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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391-400 订阅

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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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An Enhanced Deep Learning algorithms for image Recognition and Plant Leaf Disease Detection 2

An Enhanced Deep Learning Algorithms for Image Recognition a...

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2nd International conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Asha, V. New Horizon College of Engineering Department of Master of Computer Application Bengaluru India

ISBN: (纸本)9798350325799

Object recognition in photographs has only been possible with the advent of modern deep learning (DL) techniques. Following its success in other fields, DL techniques are increasingly being used to a broad variety of agricultural operations. Due to its intrinsic complexity, image-based plant disease detection is difficult to solve. Targeted management relies on more than just detection and requires accurate identification of each species. An automated disease detection system aids the farmer by providing a speedy and accurate diagnosis of plant illness. Accelerating agricultural diagnostics requires the development of an automated system for identifying plant leaf diseases. Using machine learning and image processing, the authors of this study outline a strategy for detecting leaf blight. A photo of a leaf may be fed into this framework. In a first processing stage, noise is eliminated from the leaf images. The mean filter may be used to get rid of noise. Histogram equalisation is a method for enhancing image quality. Segmentation is a technique used in photography to break a large image down into more manageable sections. This helps to establish the picture's limits. The image is broken up using the BIRCH (Balance Iterative Reducing and Clustering utilising Hierarchies) technique. For this purpose of feature extraction, fine grained classification model, a convolutional neural network is used. After that, the pictures are categorized using a variety of methods, one of which is RCNN (Region based Convolutional neural network). Using this approach, this study discovered the split ratio, computational time, and as a result RCNN has resulted in a high accuracy of 0.98 when detecting the leaf compared to other algorithms. © 2023 IEEE.

关键词： Iterative methods

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Application-Specific image and Video processing Techniques for Security and Surveillance 15

Application-Specific Image and Video Processing Techniques f...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kalanandhini, G. Sidhu, Amritpal Malathi, H. Dedgaonkar, Suruchi G. Singh, K. Ranjith Rampal, Sourav Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Karnataka Bangalore India Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore641021 India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India

ISBN: (纸本)9798350370249

Utility-particular photo and video processing strategies for protection and surveillance are a set of algorithms and techniques used to procedure and examine pictures and motion pictures captured by protection and surveillance systems. These techniques are used to discover and understand items and identify suspicious activities and music-shifting objects in actual time. Common strategies include motion detection, item recognition, facial recognition, item monitoring, and anomaly detection. Those strategies are used to enhance the accuracy and efficiency of security and surveillance structures and to provide extra dependable and relaxed monitoring. ©2024 IEEE.

关键词： Video signal processing

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Research on deep learning-based channel estimation algorithm in 5G high-speed mobile scenario

Research on deep learning-based channel estimation algorithm...

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2024 International conference on Signal processing and Communication Security, ICSPCS 2024

作者： Teng, Wang Jie, Chen Honghe Vocational and Technical College Mengzi Yunnan661199 China

ISBN: (纸本)9781510681699

In the high-speed mobile environment supported by the fifth-generation mobile communication technology, higher vehicle speeds, more frequent switching and wider bandwidth make the design of high-speed mobile communication systems more challenging. High-quality wireless communication technology is needed to support future high-speed mobile scenarios to achieve low-latency and highly reliable communication, among which anti-Doppler frequency shift technology is the key. Based on this, this paper proposes a Rayleigh channel estimation algorithm based on image super-resolution (SR) network. This method models the channel response matrix obtained by the LS method as a two-dimensional image, uses the SR network and image restoration (image restoration, IR) technology for learning optimization, and obtains the channel response at the unknown symbol. The simulation results show that the proposed algorithm is significantly better than the traditional Linear Minimum Mean Square Error (LMMSE) method. Compared with other SR-based channel estimation algorithms under low Signal to Noise Ratio (SNR), it has a 3dB gain and a 5dB gain under high SNR. © 2024 SPIE.

关键词： image reconstruction

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Communication Protocol Between Embedded Computers and Fog Computing Environment for image processing 11

Communication Protocol Between Embedded Computers and Fog Co...

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11th Mediterranean conference on Embedded Computing (MECO) / 3rd Summer School on Cyber-Physical + systems and Internet of Things (CPS and IoT)

作者： Zhukova, Nataly Subbotin, Alexey Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg Russia St Petersburg Electrotech Univ LETI St Petersburg Russia

ISBN: (纸本)9781665468282

In this article a new data transmission protocol between embedded computers and the fog computing environment for image processing is considered. The possibilities of using fog environment for intelligent processing of video are revealed. The problems of transmission protocols are indicated. A comparison of existing video processing systems is given. The place of embedded computers in intelligent video surveillance systems and ways of upgrading these systems are considered. Data from the devices is collected using embedded computers and visualized using IoT technologies. The developed data transmission protocol allows processing images in the fog and/or cloud for intelligent video surveillance systems. It assumes packet data transmission of series of frames with additional information that are processed using machine learning algorithms and neural networks. The effectiveness of the new systems is shown. As an example, the problem of processing video coming from cameras used in the subway to determine damage of the escalator tape and steps is considered. systems for image processing that implement the proposed protocol can be used not only in the subway, but also in many other areas where Internet of things technologies are supported.

关键词： Internet of Things mobile technology communication protocol image processing embedded computers intelligent video surveillance video processing system fog computing environment

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The astronomical object recognition and its near-zero motion detection in series of images by in situ modeling 29

The astronomical object recognition and its near-zero motion...

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29th International conference on systems, Signals and image processing (IWSSIP)

作者： Khlamov, Sergii Savanevych, Vadym Tabakova, Iryna Trunova, Tetiana Kharkiv Natl Univ Radio Elect Kharkiv Kharkiv Ukraine Kharkiv Natl Univ Radio Elect Kharkiv Dept Syst Engn Kharkiv Ukraine Kharkiv Natl Univ Radio Elect Dept Media Syst & technol Kharkiv Ukraine

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

In this work we have presented the approach for the astronomical object recognition and detection of its near-zero motion in the series of images and compressed videos from the "live" (online) data stream using the developed method of in situ modeling. We have described all required preconditions and constants for the method of in situ modeling. The substitutional method implemented on a maximum likelihood criterion and the method implemented on the Fisher f-criterion were selected as the detection algorithms for the near-zero motion of objects for the current research. We have analyzed the several quality indicators of detection of the objects near-zero motion by applying the statistical imitation modeling techniques and using the conditional probability of the true detection.

关键词： in situ modeling OLS-evaluation F-test maximum likelihood criterion hypothesis image recognition motion detection series of images machine vision

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Smart Healthcare: A Logical Development of Medical image processing Based Sleep Apnea Disease Detection Using Elevated Neural Classification Principle

Smart Healthcare: A Logical Development of Medical Image Pro...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Jagadeesh, P. Radhamani, V. Ravi, T. Charulatha, G. Shahare, D.Y. Aljawawdeh, Hamzeh J. Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering college Department of Electronics and Communication Engineering Avadi Chennai India Rajalakshmi Engineering College Department of Ece Chennai India Sathyabama Institute of Science and Technology Department of Ece Chennai India Selvam College of Technology Department of Biomedical Engineering Namakkal India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of Software Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Entirely common sleep disorder is the dangerous wakefulness (Apnea) of breathing during snoring. In this study, we explore the possibility of how medical image processing can be useful and accurate method for sleep disordered breathing detection. The new approach named as Sleep Apnea Analyzing Strategy (SAAS) is firstly tested with the help of existing method namely image base Sleep Apnea Analyzer (ISAA) that use less number or data to be processed and hence running on portable device delivering results much faster than other available methods, both being cross-validated against each other. Sleep Apnea image-based Detector using Ephysiology - A new Algorithm (SAAS) We rely on sophisticated image processing techniques to extract relevant characteristics from medical images (such as x-rays, CT scans or MRIs) which are indicative of patterns typically associated with sleep apnea. The proposed methodology includes image preprocessing to improve the quality of images, feature extraction for quantification and characterization of all relevant information during OSA trials in GUI interface through well-known classification algorithms to classify patients with at least 20 de-saturations without sleep apnea. Our research aims to help in the creation of automatic diagnostic methods that will lead to early detection and treatment for sleep apnea. Our objective with this work is to provide a fair evaluation of the proposed technique in terms of sensitivity, specificity and accuracy through using a large dataset. © 2024 IEEE.

关键词： Sleep research

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Registration of Longitudinal Spine CTs for Monitoring Lesion Growth

Registration of Longitudinal Spine CTs for Monitoring Lesion...

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conference on Medical Imaging - image processing

作者： Sanhinova, Malika Haouchine, Nazim Pieper, Steve D. Wells, William M., iii Balboni, Tracy A. Spektor, Alexander Mai Anh Huynh Guenette, Jeffrey P. Czajkowski, Bryan Caplan, Sarah Doyle, Patrick Kang, Heejoo Hackney, David B. Alkalay, Ron N. Harvard Med Sch Boston MA 02115 USA Beth Israel Deaconess Med Ctr Boston MA 02215 USA Brigham & Womens Hosp 75 Francis St Boston MA 02115 USA Isomics Inc Cambridge MA USA Dana Farber Canc Inst Boston MA 02115 USA

ISBN: (纸本)9781510671577;9781510671560

Accurate and reliable registration of longitudinal spine images is essential for assessment of disease progression and surgical outcome. Implementing a fully automatic and robust registration is crucial for clinical use, however, it is challenging due to substantial change in shape and appearance due to lesions. In this paper we present a novel method to automatically align longitudinal spine CTs and accurately assess lesion progression. Our method follows a two-step pipeline where vertebrae are first automatically localized, labeled and 3D surfaces are generated using a deep learning model, then longitudinally aligned using a Gaussian mixture model surface registration. We tested our approach on 37 vertebrae, from 5 patients, with baseline CTs and 3, 6, and 12 months follow-ups leading to 111 registrations. Our experiment showed accurate registration with an average Hausdorff distance of 0.65 mm and average Dice score of 0.92.

关键词： image registration Spine Data modeling 3D modeling Automatic alignment Bone Computed tomography Mixtures Reconstruction algorithms Rigid registration

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Design and Development of DenseNet Transfer Learning Model for image Classification 4

Design and Development of DenseNet Transfer Learning Model f...

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4th International conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Attri, Vikas Kapoor, Ankush Singh, Simranjeet Sharma, Payal University School of Computing Rayat Bahra University Mohali India University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9798350387490

Integrating deep learning with computer vision technologies and analysing their application efficiency is the main emphasis of this research. Building hierarchical neural networks, a key component of deep learning, allows for end-to-end feature learning and semantic image processing, marking a significant advancement in the field. When training deep learning algorithms, the successes in computer vision give a solid foundation. Combining these two areas has produced cutting-edge computer vision systems that outperform the state-of-the-art in object detection and machine vision image classification. This research uses a combination of everyday image classification situations to examine the deep neural network models' better performance, draw attention to their shortcomings in interpretability and generalizability, and suggest ways to enhance them in the future. In general, advancements in computer vision will be propelled by the expanding use of deep learning and its efficient integration with large amounts of visual data. This trend will pave the way for intelligent machine vision systems. After briefly introducing deep learning, this paper proposed the most recent model used for deep learning picture classification that generates an accuracy of 96.3 %. Lastly, the accuracy of our deep learning model compared with other models used in the literature for the most challenging datasets, CIFAR-10, imageNet, and CIFAR-100. © 2024 IEEE.

关键词： Machine vision

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