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

共 12901 条记录，以下是351-360 订阅

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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...

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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...

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

Fractal Image Compression Using Discrete Cosine Transform wi...

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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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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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Research on fault and display defect detection system of watt-hour meter based image processing 5

Research on fault and display defect detection system of wat...

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5th International conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Sun, Yanling Dong, Xianguang Dai, Yanjie Wang, Sixian Xing, Yu Zhai, Xiaohui Zhang, Songhui Sun, Kai State Grid Shandong Electric Power Company Shandong Jinan250001 China

ISBN: (纸本)9781510691667

With the rapid development of smart grids, electric energy meters, as an important component of power equipment, have a direct impact on the reliability and efficiency of power systems. In response to the limitations of traditional fault detection methods in terms of efficiency and accuracy, an automatic detection system for display defects in smart meters based on image processing technology is researched and designed. An efficient fault detection framework is constructed by combining convolutional neural network (CNN) feature extraction and support vector machine (SVM) classification algorithms. Experimental results demonstrate that the system performs excellently in multiple performance indicators. In practical applications, the fault detection accuracy reaches 93.8%, with detection delay and system response time reduced to 180 milliseconds and 220 milliseconds, respectively. Compared with other combined algorithms, the processing capability of the system is significantly improved, capable of processing 1650 meter images per hour. In tests involving various fault types, the system achieves F1 scores of 0.90 and 0.86 for "Blurry Display" and "Cracked Digits" faults, respectively, demonstrating excellent classification ability and high robustness. The proposed smart meter fault detection system in this study can effectively enhance the accuracy and real-time performance of fault identification, providing reliable technical support for the operation and maintenance of smart grid equipment and possessing significant application value. © 2025 SPIE.

关键词： Watt hour meters

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Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play image Reconstruction 38

Provably Robust Score-Based Diffusion Posterior Sampling for...

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38th conference on Neural Information processing systems, NeurIPS 2024

作者： Xu, Xingyu Chi, Yuejie Carnegie Mellon University United States

In a great number of applications, the goal is to infer an unknown image from a small number of noisy measurements collected from a known and possibly nonlinear forward model describing certain sensing or imaging modality, which is often ill-posed. Score-based diffusion models, thanks to their impressive empirical success, have emerged as an appealing candidate of an expressive prior in image reconstruction. In order to accommodate diverse tasks at once, it is of great interest to develop efficient, consistent and robust algorithms that incorporate unconditional score functions of an image prior distribution in conjunction with flexible choices of forward models. This work develops an algorithmic framework for employing score-based diffusion models as an expressive data prior in nonlinear inverse problems with general forward models. Motivated by the plug-and-play framework in the imaging community, we introduce a diffusion plug-and-play method (DPnP) that alternatively calls two samplers, a proximal consistency sampler based solely on the likelihood function of the forward model, and a denoising diffusion sampler based solely on the score functions of the image prior. The key insight is that denoising under white Gaussian noise can be solved rigorously via both stochastic (i.e., DDPM-type) and deterministic (i.e., DDIM-type) samplers using the same set of score functions trained for generation. We establish both asymptotic and non-asymptotic performance guarantees of DPnP, and provide numerical experiments to illustrate its promise in various tasks. To the best of our knowledge, DPnP is the first provably-robust posterior sampling method for nonlinear inverse problems using unconditional diffusion priors. © 2024 Neural information processing systems foundation. All rights reserved.

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Hardware-aware, deep-learning approaches for image denoising and star detection for star tracker sensor 10

Hardware-aware, deep-learning approaches for image denoising...

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

作者： Guesmi, Besma Moloney, David Ubot Technol Dublin Ireland

ISBN: (纸本)9798350373981;9798350373974

In recent years, Deep Neural Networks (DNNs) approaches have outperformed traditional techniques for several computer vision problems. This has been made possible by the increase of computational resources represented by Graphical processing Units (GPU) that allow training using large datasets and the availability of deep learning accelerators for inference. On the other hand, the attitude determination accuracy requirements for spacecraft are increasing. The most accurate attitude determination sensor for spacecraft is the so-called star sensor or star tracker. With the increase in lowcost satellite platforms such as CubeSats, research into the improvement of star sensor accuracy for low-power and low-cost sensor architectures remains a relevant subject. In this context, we examine several methods for noise reduction and star detection for improving centroiding performance. More specifically, an efficient and robust denoising method for star images using an Auto-Encoder (AE) is proposed. This method enhances the image quality for systems sensitive to noise. Furthermore, an accurate and lightweight algorithm based on an existing YOLO (You Only Look Once) architecture is proposed to detect the location of stars in the image. In this work, the YOLO bounding boxes are used to describe the space region around the stars. Subsequently, the star centroid within the bounding box is computed using the COG (Center Of Gravity) method. This method removes the need for centroiding algorithms sliding over the entire image area. An extensive comparison of the proposed denoising technique with other traditional filters confirms that the proposed method resists all noise models and reconstructs well the corrupted images. Experiments show that the proposed YOLO-based star detector achieves high accuracy with a lightweight architecture without any extra latency.

关键词： star tracker attitude determination deep learning image preprocessing denoising detection centroiding YOLOv5 autoencoder low-power

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