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

共 13626 条记录，以下是181-190 订阅

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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...

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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...

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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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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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HVS-based AR Display image Enhancement With Delta Look-Up Table Considering Ambient Light 20

HVS-based AR Display Image Enhancement With Delta Look-Up Ta...

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20th International SoC Design conference (ISOCC)

作者： Cho, Donghun Lee, Hyeseong Lee, Jeonghun You, Jaehee Hongik Univ Integrated Syst Chip Seoul South Korea

ISBN: (纸本)9798350327038

Based on human visual systems, image processing algorithms, and efficient hardware implementation methodologies are proposed to optimize the image qualities of AR displays according to the changes in ambient lights. To this end, methods are described to improve the image qualities perceived by humans. In addition, the delta look-up table is presented to minimize the number of additional circuits without significant changes in existing hardware. HOSA, an image quality assessment based on the human visual system is used to verify the image qualities for the extreme ambient light conditions.

关键词： image quality enhancement AR display Ambient light Human visual system Delta LUT

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Enhancing Palm Recognition Accuracy Through image processing Deep Learning Techniques 2

Enhancing Palm Recognition Accuracy Through Image Processing...

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2nd IEEE International conference on Data Science and Network Security, ICDSNS 2024

作者： Mercy, J. Sheela Priscila, S. Silvia Bharath Institute of Higher Education and Research Department of Computer Applications Chennai India Bharath Institute of Higher Education and Research Department of Computer Science Chennai India

ISBN: (纸本)9798350373110

Palm recognition systems play an important role in biometric authentication;however, existing systems frequently have low accuracy and resiliency due to problems such as changing lighting conditions, occlusions, and hand orientations. The paper describes a novel way to improve palm recognition accuracy by combining modern image processing and deep learning approaches. Unlike traditional systems that utilize handcrafted features and shallow learning algorithms, the proposed system uses convolutional neural networks (CNNs) for feature extraction and classification, as well as advanced image preprocessing techniques. The technology preprocesses palm images to increase quality and consistency, thereby reducing the impact of environmental factors. Cross-validation trials yielded consistent results, with an average accuracy of 95%, precision of 92%, recall of 96%, and Fl-score of 94%. Furthermore, computational efficiency comparisons reveal that the proposed system beats existing ones in terms of training time, inference time, and model size, indicating that it is a promising option for accurate and efficient palm recognition in a variety of real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approaches In Automatic Number Plate Recognition: Result and Analysis 4

Deep Learning Approaches In Automatic Number Plate Recogniti...

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4th IEEE International conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Sharma, Rajeshwari Soni, Nandani Chaturvedi, Payashwani Malleri, Nimisha Bahety, Priyal Kukade, Jyoti Mishra, Trapti Medicaps University Indore India

ISBN: (纸本)9798331518981

systems known as Automatic Number Plate Recognition (ANPR), license plate recognition or LPR are now widely used in many sectors such as law enforcement, traffic control, vehicle access etc. This is a technology that uses object detection and optical character recognition to identify the numbers on number plates from input images or video. These systems use high-level hardware and software components together to automatically capture, recognize and process license plate information. Key elements of any ANPR system include high-resolution cameras, image processing algorithms, and optical character recognition (OCR) methods. The operational workflow of an ANPR system comprises several steps like image acquisition, preprocessing, license plate localization, character segmentation, OCR, and post-processing stages. Different applications of ANPR technology include traffic flow monitoring;helping police track stolen vehicles;congestion detection for traffic management purposes;and automated entry/exit processes based on authorized number plate recognition among others for supporting vehicle access control. A accuracy of 90% was achieved by proposed, ANPR systems represent a significant technological advance that improves accuracy;efficiency as well as automation in various critical areas of our lives. © 2024 IEEE.

关键词： image acquisition

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Infrared small target detection method based on full-direction dilation Gaussian filtering and local background estimation

Infrared small target detection method based on full-directi...

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2023 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Sun, Xiyu Pan, Shengda College of Information Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9781510677685

Addressing the limitations of currently rare small target detection algorithms based on Human Visual systems (HVS) that struggle with achieving satisfactory performance in complex backgrounds and lack high real-time capabilities, this paper introduces an innovative small target detection algorithm. This approach is founded on the integration of the Full-Direction Dilation Difference of Gaussians (FDD-DOG) and background estimation based on local contrast (LCBE). First, the high-frequency signal in the target area is enhanced by using the FDD-DOG operation, based on the difference between the real target and the background. Then, the background noise is further suppressed by assigning appropriate weights to the enhanced image by using local contrast-based background estimation. After that, the target to be detected is extracted by the adaptive threshold segmentation method. Experiments comparing the algorithm proposed in this paper with other HVS-based detection algorithms show that the method in this paper exhibits better detection performance in different infrared image sequences, where the background suppression factor is improved by an average of about 25 times and the signal-to-clutter ratio gain is improved by an average of about 15 times. © 2024 SPIE.

关键词： image enhancement

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