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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4715 条记录，以下是121-130 订阅

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Multi-class Pathogenic Microbes Classification by stochastic Gradient Descent and Discriminative Fine-Tuning on Different CNN Architectures 3rd

Multi-class Pathogenic Microbes Classification by Stochastic...

引用

3rd International conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Jha, Nirajan Pant, Dibakar Raj Heikkonen, Jukka Kanth, Rajeev Institute of Engineering Tribhuvan University Kathmandu Nepal University of Turku Turku Finland Savonia University of Applied Sciences Kuopio Finland

ISBN: (纸本)9789819939312

The detection of microorganisms is an important task in the clinical microbiology field. It is equally important during the pandemic breakout. Pathogenic microbes’ orientational behavior helps in distinguishing them. However, it is not an easy task to classify them based on that behavior only. In this research work, image processing and CNN methods like Resnet50, DenseNet121, Inception-ResNetv2, and MobileNetv2 have been implemented to classify species of 33 different pathogenic microbes. The pathogenic microbes have stained with Gramm’s method to distinguish them as gram-positive and gram-negative bacteria. Lactobacillus, Staphylococcus, and Enterococcus are used for their intra-general classification. Further, stochastic gradient descent and fine-tuning are used to tune the learning rate. The result shows that 90.62 accuracies have been obtained using ResNet architecture for discriminative fine-tuning (DFT) and 92.96 accuracies have been obtained using stochastic gradient descent with the warm restarts (SGDR) approach. Similarly, 91.41 accuracies have been obtained using DenseNet 121 architecture for DFT and an accuracy of 98.7 has been obtained using stochastic gradient descent with warm restarts approach. Also, an accuracy of 96.88 has been obtained using MobileNet architecture for DFT and an accuracy of 99.2 using stochastic gradient descent with the warm restarts method. Further, Inception-ResNetV2 architecture has obtained an accuracy of 99.15 using DFT and 99.53 for the SGDR approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Architecture

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A Study on Deep Learning-Based image Target Identification Techniques 5th

A Study on Deep Learning-Based Image Target Identification T...

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5th International conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Wang, Yining College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China

ISBN: (纸本)9789819751808

This study aims to explore deep learning-based image target recognition methods to improve the performance of target detection and classification in the field of computer vision. The experiments use satellite-acquired remote sensing images of moving objects, including four target categories such as buses, cars, bicycles and ships. In model training, we performed parameter optimisation by stochastic gradient descent algorithm and set the training period to 12. To validate the model’s effectiveness, we compare the improved R-CNN (Region-based Convolutional Neural Network), traditional CNN (Convolutional Neural Network) and Faster-RCNN (Faster Region-based Convolutional Neural Network) models, and generate confusion matrices to evaluate their performance. The experimental results show that the improved model exhibits excellent performance in recognition of different types of target objects, especially for larger targets. This research provides reliable experimental support for the application of deep learning in the field of image target recognition and is expected to promote further development and innovation in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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ICCIP 2023 - 2023 the 9th International conference on Communication and Information processing

ICCIP 2023 - 2023 the 9th International Conference on Commun...

引用

9th International conference on Communication and Information processing, ICCIP 2023

ISBN: (纸本)9798400708909

The proceedings contain 96 papers. The topics discussed include: multi-scale pedestrian detection based on attention mechanism and feature fusion;statistical characteristics-based multi-scale image feature extraction;a fine-grained biometric image recognition method based on transformer;multi-scale global consistency residue feature enhancement based protein structure analysis;cover song identification technologies: a survey;license plate recognition algorithms for complex situations;a survey of facial detection and recognition methods for cartoon characters;a matrix coding genetic algorithm based on memristor for image edge detection;joint semantic graph and visual image retrieval guided video copy detection;space target spin motion recognition based on 3D convolutional networks;teaching neural networks to imitate human habits for recognizing anime characters;and design of intelligent indoor rowing training pool digitization system based on multisource data fusion.

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DEEPSAR: VESSEL DETECTION IN SAR imageRY WITH NOISY LABELS 29

DEEPSAR: VESSEL DETECTION IN SAR IMAGERY WITH NOISY LABELS

引用

IEEE International conference on image processing (ICIP)

作者： Pillai, Manu S. Bhattacharya, Abhijeet Baweja, Tanmay Gupta, Rohit Shah, Mubarak Indian Inst Sci Bengaluru India Int Inst Informat Technol Hyderabad India Indian Inst Technol Delhi India Univ Cent Florida Ctr Comp Vis Res Orlando FL USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Unlike traditional optoelectronic satellite imaging, Synthetic Aperture Radar (SAR) allows remote sensing applications to operate under all weather conditions. This makes it uniquely valuable for detecting ships/vessels involved in illegal, unreported, and unregulated (IUU) fishing. While recent work has shown significant improvement in this domain, detecting small objects using noisy point annotations remains an unexplored area. In order to meet the unique challenges of this problem, we propose a progressive training methodology that utilizes two different spatial sampling strategies. Firstly, we use stochastic sampling of background points to reduce the impact of class imbalance and missing labels, and secondly, during the refinement stage, we use hard negative sampling to improve the model. Experimental results on the challenging xView3 dataset show that our method outperforms conventional small object localization methods in a large, noisy dataset of SAR images. Source code for our method can be found at: https://***/manupillai308/DeepSAR

关键词： Remote sensing Object detection image segmentation Synthetic aperture radar

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Development of an Approach to Preprocessing of Black-and-White images for Electrical Equipment Diagnostics

Development of an Approach to Preprocessing of Black-and-Whi...

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2024 International Russian Automation conference, RusAutoCon 2024

作者： Kolodenkova, Anna Vereshchagina, Svetlana Favorskaya, Ekaterina Samara State Technical University Dept. of information technologies Samara Russia

Diagnostics of electrical equipment (EE) involves a variety of interrelated parameters which are analyzed using different types of diagnostics. For the most accurate assessment of the influence of parameters on EE performance and the influence on each other, it is necessary to analyze a large amount of data. But, despite the large amount of statistical data for EE diagnostics, the most illustrative type is still the graphical representation of the dependence of parameter changes in time or the dependence of parameters on each other. The scientific novelty of this approach is to develop an algorithm for preprocessing of black-and-white images with the complex use of the following methods: spline interpolation, component-by-component subtraction and adaptive binarization under conditions of heterogeneous data. Using methods in combination provides improving the informativeness in decision-making situations, as well as the knowledge completeness and reliability of conclusions regarding EE technical condition. The approach proposed for processing of black-and-white images is needed to determine the deviations in the EE operation and prevent its premature failure. © 2024 IEEE.

关键词： image enhancement

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A Study of Noise Reduction Algorithm Using statistical Optimization in Digital Holographic Microscopy 21

A Study of Noise Reduction Algorithm Using Statistical Optim...

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21st International Joint conference on Computer Science and Software Engineering (JCSSE)

作者： Jeong, Jongpil Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Iizuka Fukuoka Japan Hankyong Natl Univ IITC Sch ICT Robot & Mech Engn Anseong Kyonggi Do South Korea

ISBN: (纸本)9798350381771;9798350381764

Recently, due to the coronavirus era, we have a heightened need for fast and accurate diagnostics. To meet these requirements, there are many methods such as polymerase chain reaction, but they also require about from a few hours to a day. In contrast, a three-dimensional model of cells can be observed by simply extracting cells and photographing them with digital holographic microscopy, and in the case of diseases that can be classified by the shape of cells, diagnosis is possible in a few minutes. However, there are precise focal issues and noise in the high-frequency domain, respectively. Therefore, to resolve these issues, we propose an optimization method by modifying the threshold value of the high variance pixel averaging method in digital holographic microscopy.

关键词： image processing Digital holographic microscopy Noise reduction Computational visualization Three-dimensional(3D) Imaging statistical optimization

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Application Research of Neural Network-based Data Assessment and Prediction Method in statistical Analysis 8

Application Research of Neural Network-based Data Assessment...

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8th International conference on Intelligent Computing and Signal processing, ICSP 2023

作者： Li, Xiaolian Xu, Kun Men, Xiaoqian Lin, Zhe Zhao, Yixuan Wei, Daiming PetroChina Research Institute of Petroleum Exploration & Development Beijing100083 China

ISBN: (纸本)9798350302455

This article introduces the application of neural networks in evaluation and prediction tasks. Compared with traditional statistical methods and manual experience, neural networks have the characteristics of automatically learning based on data, can extract effective features from massive data, and achieve high accuracy prediction and complex pattern recognition of data through modeling and learning of deep abstract representations. Neural networks are widely used in petroleum data prediction, financial risk assessment, social network prediction, medical diagnosis, and other fields. This article aims to improve the accuracy and efficiency of prediction and decision-making based on the research of statistical evaluation and prediction methods based on neural networks. In addition, this article also introduces the indicator system and evaluation and prediction construction standards. The indicator system mainly describes the relationship between maintenance and indicators and their respective main attributes, including indicators, dimensions, measurement units, and unit indicators. The evaluation and prediction construction standards include production and operation data prediction and data quality prediction. The prediction platform provides various functions to support data prediction, such as prediction indicator management, prediction algorithm management, prediction algorithm simulation, prediction rule engine, production and operation warning, data quality prediction, prediction information release, and prediction problem management. The research and application of this article have important significance for promoting data mining, image recognition, natural language processing, and other fields. © 2023 IEEE.

关键词： Risk assessment

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statistical Feature Vector (SFV) For SAR ATR

Statistical Feature Vector (SFV) For SAR ATR

引用

2023 IEEE International Radar conference, RADAR 2023

作者： Picciolo, Michael L. Myrick, Wilbur L. Goldstein, J. Scott Anduril Industries WashingtonDC United States ENSCO Inc. SpringfieldVA United States Parsons Corp. CentrevilleVA United States

ISBN: (纸本)9781665482783

Historically, Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) has faced challenges and performance issues because the tendency is to treat SAR imagery the same way we treat optical imagery, whereas SAR complex images are fundamentally a clutter map that only resembles an image. As indicated by the name itself, the statistical Feature Vector (SFV) is a radar phenomenology-based ATR engine that can map many image processing enhancements to the radar domain, while also including radar unique capabilities that can improves SAR ATR. An SFV is based on information theoretic compression of the SAR target formed from a complex Wiener filter that exploits the holographic nature of SAR images. For example, the Multistage Wiener Filter (MWF) may be used that determines the most compressed scattering space (potentially statistical combinations of scattering center elements) that represent the target. The SFV is shown to be a type of sufficient statistic for SAR ATR where methods of employment are the subject of ongoing research. © 2023 IEEE.

关键词： Synthetic aperture radar

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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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Rethinking Imbalance in image Super-Resolution for Efficient Inference 38

Rethinking Imbalance in Image Super-Resolution for Efficient...

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38th conference on Neural Information processing Systems, NeurIPS 2024

作者： Yu, Wei Yang, Bowen Liu, Qinglin Li, Jianing Zhang, Shengping Ji, Xiangyang School of Computer Science and Technology Harbin Institute of Technology China School of Information Science and Technology Tsinghua University China

Existing super-resolution (SR) methods optimize all model weights equally using L1 or L2 losses by uniformly sampling image patches without considering dataset imbalances or parameter redundancy, which limits their performance. To address this issue, we formulate the image SR task as an imbalanced distribution transfer learning problem from a statistical probability perspective and propose a plug- and-play Weight-Balancing framework (WBSR) for image SR to achieve balanced model learning without changing the original model structure or training data. Specifically, we develop a Hierarchical Equalization Sampling (HES) strategy to address data distribution imbalances, enabling better feature representation from texture-rich samples. To tackle model optimization imbalances, we propose a Balanced Diversity Loss (BDLoss) function, focusing on learning texture regions while disregarding redundant computations in smooth regions. After joint training of HES and BDLoss to rectify these imbalances, we present a gradient projection dynamic inference strategy to facilitate accurate and efficient reconstruction during inference. Extensive experiments across various models, datasets, and scale factors demonstrate that our method achieves comparable or superior performance to existing approaches with approximately a 34% reduction in computational cost. The code is available at https://***/aipixel/WBSR. © 2024 Neural information processing systems foundation. All rights reserved.

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