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检索条件"任意字段=5th International Symposium on Multispectral Image Processing and Pattern Recognition"

共 1594 条记录，以下是1-10 订阅

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Research on the Application of Deep Learning-Based image recognition Algorithm in Signal processing 5

Research on the Application of Deep Learning-Based Image Rec...

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5th IEEE international Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Lv, Jing Hunan Chemical Vocational and Technical College Zhuzhou China

ISBN: (纸本)9798331533694

A GAN-based image recognition algorithm is presented to solve these problems. Firstly, the GAN frame is composed of a generator and a discriminator. the generator can produce real images or remove noise by learning the signal image. then, the image is compared with the real image. In the pre-processing phase, the standard, denotation and enhancement of the signal image are carried out to guarantee the data quality and the validity of the characteristic. Experiments show that the precision of GAN-based image recognition is up to 96.3%, which is more than 15% than traditional methods (e.g., SVM). In the signal denoising task, the model significantly improves the signal-to-noise ratio (SNR) by 20% and effectively removes image noise. In addition, GAN also performs extremely well in pattern recognition tasks, with a correct recognition rate of 94.7%. Compared with traditional deep learning algorithms, the GAN model shows stronger robustness when processing signals with more noise or incomplete data. © 2025 IEEE.

关键词： image denoising

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Generalization of Brady-Yong algorithm for fast Hough transform to arbitrary image size 5

Generalization of Brady-Yong algorithm for fast Hough transf...

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5th symposium on pattern recognition and Applications, SPRA 2024

作者： Kazimirov, Danil Nikolaev, Dmitry Rybakova, Ekaterina Terekhin, Arseniy Smart Engines Service LLC Moscow Russia RAS Moscow Russia Faculty of Mechanics and Mathematics Lomonosov Moscow State University Moscow Russia Federal Research Center Computer Science and Control RAS Moscow Russia

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

Nowadays, the Hough (discrete Radon) transform (HT/DRT) has proved to be an extremely powerful and widespread tool harnessed in a number of application areas, ranging from general image processing to X-ray computed tomography. Efficient utilization of the HT to solve applied problems demands its acceleration and increased accuracy. Along with this, most fast algorithms for computing the HT, especially the pioneering Brady-Yong algorithm, operate on power-of-two size input images and are not adapted for arbitrary size images. this paper presents a new algorithm for calculating the HT for images of arbitrary size. It generalizes the Brady-Yong algorithm from which it inherits the optimal computational complexity. Moreover, the algorithm allows to compute the HT with considerably higher accuracy compared to the existing algorithm. Herewith, the paper provides a theoretical analysis of the computational complexity and accuracy of the proposed algorithm. the conclusions of the performed experiments conform with the theoretical results. Copyright © 2025 SPIE.

关键词： Hough transforms

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Road detection in autonomous vehicles through deep learning 5

Road detection in autonomous vehicles through deep learning

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5th symposium on pattern recognition and Applications, SPRA 2024

作者： Lee, Soeun Daegu International School Daegu Korea Republic of

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

In a rapidly advancing technological landscape, integrating deep learning in autonomous vehicles presents a pivotal challenge and opportunity. Our study briefly introduces some concepts in machine learning and its network structures. We aim to perform road segmentation using a U-Net image segmentation model trained on the Lyft Udacity dataset. We trained the model under different settings by adjusting parameters such as the initial channel, pooling method, activation function, and batch size. the performance of models is evaluated using the IoU metric. By comparing the performance of the models, we identify which settings yield higher performance for road segmentation task. Additionally, we observe the validation loss per epoch to determine which configuration provided the best training stability. this comprehensive evaluation allows us to identify optimal model setup for robust and reliable road segmentation models. Copyright © 2025 SPIE.

关键词： Convolutional neural networks

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Fifth symposium on pattern recognition and Applications, SPRA 2024

Fifth Symposium on Pattern Recognition and Applications, SPR...

引用

5th symposium on pattern recognition and Applications, SPRA 2024

ISBN: (纸本)9781510688797

the proceedings contain 18 papers. the topics discussed include: generalization of Brady-Yong algorithm for fast Hough transform to arbitrary image size;leveraging machine learning to predict factors that drive successful basketball team formation;real-time reconstruction on Micro-CT setup;stability of artificial intelligence methods in computed tomography metal artifacts reduction task;orthotropic alignment for X-ray computed tomography images;X-ray computed tomography traps and challenges for deep learning scientist;denoising diffusion probabilistic model for accurate super-resolution in landslide and debris flow disaster remote sensing images;and trajectory refinement in SLAM: the impact of Adam, AdamW, and SGD with momentum.

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Generation of semisynthetic natural-looking 2D barcodes for localization problems 5

Generation of semisynthetic natural-looking 2D barcodes for ...

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5th symposium on pattern recognition and Applications, SPRA 2024

作者： Limonova, Elena Zlobin, Pavel Bezmaternykh, Pavel Federal Research Center 'Computer Science and Control' Russian Academy of Sciences Moscow119333 Russia Smart Engines Service LLC Moscow117312 Russia

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

the role of barcodes in industrial processes and product labeling is rapidly growing. So, the problem of their recognition using mobile cameras in uncontrolled environments becomes very acute. the development of neural networks and computer vision provides an opportunity to solve it with high accuracy, given that there is enough representative data to train and test algorithms. However, the barcodes can contain sensitive information, and huge amounts of data cannot be published. Synthetic image generation can solve this problem, and in this paper, we propose a method to generate semi-synthetic natural-looking 2D barcodes with illumination changes, blur, and projective distortion, which can be used to create training or testing data for localization problems. We introduce the SE-DMTX-SYN-1000 dataset, designed for Data Matrix fine localization. We validate localization accuracy using Zxing, Zxing-cpp and libdmtx barcode reading libraries and demonstrate that this benchmark is quite challenging and can help to improve Data Matrix localization. Copyright © 2025 SPIE.

关键词： image coding

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Coffee plant disease detection using JSEG segmentation and near sets clustering 5

Coffee plant disease detection using JSEG segmentation and n...

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5th symposium on pattern recognition and Applications, SPRA 2024

作者： Dias, Jeferson de Souza Boa Sorte, Lucas Ximenes Fambrini, Francisco Saito, José Hiroki Centro Universitário Campo Limpo Paulista Brazil Centro Universitário da Fundação Educacional de Guaxupé Brazil Universidade Virtual do Estado de São Paulo Brazil

ISBN: (数字)9781510688803

ISBN: (纸本)9781510688797

It is important to study coffee plants, and help agronomists to detect diseases, such as rust, with resources of computer science. In this work, it is described experiments using image segmentation algorithm JSEG, which is capable to segment images in multi-scale. Using a coffee tree image database RoCoLe (Robusta Coffee Leaf images), the JSEG algorithm is used to segment these images in four scales. It is selected typical segments in each scale and they are clustered in similarity of normalized color histograms, using Near Sets theory. In this way the several scales segmentations are compared. It is concluded that smaller segments, scales 1 and 2, in which the colors are more homogeneous than in greater segments, scales 3 and 4, are adequate to use as training samples for the detection of rust diseases using artificial neural networks. the proposed approach can be applied for counting the segments affected by rust, to estimate the disease severity in coffee leaves. Copyright © 2025 SPIE.

关键词： image segmentation

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Research on the Application of Deep Learning-Based image recognition Algorithm in Signal processing

Research on the Application of Deep Learning-Based Image Rec...

引用

Power Electronics, Computer Applications (ICPECA), IEEE international Conference on

作者： Jing Lv Hunan Chemical Vocational and Technical College Zhuzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

关键词： Deep learning Training image recognition Signal processing algorithms Generative adversarial networks Generators Robustness pattern recognition image denoising Signal to noise ratio

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Non-invasive Technique for Detecting Glycosuria through image processing and Deep Learning Approaches 5th

Non-invasive Technique for Detecting Glycosuria Through Imag...

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5th EAI international Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Teja, Chitturi Siva Nitheesha, Gokaraju Kumar, Boyidi Ravi Krishna, Bandaru Radha Naik, Desavath Madhu Pareek, Prakash Singh, Lokendra Department of ECE Vishnu Institute of Technology Bhimavaram534202 India Dehradun248002 India

ISBN: (纸本)9783031770746

Glycosuria level monitoring is a fundamental aspect of diabetes management, enabling proactive and personalized care to prevent complications, improve overall health, and enhance the quality of life for individuals with diabetes. Conventional glycosuria monitoring methods often involve invasive procedures, potentially risking the immune system, especially with repeated injections. the research focuses on a non-invasive method utilizing urine samples for glycosuria testing to address these concerns. the proposed method integrates image processing techniques, such as Gaussian filtering and image resizing, to optimize input data with machine learning approaches. Deep learning, especially convolutional neural networks is employed for their ability in feature extraction and pattern recognition. the system aims to achieve high accuracy in categorizing glycosuria levels into groups such as diabetes, prediabetes, and normal, providing a reliable and non-invasive alternative to traditional glycosuria monitoring techniques. this approach shows promise in enhancing patient compliance and overall health monitoring for individuals requiring regular glucose assessment. the accuracy of the proposed method based on CNN achieves 98.54% where the existing method based on support vector machine (SVM) achieves only 88.46%. it is evident from simulation results, the proposed method attained 11.39% improved accuracy when compared to existing SVM. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Accuracy Classification Convolutional Neural Network Deep learning gaussian filter Glycosuria

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5th international Conference on Recent Trends in image processing and pattern recognition, RTIP2R 2022

5th International Conference on Recent Trends in Image Proce...

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5th international Conference on Recent Trends in image processing and pattern recognition, RTIP2R 2022

ISBN: (纸本)9783031235986

the proceedings contain 31 papers. the special focus in this conference is on Recent Trends in image processing and pattern recognition. the topics include: image processing and pattern recognition of Micropores of Polysulfone Membrane for the Bio-separation of Viruses from Whole Blood;An Extreme Learning Machine-Based AutoEncoder (ELM-AE) for Denoising Knee X-ray images and Grading Knee Osteoarthritis Severity;motor imagery Classification Combining Riemannian Geometry and Artificial Neural Networks;Autism Spectrum Disorder Detection Using Transfer Learning with VGG 19, Inception V3 and DenseNet 201;shrimp Shape Analysis by a Chord Length Function Based Methodology;supervised Neural Networks for Fruit Identification;targeted Clean-Label Poisoning Attacks on Federated Learning;building Marathi SentiWordNet;A Computational Study on Calibrated VGG19 for Multimodal Learning and Representation in Surveillance;alzheimer’s Disease Detection Using Ensemble Learning and Artificial Neural Networks;automated Deep Learning Based Approach for Albinism Detection;a Deep Learning-Based Regression Scheme for Angle Estimation in image Dataset;the Classification of Native and Invasive Species in North America: A Transfer Learning and Random Forest Pipeline;Towards a Digital Twin Integrated DLT and IoT-Based Automated Healthcare Ecosystem;enabling Edge Devices Using Federated Learning and Big Data for Proactive Decisions;ioT and Blockchain Oriented Gender Determination of Bangladeshi Populations;Federated Learning Based Secured Computational Offloading in Cyber-Physical IoST Systems;a Hybrid Campus Security System Combined of Face, Number-Plate, and Voice recognition;single-Trial Detection of Event-Related Potentials with Artificial Examples Based on Coloring Transformation;identifying the Relationship Between Hypothesis and Premise;semi-supervised Multi-domain Learning for Medical image Classification;data Poisoning Attack by Label Flipping on SplitFed Learning;a Deep Learning-Powered

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th international Conference on Bioinformatics and Intelligent Computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. the functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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