managing of data. This network security was provided with a layered security approach like the physical, data link, network, transport, and application layers. Data security and integrity are secured through advanced image processing algorithms, and reliable communication protocol. Regular maintenance, constant monitoring and hardening solutions through resilience and fault tolerance mechanisms added a layer of trust in securing this WSN. A comparative analysis was made using different algorithms, such as Intrusion Detection systems (IDS) and detection accuracy is 98% and proposed model which has shown a better result in the energy consumption, data transmission latency, encryption time, detection precision as well as efficiency to insure an original data integrity. © 2024 IEEE.

关键词： Data integrity

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Braille-to-Voice Converter Using image processing 2nd

Braille-to-Voice Converter Using Image Processing

引用

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...

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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...

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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...

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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...

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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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A GPU-accelerated Algorithm for Copy Move Detection in large-scale satellite images 29

A GPU-accelerated Algorithm for Copy Move Detection in large...

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conference on image and Signal processing for Remote Sensing XXIX

作者： Barni, Mauro Costanzo, Andrea Dimitri, Giovanna Maria Tondi, Benedetta Univ Siena DIISM Via Roma 56 I-53100 Siena Italy

ISBN: (纸本)9781510666955;9781510666962

With the diffusion of advanced image editing software, image manipulation is becoming an impelling aspect also for satellite images. In a copy-move (CM) forgery, part of the image is copied and pasted elsewhere into the same image. In the satellite domain, CM can be performed with the intent of propagating misleading information on the geography and morphology of the landscapes pictured in the images. The best algorithms for CM detection rely on a multi-step procedure involving extraction of image descriptors (keypoints), keypoint matching and finally clustering, for the localization of the forged area. The large size of many satellite images and their richness of details, often prevent the adoption of off-the-shelf tools developed for multimedia images. Due to the large number of keypoints typically present in satellite images, in fact, the computational complexity and memory requirements for SIFT keypoints extraction, matching, clustering and forgery localisation is prohibitive. In this paper, we propose a CM detection algorithm that can successfully process very high resolution satellite images, where off-the-shelf alternatives are crashing due to system memory exhaustion. The proposed algorithm is based on three main strategies powered by GPU acceleration: i) multi-threaded tile-based SIFT keypoints extraction, ii) optimised batch-based descriptors matching, iii) clustering and localisation of manipulated pixels exploiting tensors instead of a sliding window approach. Experiments carried out on images belonging to the ESA WorldView-2 European Cities dataset and on a set of hand-made copy-move forgeries with resolution above 1 Gigapixel, show the good performance of the proposed algorithm in terms of processing time and memory consumption.

关键词： Copy Move detection Satellite image forgery detection Remote Sensing image Forgery

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

Fingerprint Image Enhancement Method Based on U-Net Model

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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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