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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381-390 订阅

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FPGA-Based High-Speed Remote Sensing Satellite image Data Transmission System 10th

FPGA-Based High-Speed Remote Sensing Satellite Image Data Tr...

引用

10th International conference on Signal and Information processing, Network and Computers, ICSINC 2022

作者： He, Yifeng Sun, Peng Liu, Qian Zhang, Hao Zhao, Dayang Xi’an Institute of Space Radio Technology Xi’an710100 China

ISBN: (纸本)9789811999673

At present, with the increasing number of remote sensing satellite systems established in China, a large amount of remote sensing satellite image data has been obtained. Based on FPGA, this paper studies the transmission of high-speed remote sensing satellite image data, studies the image characteristics, image preprocessing methods and image detection principles of remote sensing satellite images, and analyzes various methods of image preprocessing. For different targets and different image backgrounds, the specific processing algorithms are also different. Only by flexibly using the basic theory of image processing can a good image processing effect be achieved. Among them, the fast connected region labeling algorithm designed and implemented can realize the merging of equivalent pairs while scanning. The results show that the system can reliably implement the algorithm in this paper, complete the processing of image data, and meet the requirements of the project. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Field programmable gate arrays (FPGA)

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Enhancing image Security Using Legacy-Based Encryption With Chaotic Tent Map and Memristor 26

Enhancing Image Security Using Legacy-Based Encryption With ...

引用

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

作者： Elnoamy, Omar Gabr, Mohamed Korayem, Youssef Alexan, Wassim El-Aasser, Minar The German University in Cairo Faculty of MET Cairo Egypt The German University in Cairo Faculty of IET Cairo Egypt

ISBN: (纸本)9798350304985

The development and utilization of effective image encryption techniques is seeing an unprecedented need with the advancements in multimedia production and exchange over unsecured networks. In a simultaneous manner, chaotic functions are increasingly being used by scholars to effectuate chaos in a deterministic manner, to develop pseudo-random bit-streams for the construction of encryption keys and substitution boxes. This work proposes the utilization of a 3-stage image encryption scheme. The Tent chaotic map is used in the first stage, followed by a legacy cellular automata S-box in the second stage, and finally a Memristor chaotic system in the third stage. Performance evaluation shows superior entropy performance and comparable values to the literature in terms of other metrics. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： Memristors

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A Certain Investigation on Undersea Water image Object Detection and Classification Using Artificial Intelligence algorithms 3rd

A Certain Investigation on Undersea Water Image Object Detec...

引用

3rd International conference on Computing and Communication Networks, ICCCN 2023

作者： Sandhya, Kaipa Arumugam, Jayachandran Presidency University Bangalore India

ISBN: (纸本)9789819708918

The increase in the usage of marine resources in recent years has drawn attention toward the underwater image processing research field;however, underwater images face some of the challenges like severe absorption and scattering of water, low contrast, monotonous color, etc., and make a complicated process. In this paper, we made a comprehensive study related to underwater image processing in recent years. The study includes both machine learning and deep learning approaches used for the detection of underwater objects. This study also presents the current scenarios along with future trends and provides better insights into the research direction in underwater image processing. Recently, artificial intelligence (AI) techniques are widely used for the detection and classification process of undersea water object detection and they demonstrated good outcomes. This study categorizes AI into machine learning and deep learning techniques. This study analyzed 31 papers related to underwater image object detection from various publishers like IEEE, Springer, Elsevier, Taylor and Francis, etc. More survey analysis is based on the study of the deep learning model. Hence, this study presents the advantages and the disadvantages of each model with open research challenges of undersea water object detection and classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Water absorption

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Synergistic Integration of MRI Prior for Enhanced Photoacoustic Tomography: Advancing Biomedical Imaging Quality 12th

Synergistic Integration of MRI Prior for Enhanced Photoacous...

引用

12th International conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Krishna Satya Varma, M. Yamini Priyanka, S. Department of IT SRKR Engineering College Chinaamiram Andhra Pradesh Bhimavaram India

ISBN: (纸本)9789819601424

In this research endeavor, we introduce a pioneering strategy to enhance the fidelity of photoacoustic tomography (PAT) images, addressing prevalent artifacts and distortions stemming from acoustic and optical properties of biological tissues. Our approach capitalizes on the synergy between PAT and magnetic resonance imaging (MRI), exploiting MRI's wealth of information on tissue anatomy and composition to refine PAT reconstructions. Through a meticulously designed framework, we integrate advanced image processing methodologies, spanning registration, fusion, and deep learning-based algorithms, to seamlessly merge MRI priors into the PAT reconstruction pipeline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image fusion

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Values Definition of the Leading Threshold of the Primary Process Colors by the Method of Color Separation and image Segmentation by Thresholding 6th

Values Definition of the Leading Threshold of the Primary Pr...

引用

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

作者： Drofova, Irena Adamek, Milan Tomas Bata Univ Zlin Fac Appl Informat Dept Secur Engn Zlin Czech Republic

ISBN: (纸本)9783031214370;9783031214387

image analysis and image segmentation methods are a current field of computer vision and are applied in many fields. This text deals with image segmentation and defining the leading edge of a color sample. For the experiment, a sample of material printed with process inks and their overprint was used. The printed material is a glossy white paper with primary print process colors Magenta and Yellow. The paper presents the methodology and procedure of image segmentation using the separation of colors into halftone values. From that values, this basic threshold is created and defined. A convulsion mask was used, the maximum target threshold of the given color was obtained from this basic halftone threshold, and its values were defined. Methods of image analysis of color originals offer the potential for use in the field of protection against forgery of works of art, especially in the area of graphic art techniques and their specific areas.

关键词： image analysis image segmentation image processing Processcolor Color separation Threshold

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Localization of Dengue Virus Likelihood Using image processing Technique 6

Localization of Dengue Virus Likelihood Using Image Processi...

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6th IEEE International conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Hosen, Md. Mahbub Islam, Md. Rahatul Tabassum, Faria Tonni, Syeda Thamina Noor, Malik Mohammad Nafiur Rahman, Shahid Northern University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Kyushu Institute of Technology Dept. of Human Intelligence Systems Wakamatsu Kitakyushu Japan Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh Advance Robotics Research Center Human Intelligence Lab Dhaka Bangladesh Canadian University of Bangladesh Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306613

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo V4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes;it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image. © 2024 IEEE.

关键词： aedes mosquito dengue-prone image processing yolo v4 tiny

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Fractal image Compression Using Discrete Cosine Transform with Domain Classification and Hexagonal Partitioning

Fractal Image Compression Using Discrete Cosine Transform wi...

引用

International conference on Power Engineering and Intelligent systems, PEIS 2024

作者： Kulkarni, Milind Bharadwaj, Rakhi Joshi Hedaoo, Anushka Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819767090

image compression has seen much advancement over the years, with new algorithms and techniques being developed to make image files smaller and easier to transmit and store using both lossy compression methods as well as lossless compression techniques. This paper proposes a lossy fractal image compression method using hexagonal partitioning and domain classification on the quantized image pixels to alleviate the image compression technique's processing complexity. Fractal image compression is a technique for compressing digital images that uses the idea of self-similarity, which is the concept that a pattern can repeat itself at different scales. Our proposed method consists of transforming the spatial domain to frequency domain using discrete cosine transform with quantization and then applying hexagonal partitioning on the square image pixels. A range block is compared with every other classified domain block and the domain block having the least root mean squared (RMS) error is mapped to the range block and replaced with it, storing the original contrast and brightness settings. In this manner, an encoding file is created which stores information of all the mappings, and then the original image is decoded iteratively until the output resembles the original image. To evaluate the performance of the suggested algorithm, numerous simulations are performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discrete cosine transforms

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Multi-Spectral Stereo-Imaging for Autonomous Farming Using Low-Cost Sensors 10

Multi-Spectral Stereo-Imaging for Autonomous Farming Using L...

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10th International conference on Control, Decision and Information Technologies (CoDIT)

作者： Homann, Hanna Boukhris, Fedi Stollmeier, Frank Ufheil, Manuel Will, Jens Christian Hannover Univ Appl Sci & Arts Fac Elect Engn Hannover Germany Fenntec GmbH Hannover Germany

ISBN: (纸本)9798350373981;9798350373974

This paper presents the development of a low-cost, multi-spectral stereo-imaging system designed for autonomous farming applications. With the growing interest in reducing manual labor in organic farming through technological means, especially in tasks such as weeding and plant health monitoring, there is a significant push towards the development of autonomous robotic systems. These systems often rely on advanced imaging techniques for accurate plant identification, health assessment, and navigation. We propose a novel stereo-camera system combining RGB and near-infrared (NIR) imaging to generate Normalized Difference Vegetation Index (NDVI) images for detailed plant health analysis. The system utilizes off-the-shelf components, including a StereoPi board and RaspberryPi 4 compute module, equipped with two IMX219-160 cameras. By removing the NIR blocking filter from one camera, we achieve a dual RGB and NIR imaging capability, enhancing plant-to-soil contrast and improving the detection sensitivity crucial for automated weeding applications. Our approach includes system calibration, stereo-disparity computation, and NDVI image formation, demonstrating the feasibility of stereo-matching between RGB and NIR images through semi-global matching. Preliminary results indicate that the system is capable of producing NDVI images with reasonable quantitative values, offering insights into plant health that could significantly benefit autonomous farming operations. Improved contrast is observed in the NIR band, promising improved robustness of AI-based plant detection algorithms. Further studies will explore the system's potential in automated plant health and growth assessment, as well as its integration into robotic weeding and harvesting systems.

关键词： Stereo image processing

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TPC-NAS: Simple and Effective Neural Architecture Search Based on Total Path Count

TPC-NAS: Simple and Effective Neural Architecture Search Bas...

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6th International conference on AI Circuits and systems (AICAS)

作者： Huang, Ming-Shan Chen, Pi-Chuan Chiueh, Tzi-Dar Natl Taiwan Univ Taipei Taiwan

ISBN: (纸本)9798350383638;9798350383645

With the explosive growth of neural network (NN) research and application areas, there is a pressing need to automate the NN model search process in order to attain optimal performance. Nevertheless, existing neural architecture search (NAS) algorithms are time-consuming, resource-intensive, and predominantly tailored for image-related applications. This paper presents the Total Path Count (TPC) score, a straightforward yet highly efficient accuracy predictor solely reliant on the architectural information of a model. The effectiveness of the TPC score is underscored by a robust rank correlation of 0.96 between TPC scores and the accuracies of CIFAR100 architectures. We further introduce TPC-NAS, a zero-shot NAS method that can complete a NAS task in under five CPU minutes without training and inference. TPC-NAS has found wide-ranging applications and it outperforms many other NAS solutions. In image classification, TPC-NAS achieves 78.3% imageNet top-1 accuracy with 399M FLOPs, while in object detection, it improves mAP by at least 2% over other NAS-derived models. Moreover, TPC-NAS successfully discovers a super-resolution architecture with < 300K parameters and achieves 32.09dB PSNR. In NLP, TPC-NAS delivers a model that matches tinyBERT's FLOPs but outperforms it by almost 10% in accuracy. These experiments illustrate TPC-NAS's ability to rapidly generate high-performance CNN/transformer architectures for various applications.

关键词： Neural Architecture Search (NAS) image Classification Super-Resolution Object Detection Natural Language processing

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Turkish Document image Classification

Turkish Document Image Classification

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International conference on Advanced Engineering, Technology and Applications, ICAETA 2024

作者： Nar, Meryem Tuğba Durukan, Gürcan Özcan, Abdullah Çakıl, Lütfü Kara, Hüseyin Omurca, Sevinç İlhan GoLive Yazılım Hizmetleri Istanbul Turkey Kocaeli University İzmit Turkey

ISBN: (纸本)9783031709234

Document image classification has gained extensive attention due to the rising number and types of scanned documents. Multi-modal architectures, processing image and text simultaneously, leverage the strengths of each modality. This study explores an efficient neural architecture for classifying scanned documents in a private company. The effectiveness of CNN-based deep learning and OCR algorithms in extracting textual and visual features is investigated. Different feature fusion methods are applied in the next stage to combine these extracted features. A multi-modal document image classifier is developed for companies managing a large number of scanned documents, delivering superior performance even with fewer and faint documents. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Transfer learning

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