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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

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PCB Component Rotation Detection Based on Polarity Identifier Attention 1

引用

32nd International conference on Artificial neural networks (ICANN)

作者： Ma, Haoming Zhang, Hongjie Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sichuan Normal Univ Coll Comp Sci Chengdu Peoples R China Sichuan Yuanzhigu Technol Co Ltd Chengdu Peoples R China

ISBN: (数字)9783031442018

ISBN: (纸本)9783031442001;9783031442018

Accurate detection of component rotation is a critical task in the production of printed circuit boards (PCBs). However, with the rapid development of integrated circuits, manually labeling large datasets of all kinds of components for training machine learning models is time-consuming and impractical. To address this challenge, we propose a novel one-shot conditional component rotation detection framework, PolarNet. Given a standard image of the target component, a query image, and a polar indicator image as input, PolarNet can detect the rotation angle of the component in the query image. PolarNet comprises a Siamese network, an Indicator Attention Generator, and a Rotation Detector. The Indicator Attention Generator takes the polar indicator image as heuristic knowledge to help the Rotation Detector. Once trained, PolarNet can detect component rotation in both seen and unseen classes without further training. Our experiments on our own dataset demonstrate that PolarNet achieves good performance for one-shot rotation detection in both accuracy and scalability.

关键词： Rotation Detection One-shot learning Polar Component

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Self-Training and Modular Approaches for Surgical image Recognition

Self-Training and Modular Approaches for Surgical Image Reco...

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International Joint conference on neural networks (IJCNN)

作者： Ben Salem, Nosseiba Bennani, Younes Karkazan, Joseph Barbara, Abir Dacheux, Charles Gregory, Thomas Univ Sorbonne Paris Nord LIPN CNRS UMR 7030 Villetaneuse France Deep Knowledge Hop Avicenne AP HP Bobigny France La Maison Sci Numer Hop Avicenne AP HP Bobigny France

ISBN: (纸本)9781665488679

Deep learning-based applications have seen a lot of success in recent years. Text, audio, image, and video have all been explored with great success using deep learning approaches. The use of convolutional neural networks (CNN) in computer vision, in particular, has yielded reliable results. In order to achieve these results, a large amount of data is required. However, the dataset cannot always be accessible. Moreover, annotating data can be difficult and time-consuming. Self-training is a semi-supervised approach that managed to alleviate this problem and achieve state-of-the-art performances. Theoretical analysis even proved that it may result in a better generalization than a normal classifier. Another problem neural networks can face is the increasing complexity of modern problems, requiring a high computational and storage cost. One way to mitigate this issue, a strategy that has been inspired by human cognition known as modular learning, can be employed. The principle of the approach is to decompose a complex problem into simpler sub-tasks. This approach has several advantages, including faster learning, better generalization, and enables interpretability. In the first part of this paper, we introduce and evaluate different architectures of modular learning for Dorsal Capsulo-Scapholunate Septum (DCSS) instability classification. Our experiments have shown that modular learning improves performances compared to non-modular systems. Moreover, we found that weighted modular, that is to weight the output using the probabilities from the gating module, achieved an almost perfect classification. In the second part, we present our approach for data labeling and segmentation with self-training applied on shoulder arthroscopy images.

关键词： Data Science machine learning Modular neural networks Self-training Dorsal Capsulo-Scapholunate Septum Shoulder arthroscopy

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Implementation of Parallel Evolutionary Convolutional neural Network for Classification in Human Activity and image Recognition 22nd

Implementation of Parallel Evolutionary Convolutional Neural...

引用

22nd Mexican International conference on Artificial Intelligence (MICAI)

作者： Villegas-Cortez, Juan Roman-Alonso, Graciela Fernandez De Vega, Francisco Flores-Morales, Yafte Aaron Cordero-Sanchez, Salomon Univ Autonoma Metropolitana Dept Sistemas Azapotzalco Av Sn Pablo 180 Mexico City 02200 DF Mexico Univ Autonoma Metropolitana Dept Ingn Elect Unidad Iztapalapa Mexico City DF Mexico Univ Extremadura Dept Comp Sci C Santa Teresa Jornet38 Merida 06800 Spain Univ Autonoma Metropolitana Unidad Iztapalapa Dept Chem Mexico City DF Mexico

ISBN: (纸本)9783031477645;9783031477652

Pattern recognition has been evolving to include problems posed by new sceneries containing a high number of pattern components. processing this volume of information allows a more exact classification in wider types of applications;however, some of the difficulties of this scheme is the maintenance of numerical precision and mainly the reduction of the execution time. During the last 15 years, several machine learning solutions have been implemented to reduce the number of pattern components to be analyzed, such as artificial neural networks. Deep learning is an appropriate tool to accomplish this task. In this paper, a convolutional neural network is implemented for recognition and classification of human activity signals and digital images. It is achieved by automatically adjusting the parameters of the neural network through genetic algorithms using a multiprocessor and GPU platform. The results obtained show the reduction of computational costs and the possibility of better understanding of the solutions provided by Deep learning.

关键词： Deep learning Pattern recognition HAR image Recognition Parallel Genetic Algorithms

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Improved image Classification with Manifold neural networks

Improved Image Classification with Manifold Neural Networks

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Deberaldini Netto, Caio F. Wang, Zhiyang Ruiz, Luana Johns Hopkins University Baltimore United States Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia United States

ISBN: (纸本)9798350368741

Graph neural networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting from GNNs' message-passing framework. However, the potential of GNNs in more general data representations, especially in the image domain, remains underexplored. Leveraging the manifold hypothesis, which posits that high-dimensional data lies in a low-dimensional manifold, we explore GNNs' potential in this context. We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image. This approach reduces data dimensionality while preserving geometric information. We then train a GNN to predict node labels corresponding to the image labels in the classification task, and leverage convergence of GNNs to manifold neural networks to analyze GNN generalization. Experiments on MNIST and CIFAR10 datasets demonstrate that GNNs generalize effectively to unseen graphs, achieving competitive accuracy in classification tasks. © 2025 IEEE.

关键词： generalization graph neural networks manifold neural networks variational autoencoders

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Synaptic turnover promotes efficient learning in bio-realistic spiking neural networks 57

Synaptic turnover promotes efficient learning in bio-realist...

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57th Asilomar conference on Signals, Systems and Computers

作者： Malakasis, Nikos Chavlis, Spyridon Poirazi, Panayiota UoC Sch Med Iraklion Greece FORTH IMBB Iraklion Greece Tech Univ Munich Sch Life Sci Weihenstephan Munich Germany

ISBN: (纸本)9798350325744

While artificial machine learning systems achieve superhuman performance in tasks such as language processing and image/video recognition, they do so using extremely large datasets and consume huge amounts of power. On the other hand, the brain remains superior in several cognitively challenging tasks while operating with the energy of a small lightbulb. To explore how biology achieves such high efficiency, we built a biologically constrained spiking neural network model and assessed its learning capacity on various discrimination tasks. We found that a form of structural plasticity, namely the ability of synapses to form and eliminate continuously, results in higher performance accuracy and faster learning across all scenarios tested. These improvements are most significant under more difficult learning conditions, such as when the number of trainable parameters is halved or when the task difficulty is increased. The study highlights the important role of structural plasticity in optimizing learning in biological circuits and opens new avenues for exploring the applicability of such biological plasticity rules in machine learning applications.

关键词： dendrites spiking neural network structural plasticity synaptic turnover

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Deep learning Transformations in Content-Based image Retrieval: Exploring the Visual Geometry Group (VGG16) Model 4

Deep Learning Transformations in Content-Based Image Retriev...

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4th IEEE International conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Bala, Myneni Madhu Bai, K. Jhansi Lakshmi Kattubadi, Sarayu Pulipati, Gayathri Balguri, Aashritha Vnr Vignana Jyothi Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798331518981

Content-based image retrieval (CBIR) is evolving with new computational paradigms such as deep learning in vision applications viz. visual searches, machine translation, and query-based image retrieval. The deep visual learning with Visual Geometry Group (VGG) Model is one of the support layer-based convolution neural networks (CNN) utilized in multiple machine vision tasks like image segmentation, categorization, and object detection within image recovery systems. A VGG architecture consists of numerous blocks, each having a maximum-pooling layer, bus encoder, and 2D complexity. VGG16 consists of 16 layers having extracting features and learning weights to enhance the system's performance. This highlights the challenge of handling large datasets, compounded by the complexity of the network's architecture and resulting features. © 2024 IEEE.

关键词： machine vision

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A Deep (learning) Dive Into Bacterial Classification 8

A Deep (Learning) Dive Into Bacterial Classification

引用

conference on Optics, Photonics, and Digital Technologies for Imaging applications VIII

作者： Daniel Fuentes-Villegas, Alberto Hernandez, Haydee O. Olveres, Jimena Escalante-Ramirez, Boris Univ Nacl Autonoma Mexico Ciencias & Ingn Comp Mexico City DF Mexico Univ Nacl Autonoma Mexico Fac Ingn Mexico City DF Mexico Univ Nacl Autonoma Mexico Ctr Estudios Comp Avanzada Mexico City DF Mexico

ISBN: (纸本)9781510673151;9781510673144

Most bacteria classifiers created by neural networks and/or image processing methods are unable to generalize when used with different data bases of images acquired with the same type of acquisition systems, even if the sample preparation is similar. In this work, we introduce an ensemble of deep neural networks designed for the classification of bacteria in a broad context. We use a dataset comprising Actinomyces, Escherichia, Staphylococcus, Lactobacillus, and Micrococcus bacteria with Gram staining, which was acquired through brightfield microscopy from various sources. To normalize diversity of image characteristics, we applied domain generalization and adaptation techniques. Subsequently, we used phenotypic characteristics, such as the color reaction to Gram staining and morphology, to classify the bacteria.

关键词： Ensemble Deep learning Microscopy Bacteria Classification

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Automatic Generation of a Portuguese Land Cover Map with machine learning 1

引用

Intelligent Systems conference (IntelliSys)

作者： Esteves, Antonio Valente, Nuno Univ Minho ALGORITMI Res Ctr LASI Braga Portugal Univ Minho Braga Portugal

ISBN: (数字)9783031477218

ISBN: (纸本)9783031477201;9783031477218

The application of machine learning techniques to satellite imagery has been the subject of interest in recent years. The increase in quality and quantity of images, made available by Earth observation programs, such as the Copernicus program, led to the generation of large amounts of data. Among the various applications of this data is the creation of land cover maps. The present work aimed to create machine learning models capable of accurately segmenting and classifying satellite images to automatically generate a land cover map of the Portuguese territory. Several experiments were carried out with the spectral bands of the Sentinel-2 satellite, with vegetation indices, and with several sets of land cover classes. Three machine learning architectures were evaluated, which adopt two different techniques for image classification. One of the classification techniques follows an object-oriented approach, and in this case the architecture adopted in our models was a U-Net artificial neural network. The other classification technique is pixel-oriented, and the machine learning models tested were random forest and support vector machine. The overall accuracy of the results obtained ranged from 68.6% to 94.75%, depending strongly on the number of classes into which the land cover is classified. The result of 94.75% was obtained when classifying the land cover only into five classes. However, a very interesting accuracy of 92.37% was achieved by the model when trained to classify eight classes. These results are superior to those reported in the related bibliography.

关键词： machine learning Deep learning Remote sensing Land cover map

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International conference on Advances in Signal processing and Communication Engineering, ICASPACE 2023

International Conference on Advances in Signal Processing an...

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International conference on Advances in Signal processing and Communication Engineering, ICASPACE 2023

ISBN: (纸本)9789819705610

The proceedings contain 49 papers. The special focus in this conference is on Advances in Signal processing and Communication Engineering. The topics include: Traffic Sign Recognition Using Faster R-CNN Deep learning Algorithm;Virtual Implementation of MIMO in Cooperative Communication;pseudo-Coloring on Dual Motion History image for Action Recognition from Depth Maps;S2JD: Segmented Skeleton Joint Descriptor for Human Action Recognition from 3D Posture Data;leaf Disease Detection of Medicinal Plants Using Convolutional neural Network;Integration of BIST Module for Error Bit Detection in SPI Protocol;design and Simulation of Shape Changing Nanoparticle for Bio Medical applications;driving Training-Based Optimization-Enabled Key Generation Approach for Secure and Efficient Authentication with Data Sharing Approach in IoT Healthcare;analysis of image Quality and Video Denoising Using Convolutional neural networks;Real-Time Accident Detection and Reporting System Using Edge Computing and Convolutional neural networks with Integration of GPS for Public Safety;near-Optimal Slot Sizes for the Single Mode Flexible-Grid Optical networks;Multiclass Classification of Camouflage images Using Combined WLD and LPQ Feature Set Using a ANN Classifier;supervised Deep Statistical Analysis Model Using Improved Canny Edge Detection Based on Optimal Double-Threshold Estimation for Palmprint Recognition System;preface;design of Polygon-Shaped Open-Ended Slot Antenna for C-Band applications;a Novel Technique Using Particle Swarm Optimization and Firefly Algorithm for Wireless Network;development of Mechanoluminescent-Based Sensor for Wearable Technology;Optimal Sensor Geometry Analysis for 3D TDOA-Based Source Localization;machine learning for Prediction of Emotion Towards Digital Assets;design of Two-Stage Differential Op-Amp with Frequency Compensation;dual Port Multiple Input Multiple Output Antenna for 5G Communication applications;sidelobe Reduction Filter for Frank Codes;classifica

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Deep learning: The Future of Medical image processing

Deep Learning: The Future of Medical Image Processing

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2023 International conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Singh, Kamred Udham Pandey, Saroj Kumar Yadav, Dhirendra Prasad Singh, Teekam Kumar, Gaurav Kumar, Ankit School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Health care is a vital service that is constantly in high demand since everyone needs it. Individuals have higher hopes for advancement in this profession than for receiving the status quo since they would rather be treated better. Sometimes, or maybe more accurately, in many situations, the findings are not clear and the sickness cannot be understood at the first stage from a manual reading of the report. When it comes to viewing medical images, the fact that they must be interpreted by hand inevitably leads to delays and errors. Many deep learning and machine learning approaches can be used to address this issue. Therefore, although machine learning may be thought of as a subset of deep learning, the reverse is not true. Let's talk about how deep-learning models will be used to process medical images. Medical image processing is one area where deep learning models are having a profound effect. Convolutional neural networks (CNNs) and other deep learning techniques have made it feasible to automate the processing of medical pictures and improve diagnostic and treatment accuracy. It is widely used in radiology to examine medical pictures such as X-rays, CT scans, and MRIs. In order to aid radiologists in their diagnostic work, deep learning models may be taught to identify patterns and characteristics in medical pictures that are diagnostic of certain diseases or ailments. © 2023 IEEE.

关键词： Convolutional neural networks

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