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

共 15613 条记录，以下是761-770 订阅

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RNCE: A New image Segmentation Approach

RNCE: A New Image Segmentation Approach

引用

International conference on Computer vision and Machine Intelligence, CVMI 2022

作者： Kumar, vikash Ali, Asfak Chaudhuri, Sheli Sinha Electronics and Telecommunication Engineering West Bengal Jadavpur Kolkata700032 India

ISBN: (纸本)9789811978661

Semantic image segmentation based on deep learning is gaining popularity because it is giving promising results in medical image analysis, automated land categorization, remote sensing, and other computer vision applications. Many algorithms have been designed in recent years, yet there is scope for further improvement in computer vision research. We have proposed a unique ensemble method called Ranking and Nonhierarchical Comparison Ensemble (RNCE) for semantic segmentation of landcover images based on the Ranking and Nonhierarchical Comparison methodology. Our approach has been tested on pretrained models showing improved accuracy and mean IoU with respect to the existing method. The code is available at: https://***/vekash2021/***. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Remote sensing

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A Novel Color image Encryption using DNA Code and Dual Memristor Hyperchaotic System 18

A Novel Color Image Encryption using DNA Code and Dual Memri...

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18th INDIAcom;11th International conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Belete, Biniyam Ayele Gelmecha, Demissie Jobir Singh, Ram Sewak Rathee, Davinder Singh Arora, Amit Besfat, Henok Mezemr Adama Science and Technology University Department of Electronics and Communication Engineering Adama Ethiopia Galgotias University Department of Electronics Gr. Noida India

ISBN: (纸本)9789380544519

This work presents a modified hyperchaotic system based on two memristors with unique Lyapunov exponents, offering a unique method for image encryption. The main goals are to use dynamic analysis to evaluate the complexity and unpredictable nature of the proposed method and then apply it to image encryption. A strong color picture encryption system is developed by combining Deoxyribonucleic acid (DNA) sequences, logistic maps, and the memristive hyperchaotic system. Using an upgraded four-dimensional dual memristor-based hyperchaotic system with three Lyapunov exponents, logistic mapping, and DNA encoding are all integrated in this technique. This research carefully combines these three methods to solve current issues with picture encryption schemes. Security performance tests and simulations, which verify the efficiency of the proposed algorithm against different attacks, such as brute-force, statistical, entropy, differential, visual, and histogram attacks, preserve methodological rigor. Comparisons with traditional chaotic systems in image processing demonstrate how much better security the proposed method exhibits. Important rmdings show how DNA operations may be successfully integrated with the hyperchaotic memristive system to produce improved picture encryption capabilities. The program not only proves its efficacy in protecting picture data, but it also performs better than traditional methods. Applications like information security, encryption, and secure communication are all affected by these discoveries. The research highlights the algorithm's resilience to many attacks, highlighting its potential as a significant advance in image encryption algorithms. The suggested method offers a feasible way to protect strict image data in a variety of real-world situations. © 2024 Bharati vidyapeeth, New Delhi.

关键词： DNA computing Hyperchotic system image encryption Memristor Security metrics

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Smart equipment failure detection with machine learning applied to thermography inspection data in modern power systems 11

Smart equipment failure detection with machine learning appl...

引用

11th International conference on Smart Grid (IcSmartGrid)

作者： Maria Garzon, Ana Laiton, Natalia Sicacha, victor Celeita, David F. Trung Dung Le Univ Rosario Sch Engn Sci & Technol Bogota Colombia Univ Paris Saclay Cent Supelec CNRS Grp Elect Engn Paris GeePs Gif Sur Yvette France

ISBN: (纸本)9798350310665

This paper presents a novel approach to detecting equipment failures in modern power systems by leveraging machine learning techniques applied to thermography inspection data. Particularly segmentation and pixel processing to improve accurateness is highlighted in the methodology. The proposed method is capable of identifying early warning signs of equipment failure and predicting when the failure is likely to occur. The proposed approach demonstrates the potential for early detection of equipment failure in modern power systems with accurate clustering. The use of machine learning algorithms applied to thermography inspection data provides a reliable and effective way to identify and predict equipment failures, ultimately leading to improved system reliability and reduced maintenance costs.

关键词： Thermography inspection image segmentation predictive fault identification machine learning neural network

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Automatic Classification and Recognition of Tourist Attraction images using k-Means Algorithm and image processing Technology

Automatic Classification and Recognition of Tourist Attracti...

引用

Distributed systems, Computer Networks and Cybersecurity (ICDSCNC), International conference on

作者： Qian Jiang School of Public Administration Chongqing Vocational College of Transportation China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This study investigates the automatic classification and recognition of tourist attraction images through an enhanced K-means algorithm paired with image processing techniques. Given the growing interest in efficient tourist image management and promotion, our research presents a system that significantly advances the processing and categorization of scenic spot images. We implemented a variety of image preprocessing steps—such as grayscale conversion, normalization, Gaussian filtering, and edge detection — to improve the accuracy of feature extraction. Our method involved an optimized K-means algorithm, modified to select initial centroids more effectively and adjust cluster numbers dynamically. Experimental results, based on a dataset of 150 images, demonstrated a classification accuracy of 92%, with the system maintaining robust performance even as dataset sizes increased, confirming the algorithm's efficiency and stability. The processing time for 5000 images was recorded at 12.3 seconds, showcasing a linear relationship between data size and processing time. These findings highlight the potential of our proposed system in enhancing tourism management and marketing strategies by providing a reliable and scalable image classification solution.

关键词： Accuracy Filtering image processing image edge detection Heuristic algorithms Clustering algorithms Feature extraction Classification algorithms image preprocessing Reliability

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Enhanced YOLOv8 for rapid diagnosis and analysis of infrared spectra in power industry insulator detection 4

Enhanced YOLOv8 for rapid diagnosis and analysis of infrared...

引用

4th International conference on Advanced algorithms and Neural Networks, AANN 2024

作者： Zhou, Ming Wu, Qiang Xiong, Wei Ma, Li Cheng, Zhao Sun, Liping State Grid Hubei Electric Power Company Wuhan China Economic and Technological Research Institute State Grid Hubei Electric Power Company Wuhan China

ISBN: (纸本)9781510686106

Insulator defects in power transmission systems pose significant risks to grid stability and safety. This paper presents an improved method for rapid diagnosis of insulator defects using infrared images, based on an enhanced YOLOv8 algorithm. Our approach incorporates Haar wavelet downsampling and a Polarized Self-Attention (PSA) mechanism to boost detection accuracy while maintaining real-time performance. The Haar wavelet downsampling Boosts the model's capability to grasp multi-scale features, concurrently enhancing the PSA mechanism's emphasis on pertinent thermal patterns. We evaluate our method on a comprehensive dataset of 10,000 infrared images collected from various power transmission environments. Experimental results demonstrate that our improved YOLOv8 model achieves SOTA, with a mean Average Precision of 94.7%, outperforming the original YOLOv8 and other object detection algorithms. The proposed method offers a 3.2% increase in mAP compared to the original YOLOv8, while maintaining a processing speed of 42.8 frames per second. This research contributes significantly to the advancement of more efficient and precise outcomes power grid maintenance techniques, potentially reducing the need for manual inspections and enhancing overall system reliability. © 2024 SPIE.

关键词： image enhancement

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An Efficient Workload Distribution Mechanism for Tightly Coupled Heterogeneous Hardware 6th

An Efficient Workload Distribution Mechanism for Tightly Co...

引用

6th World conference on Smart Trends in systems, Security and Sustainability, WS4 2022

作者： Rivera-Alvarado, Ernesto Torres-Rojas, Francisco J. Computer Science Costa Rica Institute of Technology Cartago Costa Rica

ISBN: (纸本)9789811976599

The Accelerated processing Units (APUs) are tightly integrated heterogeneous computing platforms that contain a CPU and GPU in a single integrated circuit. As their internal computing resources use the same memory (RAM), they can efficiently share data structures. This opens up the opportunity for creating new types of specialized algorithms for the APU. In this research, we designed a specialized load distribution mechanism for the APU and used image rendering as our test load as it is a highly expensive computational task. We found out that our workload distribution mechanism provides better performance than conventional workload mechanisms for GPUs and multicore CPUs. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： APU Computer architecture Heterogeneous computing image rendering SoC System-on-chip Tightly coupled heterogeneous hardware Workload distribution

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Comparison of algorithms for contrast enhancement based on TOD assessments by convolutional neural networks 19

Comparison of algorithms for contrast enhancement based on T...

引用

conference on Electro-Optical and Infrared systems - Technology and Applications XIX

作者： Wegner, Daniel Kessler, Stefan Fraunhofer IOSB Gutleuthausstr 1 D-76275 Ettlingen Germany

ISBN: (纸本)9781510655461

A current approach for performance assessment of imagers is triangle orientation discrimination (TOD). This approach requires observers or human visual system (HVS) models to recognize equilateral triangles pointing in four different directions. imagers may apply embedded advanced digital signal processing (ADSP) for contrast enhancement, noise reduction, edge sharpening, etc. Unfortunately, applied methods are in general not documented and hence unknown. Within the last decades a vast amount of techniques for constrast enhancement has been proposed. There are some comparisons of such algorithms for few images and figures of merit. However, many of these figures of merit cannot assess usability of altered image content for specific tasks such as object recognition In this work different algorithms for contrast enhancement are compared in terms of TOD assessments by convolutional neural networks (CNN) as models. These models are trained by artificial images with single triangles. Many methods for contrast enhancement highly depend on the content of the entire image. Therefore, the images are superimposed by natural backgrounds with varying standard deviations to provide different signal-to-background ratios. Then these images are degraded by Gaussian blur and noise representing degradational camera effects and sensor noise. Different algorithms are applied, such as the constrast-limited adaptive histogram equalization or local range modification. Then accuracies of the trained models on these images are compared for different ADSP algorithms. Accuracy gains for low signal-to-background ratio and sufficiently large triangles are found, while impairments are found for high signal-to-background ratio and small triangles. Finally, implications of replacing triangles by real target signatures when using such ADSP algorithms are discussed. The results can be a step towards the assessment of those algorithms for generic target recognition.

关键词： CNN TOD image contrast enhancement advanced digital signal processing imager assessment method comparison

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Single image Depth Estimation Based on Improved U-NET 3

Single Image Depth Estimation Based on Improved U-NET

引用

3rd International conference on Digital Society and Intelligent systems, DSInS 2023

作者： Wang, Yingkang Zhang, Yicheng Zhao, Wei Liu, Congbiao Tianjin Vocational and Technical Normal University National and Local Joint Engineering Laboratory of Automotive Mold Intelligent Manufacturing Technology Tianjin300350 China National-Local Joint Engineering Laboratory of Intelligent Manufacturing Oriented Automobile Die & Mould Tianjin300350 China

ISBN: (纸本)9798350331387

para>In response to the problems of low accuracy and complex network structure in existing deep learning based monocular image depth estimation algorithms, an improved U-NET [1] monocular image depth estimation method based on dense skip connections is proposed. The traditional U-NET network has the problem of depth distortion when processing depth estimation tasks. In response to this problem, this algorithm proposes skip connections to achieve multi-scale feature fusion from encoder to decoder;And introduce attention mechanism into the decoder to further improve the accuracy of depth estimation. The training and verification were carried out on the indoor data set KITTI [2] and the outdoor data set NYU Depth V2 respectively. The experimental results show that compared with the advanced algorithm DORN, all indicators have been improved to varying degrees, in which the root-mean-square error has been reduced by 24.1% and the square relative error has been reduced by 15.2%. © 2023 IEEE.

关键词： Complex networks

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Synthetic image Generation With a Fine-Tuned Latent Diffusion Model for Organ on Chip Cell image Classification 26

Synthetic Image Generation With a Fine-Tuned Latent Diffusio...

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26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Ivanovs, Maksims Leja, Laura Zviedris, Karlis Rimsa, Roberts Narbute, Karina Movcana, Valerija Rumnieks, Felikss Strods, Arnis Gillois, Kevin Mozolevskis, Gatis Abols, Arturs Kadikis, Roberts Institute of Electronics and Computer Science Riga Latvia Cellbox Labs Riga Latvia Latvian Biomedical Research and Study Centre Riga Latvia

ISBN: (纸本)9798350304985

Augmentation of the datasets of authentic microscopy images with synthetic images is a promising solution to the problem of the limited availability of biomedical data for training deep neural network (DNN) based classifiers. In the present study, we use a text-to-image latent stable diffusion model fine-tuned by means of low-rank adaptation (LoRA) to augment a small dataset of the images of organ on chip cells. While the resulting synthetic images appear quite similar to the authentic images on which the low-rank adaptation was performed, we find that neither training the EfficientNetB7 DNN model solely on the synthetic data nor augmentation of the real-world dataset with different proportions (10, 25, 50, and 75 percent) of these data leads to the improvement of the accuracy of the model. The findings of our study suggest that a further exploration of the low-rank adaptation options is needed to fully use the capacity of latent diffusion models for the synthesis of biomedical images. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Neural network models

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image Captioning with Reinforcement Learning 2

Image Captioning with Reinforcement Learning

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2nd IEEE International conference on Computer vision and Machine Intelligence, CVMI 2023

作者： Verma, Anand Agarwal, Saurabh Arya, K.V. Petrlik, Ivan Esparza, Roberto Rodriguez, Ciro ABV-IIITM Gwalior Department of Information Technology Madhya Pradesh 474015 India ABV-Indian Institute of Information Technology and Management Multimedia and Information Security Research Group Department of Computer Science and Engineering Gwalior474015 India National University Federico Villarreal Faculty of Industrial and Systems Engineering Lima Peru National University Mayor de San Marcos Faculty of Systems Engineering and Informatics Peru

ISBN: (纸本)9798350305142

image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO. © 2023 IEEE.

关键词： Reinforcement learning

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