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

共 13664 条记录，以下是1551-1560 订阅

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Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency For Blind image Quality Assessment

Attention Down-Sampling Transformer, Relative Ranking and Se...

引用

IEEE International conference on image processing

作者： Mohammed Alsaafin Musab Alsheikh Saeed Anwar Muhammad Usman Department of Industrial and Systems Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Electrical Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The no-reference image quality assessment is a challenging domain that addresses estimating image quality without the original reference. We introduce an improved mechanism to extract local and non-local information from images via different transformer encoders and CNNs. The utilization of Transformer encoders aims to mitigate locality bias and generate a non-local representation by sequentially processing CNN features, which inherently capture local visual structures. Establishing a stronger connection between subjective and objective assessments is achieved through sorting within batches of images based on relative distance information. A self-consistency approach to self-supervision is presented, explicitly addressing the degradation of no-reference image quality assessment (NR-IQA) models under equivariant transformations. Our approach ensures model robustness by maintaining consistency between an image and its horizontally flipped equivalent. Through empirical evaluation of five popular image quality assessment datasets, the proposed model outperforms alternative algorithms in the context of no-reference image quality assessment datasets, especially on smaller datasets. Codes are available at https://***/mas94/ADTRS

关键词： image quality Degradation Adaptation models Visualization image transformation Transformers Feature extraction

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

引用

Signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Jun Wang Yunhan Zhou Min Fu School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector Machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance.

关键词： Support vector machines Accuracy image recognition Surveillance Signal processing algorithms Signal processing Safety Convolutional neural networks Meteorology Residual neural networks

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Enhancing consistency-based image generation via adversarially-trained classification and energy-based discrimination 24

Enhancing consistency-based image generation via adversarial...

引用

Proceedings of the 38th International conference on Neural Information processing systems

作者： Shelly Golan Roy Ganz Michael Elad Technion

ISBN: (纸本)9798331314385

The recently introduced Consistency models pose an efficient alternative to diffusion algorithms, enabling rapid and good quality image synthesis. These methods overcome the slowness of diffusion models by directly mapping noise to data, while maintaining a (relatively) simpler training. Consistency models enable a fast one- or few-step generation, but they typically fall somewhat short in sample quality when compared to their diffusion origins. In this work we propose a novel and highly effective technique for post-processing Consistency-based generated images, enhancing their perceptual quality. Our approach utilizes a joint classifier-discriminator model, in which both portions are trained adversarially. While the classifier aims to grade an image based on its assignment to a designated class, the discriminator portion of the very same network leverages the softmax values to assess the proximity of the input image to the targeted data manifold, thereby serving as an Energy-based Model. By employing example-specific projected gradient iterations under the guidance of this joint machine, we refine synthesized images and achieve an improved FID scores on the imageNet 64x64 dataset for both Consistency-Training and Consistency-Distillation techniques.

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Multi-DIP: A General Framework for Unsupervised Multi-degraded image Restoration 28th

Multi-DIP: A General Framework for Unsupervised Multi-degrad...

引用

28th International conference on Neural Information processing

作者： Wang, Qiansong Hu, Xiao Wang, Haiying Men, Aidong Jiang, Zhuqing Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Natl Radio & Televis Adm Beijing 100045 Peoples R China

ISBN: (纸本)9783030922726;9783030922733

Most existing image restoration algorithms only perform a single task. But in the real world, the degradation pattern could be much more complex, such as blurred images that have been smudged or images with haze that have been blurred, and we call it multi-degradation. Many of these degenerations are coupled with each other, making it impossible to restore images by merely stacking the algorithms. In this paper, we propose Multi-DIP that uses DIP networks to solve the multi-degradation problem. We integrate multiple image restoration tasks into a unified framework. However, multi-degradation can cause difficulties for DIP networks to extract image priors. To alleviate this problem, we design a multi-scale structure to stabilize and improve the quality of generated images. We implement two image restoration tasks with the proposed DIP framework: deblur + inpainting and dehaze + deblur. Extensive experiments show that our proposed method achieves promising results for restoring multi-degraded images.

关键词： Deep image prior Multi-scale image restoration Multi-degradation

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Fuzzy Morphological processing Algorithm of Blood image

Fuzzy Morphological Processing Algorithm of Blood Image

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Abrorjon M. Turgunov Temur N. Narzullaev Marks Ya. Matyakubov Munira T. Payzieva Sevara J. Kamolova Nilufar N. Rakhmonova Department of Algorithms and mathematical modeling Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

This article, a morphological image processing algorithm is developed for early detection of viral hepatitis from microscopic blood images. It is mentioned that it can be used to accurately determine each boundary detail in the fuzzy morphological processing of the gray image of the blood image. Boundary detection leads to higher recognition results in neural network training. Compared to the work of a number of scholars, our method offers such performance, but it offers improved dynamics and flexibility in the formulation of linguistic boundary criteria, which can be a leading factor in the design of image processing systems with dynamic and flexible rules, such as Type 2 allows fuzzy rules, which offers an interesting alternative to currently common deep learning applications. The validated experimental results show the reliability of the modified method compared to the Chaira method. Mean, entropy and standard deviation were calculated. It turned out to be higher than the values of the Chaira method.

关键词： Training image processing Refining Medical services Reliability Blood X-ray imaging

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FPGA-Compatible Fog Rectification and Contrast Enhancement for Improved Vision algorithms in Foggy Environments

FPGA-Compatible Fog Rectification and Contrast Enhancement f...

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Intelligent Technologies (CONIT), International conference on

作者： Khadim Moin Siddiqui Anjali Chowdhary Rani Yadav Karandeep Yadav Sejal Verma Anurag Singh Surabhi Kanaujia Shiv Mani Tripathi Beer Singh Abhinav K. Gautam Electrical & Electronics Engineering S.R. Institute of Management & Technology Lucknow India Electronics & Communication Engineering S.R. Institute of Management & Technology Lucknow India Electronics & Communication Engineering Department S.R. Institute of Management & Technology Lucknow India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

abstract-Fog rectification is a crucial preprocessing step in enhancing image quality for applications like autonomous driving and object recognition, particularly in foggy and hazy conditions. This paper presents a streaming fixed-point implementation of a fog rectification and contrast enhancement algorithm tailored for FPGA deployment aiming to enhance visibility and contrast in foggy images. The fog rectification involves the dark channel of the image calculating the airlight map, refining it with filters and restoring the defogged image. Contrast enhancement is achieved through RGB to gray conversion and contrast stretching. The proposed algorithm is designed to efficiently process foggy RGB images, removing haze and enhancing contrast. The implementation utilizes fixed-point blocks from Vision HDL Toolbox for FPGA compatibility and employs a streaming pixel format for real-time processing. Simulation results demonstrate the effectiveness of the algorithm in improving image quality for vision algorithms in foggy environments.

关键词： image quality Simulation Refining Streaming media Real-time systems image restoration Object recognition

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Optimization of Micro-Object Identification with Factorization and Correction Mechanisms for Distorted image Points

Optimization of Micro-Object Identification with Factorizati...

引用

International Russian Smart Industry conference (SmartIndustryCon)

作者： Isroil I. Jumanov Rustam A. Safarov Olimjon I. Djumanov Intelligent systems and computer technologies Samarkand state university Samarkand Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The scientific and methodological foundations for the optimal identification of micro-objects with mechanisms for using information structural components, features, statistical and dynamic characteristics of images have been developed. Mechanisms for tracking, detecting and correcting distorted points, factorizing the structural components of the image, filtering, segmentation, tracking distorted points under conditions of a priori insufficiency, parametric uncertainty, and low reliability of information processing are proposed. Methods for converting the initial brightness of a point into homogeneous objects as a function of two variables have been studied and nonlinear optimization functionals have been used. Mechanisms for factorization of the structural components of the image in the form of an indicator of linear connectivity, “neighborhood relationship” of three separate algorithms have been implemented. An alternative image factorization algorithm has been developed. A comparative analysis of effectiveness was carried out. The effectiveness of the mechanisms for detecting and correcting distorted points was studied using the criterion of the probability of undetected errors, the root-mean-square error, according to Shannon's estimates, the average level of intensity of the points, and the total compactness parameter. Rank estimates of integral indicators of image factorization were obtained for 30 images with 100 tests. A software package for visualization, recognition, and classification of images of microobjects has been developed, the implementations of which have been tested using real medical diagnostic data.

关键词： image segmentation Uncertainty Software packages Nonlinear distortion Brightness Noise Information processing Reliability Object recognition Optimization

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Dual-Source CBCT for Larger Longitudinal Coverage: System Design and image Reconstruction

Dual-Source CBCT for Larger Longitudinal Coverage: System De...

引用

Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Xusheng Zhang Tianling Lyu Yijie Shi Xinyun Zhong Zhan Wu Yan Xi Shijie Wang Yang Chen Wentao Zhu School of Software Southeast University Nanjing China Zhejiang Lab Hangzhou China Laboratory of Image Science and Technology the School of Computer Science and Engineering Southeast University Nanjing China First-Imaging Tech. Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The application of CBCT systems in intraoperative environments has become increasingly common, but concurrent CBCT systems are unsuitable for situations that require a large longitudinal imaging FoV, such as orthopedics. To increase longitudinal coverage, we developed a dual-source CBCT (DS-CBCT) system in which two ray sources are symmetrically placed along the central plane. After further analyzing its geometric characteristics, we propose an analytical reconstruction algorithm termed DT-FDK specialized for DS-CBCT, which combines cone-beam rebinning and two rays with smaller cone angles among the four conjugate rays to further suppress cone beam artifacts. The system design and reconstruction algorithm are tested on both simulated and real-scanned data. Results show that the DS-CBCT can expand the effective imaging volume by 37.1% compared to single-source CBCT systems. The reconstructed images by DT-FDK also show improved image quality compared to traditional reconstruction algorithms.

关键词： image quality Orthopedic surgery Imaging Reconstruction algorithms Engineering in medicine and biology image reconstruction System analysis and design

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Mulsemedia Data Representation Based on Multi-image Concept 13th

Mulsemedia Data Representation Based on Multi-image Concept

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13th International conference on Interactive Mobile Communication, Technologies and Learning (IMCL)

作者： Sulema, Yevgeniya Bhattacharya, Abhishek Murray, Niall Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine Athlone Inst Technol Athlone Ireland

ISBN: (纸本)9783030499327;9783030499310

MulseMedia technology enables operation with multimodal data sets that requires their complex representation. In this paper, we propose a mathematical background of MulseMedia data representation. The application of both Algebraic System of Aggregates and Multi-image Concept allows efficient representation and processing of MulseMedia data in computer systems as well as encourages development of new algorithms for data processing, including data compression and data modelling.

关键词： Mulsemedia Multimodal data Multi-image Algebraic system of aggregates

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image Based Plant Disease Classification with Cause and Severity Analysis and Remedial Action

Image Based Plant Disease Classification with Cause and Seve...

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Intelligent systems for Communication, IoT and Security (ICISCoIS), International conference on

作者： Gopika N Rani Divya Darshini R Monirhithikka S P Samsitha Banu S Saravu Miththira V C Subashini B Department of CSE PSG College of Technology Coimbatore India

Unlike diseases of the human body, plant diseases don't camouflage themselves within the body of the crop. The leaves reflect the infection with a change in color, shape, texture or a combination of the three. Hence, predictive modelling of the leaf images dataset using machine learning can help detect the disease. Mostly the image processing algorithms is used for predicting the diseases. The Random forest algorithm is used for color segmentation and Convolutional neural networks mainly focuses on disease classification. Vision based plant infection showed efficient results and promising performance. The features are extracted such that they are feasible for a severity analysis. The percentage of severity is extracted from the percentage which is 94.6% of the classification accuracy after which suitable remedial actions are suggested. This offers a comprehensive strategy for addressing the threat of crop diseases that lower agricultural output and waste. (Choice of crop depends on accessibility of dataset).

关键词： Plant diseases Machine learning algorithms image color analysis Shape Crops Feature extraction Prediction algorithms

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