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

共 14633 条记录，以下是191-200 订阅

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A Review on Satellite image Segmentation Using Metaheuristic Optimization Techniques 1

引用

5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bandikolla, Lakshmi Md Khairuzzaman, Abdul Kayom School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh Vijayawada522237 India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

image segmentation is essential in digital image processing applications. Multilevel thresholding is a popular technique for image segmentation. An image can be divide into multiple classes. In this paper, the image quality metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), mean structural similarity (SSIM), feature similarity (FSIM) are compared using metaheuristic algorithms. The CPU time of different metaheuristics is also compared to determine the time complexity of the techniques. The Otsu’s method, Kapur’s entropy, Tsallis entropy, and Masi entropy are used as objective functions. Some of the metaheuristic algorithms, adaptive Cuckoo search algorithm (ACS), chaotic coronavirus optimization algorithm (COVIDOA), dynamic quantum-inspired genetic algorithm (DQGA), a modified artificial bee colony algorithm (MABC), dynamic Harris hawks with mutation mechanism (DHHO/M), and particle swarm optimization algorithms (PSO) are compared. As per the comparison of the quality metrics, DHHO/M with Otsu was more efficient in average PSNR. CS with Tsallis provides best value in average MSE, ACS with Otsu gives best results in average SSIM, ACS with Tsallis is more efficient in average FSIM, and MABC with Kapur’s combination was more efficient in average CPU time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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On image processing and Pattern Recognition for Thermograms of Watermarks in Manuscripts - A First Proof-of-Concept 18th

On Image Processing and Pattern Recognition for Thermograms ...

引用

18th International conference on Document Analysis and Recognition (ICDAR)

作者： Hauser, Dominik Beckmann, Matthias Koliander, Gunther Stiehl, H. Siegfried Univ Hamburg Dept Informat Hamburg Germany Univ Bremen Ctr Ind Math Bremen Germany Imperial Coll London Dept Elect & Elect Engn London England Austrian Acad Sci Acoust Res Inst Viennna Austria

ISBN: (纸本)9783031705427;9783031705434

Watermarks in historical manuscripts are figural shapes serving as tokens for provenance research (e.g. scribe identification, dating, papermill attribution, scribe-papermaker relation, trading, etc.) in Humanities such as Musicology. As of today, they come in a variety of formats: digitized handtracings and rubbings, X-ray based imagery and, more recently, thermograms acquired with infrared (IR) cameras - all of which have been made accessible via image data bases in libraries or archives like the watermark information system (WZIS). A key use case from a scholar's perspective is the search for similar or even equal watermarks in whatever digitized data collections. Non-surprisingly, the prerequisite is the availability of a versatile, reliable, and user-friendly tool comprising methods from digital image processing (IP) and pattern recognition (PR). In our paper, we focus on bridging the gap between digitized thermograms of music manuscripts and watermark classification for similarity-based search through (i) a state-of-the-art (SOTA) analysis, (ii) a resulting conceptual design based on well-understood SOTA as well as novel methods, (iii) an easy-to-use implementation, and (iv) an experimental validation as Proof-of-Concept (PoC). The current system performance is characterized using thermograms recently made openly available within the DRACMarkS project as well as WZIS. The experimental results clearly demonstrate success in bridging the existing gap hence also setting a baseline for an as yet lacking benchmark.

关键词： Historical Document Analysis image processing Document Analysis systems

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Automated Defect Detection and Segregation in Dyed Fabrics Using image processing Techniques 5

Automated Defect Detection and Segregation in Dyed Fabrics U...

引用

5th International conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Prabha, K.R. Aathavan, V. Harshit, K. Indhumathi, K. Sri Ramakrishna Engineering College Department of Ece Coimbatore India

ISBN: (纸本)9798350379945

This study addresses the critical need for efficient quality control in textile manufacturing, aiming to enhance the production of high-quality fabric to meet consumer demands and industry benchmarks. Despite the meticulous cleaning and dyeing processes involved in fabric production, there remains a challenge in identifying and segregating defective sections, which can potentially compromise the overall quality of the fabric. To overcome this challenge, this research study proposes an innovative solution: Automated Quality Control in Textile Manufacturing, utilizing advanced image processing techniques. The proposed system employs high-resolution cameras and image processing algorithms to analyze the fabric's surface in real-time. By leveraging predefined criteria and machine learning models, the system can accurately detect and isolate flawed sections within the fabric. These criteria encompass a range of defects, including stains, discolorations, or irregular patterns that deviate from established quality standards. This approach improves quality control in textile manufacturing, providing manufacturers with a reliable and efficient means of identifying and addressing defects in fabric production. By automating the quality control process, manufacturers can enhance productivity, reduce waste, and ultimately deliver higher-quality products to consumers. © 2024 IEEE.

关键词： Textile industry

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Invisible image Watermarks Are Provably Removable Using Generative AI 38

Invisible Image Watermarks Are Provably Removable Using Gene...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Zhao, Xuandong Zhang, Kexun Su, Zihao Vasan, Saastha Grishchenko, Ilya Kruegel, Christopher Vigna, Giovanni Wang, Yu-Xiang Li, Lei UC Berkeley United States Carnegie Mellon University United States UC Santa Barbara United States UC San Diego United States

Invisible watermarks safeguard images' copyrights by embedding hidden messages only detectable by owners. They also prevent people from misusing images, especially those generated by AI models. We propose a family of regeneration attacks to remove these invisible watermarks. The proposed attack method first adds random noise to an image to destroy the watermark and then reconstructs the image. This approach is flexible and can be instantiated with many existing image-denoising algorithms and pre-trained generative models such as diffusion models. Through formal proofs and extensive empirical evaluations, we demonstrate that pixel-level invisible watermarks are vulnerable to this regeneration attack. Our results reveal that, across four different pixel-level watermarking schemes, the proposed method consistently achieves superior performance compared to existing attack techniques, with lower detection rates and higher image quality. However, watermarks that keep the image semantically similar can be an alternative defense against our attacks. Our finding underscores the need for a shift in research/industry emphasis from invisible watermarks to semantic-preserving watermarks. Code is available at https://***/XuandongZhao/WatermarkAttacker. © 2024 Neural information processing systems foundation. All rights reserved.

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Improving Robustness of Satellite image processing Using Principal Component Analysis for Explainability

Improving Robustness of Satellite Image Processing Using Pri...

引用

19th International conference on Software Technologies, ICSOFT 2024

作者： Witteck, Ulrike Stambke, Jan Grießbach, Denis Herber, Paula Berlin-Adlershof Germany Embedded Systems Group University of Munster Germany

ISBN: (纸本)9789897587061

Finding test-cases that cause mission-critical behavior is crucial to increase the robustness of satellite on-board image processing. Using genetic algorithms, we are able to automatically search for test cases that provoke such mission-critical behavior in a large input domain. However, since genetic algorithms generate new test cases using random mutations and crossovers in each generation, they do not provide an explanation why certain test cases are chosen. In this paper, we present an approach to increase the explainability of genetic test generation algorithms using principal component analysis together with visualizations of its results. The analysis gives deep insights into both the system under test and the test generation. With that, the robustness can be significantly increased because we 1) better understand the system under test as well as the selection of certain test cases and 2) can compare the generated explanations with the expectations of domain experts to identify cases with unexpected behavior to identify errors in the implementation. We demonstrate the applicability of our approach with a satellite on-board image processing application. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Satellite imagery

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Building Safe and Reliable AI systems for Safety Critical Tasks with Vision-Language processing 45th

Building Safe and Reliable AI Systems for Safety Critical Ta...

引用

45th European conference on Information Retrieval (ECIR)

作者： Ao, Shuang Open Univ Walton Hall Milton Keynes MK7 6AA Bucks England

ISBN: (纸本)9783031282409;9783031282416

Although AI systems have been applied in various fields and achieved impressive performance, their safety and reliability are still a big concern. This is especially important for safety-critical tasks. One shared characteristic of these critical tasks is their risk sensitivity, where small mistakes can cause big consequences and even endanger life. There are several factors that could be guidelines for the successful deployment of AI systems in sensitive tasks: (i) failure detection and out-ofdistribution (OOD) detection;(ii) overfitting identification;(iii) uncertainty quantification for predictions;(iv) robustness to data perturbations. These factors are also challenges of current AI systems, which are major blocks for building safe and reliable AI. Specifically, the current AI algorithms are unable to identify common causes for failure detection. Furthermore, additional techniques are required to quantify the quality of predictions. All these contribute to inaccurate uncertainty quantification, which lowers trust in predictions. Hence obtaining accurate model uncertainty quantification and its further improvement are challenging. To address these issues, many techniques have been proposed, such as regularization methods and learning strategies. As vision and language are the most typical data type and have many open source benchmark datasets, this thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering. In this thesis, we aim to build a safeguard by further developing current techniques to ensure the accurate model uncertainty for safety-critical tasks.

关键词： Deep learning Model calibration Uncertainty

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Human-in-the-Loop (HITL) Augmented Growing Neural Gas for Labeling Aerial image Datasets

Human-in-the-Loop (HITL) Augmented Growing Neural Gas for La...

引用

IEEE International conference on Fuzzy systems (FUZZ-IEEE)

作者： Schulz, Jeffrey Anderson, Derek T. Keller, James M. Scott, Grant Luke, Robert H., iii Univ Missouri Elect Engn & Comp Sci EECS Dept Columbia MO 65201 USA US Army DEVCOM Ctr Ft Belvoir VA USA

ISBN: (纸本)9798350332285

There is a severe demand for, and shortage of, large accurately labeled datasets to train supervised computational intelligence (CI) algorithms in domains like unmanned aerial systems (UAS) and autonomous vehicles. This has hindered our ability to develop and deploy various computer vision algorithms in/across environments and niche domains for tasks like detection, localization, and tracking. Herein, we propose a new human-in-the-loop (HITL) based growing neural gas (GNG) algorithm to minimize human intervention during labeling large UAS data collections over a shared geospatial area. Specifically, we address human driven events like new class identification and mistake correction. We also address algorithm-centric operations like new pattern discovery and self-supervised labeling. The effectiveness of our algorithm is demonstrated using simulated realistic ray-traced low altitude UAS data from the Unreal Engine. Our results show that it is possible to increase speed and reduce mental fatigue over hand labeling large image datasets.

关键词： self-supervised learning human-in-the-loop growing neural gas possibilistic nearest neighbor UAS UAV

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Automated Dengue Recognition and Analysis through image processing and Machine Learning 2

Automated Dengue Recognition and Analysis through Image Proc...

引用

2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Patil, Preethi N Usha, J. Dharani, Andhe RV College of Engineering Department of MCA Bangalore India

ISBN: (纸本)9798350317558

Detecting dengue fever using image processing techniques typically involves the analysis of medical images such as blood smears or tissue samples. Dengue is a viral disease transmitted by Aedes mosquitoes, and its diagnosis primarily relies on clinical symptoms, serological tests, and molecular assays. image processing can be used as a complementary tool to assist and identify the dengue virus or related disparities in the body. Dengue fever, alternatively known as break-bone fever, is a lifethreatening arboviral disease caused by the DENV virus. It is a major global health concern with an increasing incidence worldwide. The existing clinical methods for diagnosing dengue are manual, requiring significant time and labor. The present work aims to develop an automated system using machine learning and digital image analysis to detect dengue infection from blood smear images. The custom algorithms are designed to extract platelets, red blood cells, and white blood cells, and train a feature vector. By automating the analysis of blood smear images, aids healthcare professionals detect cases of dengue more efficiently, allowing for early treatment and potentially saving lives. However, it is crucial to address data quality, algorithm selection, validation, interpretability, and ethical considerations throughout the development process. © 2024 IEEE.

关键词： CIELab space Dengue DENV edge detection histogram thresholding Hough transform pixel intensity

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Patch based image processing for complex environment characterization 6

Patch based image processing for complex environment charact...

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

作者： Menier, Corentin Meurie, Cyril Guillemaille, Timothee Ruichek, Yassine Marais, Juliette Univ Gustave Eiffel COSYS-LEOST Villeneuve d'AscqF-59650 France UTBM BelfortF-90010 France

ISBN: (纸本)9798331506520

Environment analysis is a critical part of autonomous vehicle for transport applications and for passenger safety. The solutions demonstrating the greatest robustness have been integrating multiple sensors used for redundancy and refinement purposes. Vision applications have proven to offer a high degree of flexibility and performance. One particular instance of this is higlighted in vehicle localisation, which predominantly relies on GNSS-based systems for positioning calculation using propagation time measurements. However, this signal may be degraded through the environment around the vehicle, worst case being urban canyons leading to Non Line Of Sight(NLOS) scenarios or multipaths issues due to reflecting obstacles. Previous work have shown vision-based algorithms can be used to mitigate these effects. One widely studied approach relies on the segmentation of an acquired wide-angle image installed on the roof of the vehicle and oriented toward the *** the sky processing module is binary, the pipeline lack any way to express its uncertainty when applying weighting policies to the detected satellite state, which can be detrimental to the resulting positioning. In this paper, we propose a novel way of analysing wide-angle camera images, also known as fisheye images, dividing the image into patches to output the corresponding situation of each region of interest. Additionally we propose a new class to the previous sky versus non-sky segmentation, designated as mixed class and designed to serve as a fuzzy answer by the deep learning model to improve confidence to other scenarios as well as allow for new analysis policies of satellites signals. The data-driven algorithm is designed and tested on a publicly available dataset, composed of a large number of finely labelled images provided by ISAE-SUPAERO reaching a 94% accuracy. © 2025 IEEE.

关键词： image segmentation

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Analysis of Decision Tree and Random Forest algorithms for Nutrient Deficiency Classification in Citrus Leaves through image processing 17

Analysis of Decision Tree and Random Forest Algorithms for N...

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17th International conference on Telecommunication systems, Services, and Applications, TSSA 2023

作者： Kamelia, Lia Nurmalasari, Rin Rin UIN Sunan Gunung Djati Bandung Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350309164

The research presents a hybrid approach to identify and categorise nutritional deficiency syndrome in citrus leaves using image processing and machine learning. The method includes processing images, segmenting images and extracting color-Texture information. The Decision Tree and the Random Forest algorithms use various statistical measures for classification, including mean, skewness, variance, entropy, maximum probability, angular second moment, contrast, correlation, dissimilarity, energy, and homogeneity. The test results show a range of performance levels, with an accuracy of 80.28% being attained by utilising an 80:20 data ratio in the Decision Tree approach. The Random Forest method's highest accuracy, 91.25%, was reached using an 80:20 ratio. In conclusion, the accuracy of the Random Forest approach regularly beats that of the Decision Tree method. © 2023 IEEE.

关键词： Decision trees

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