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共 1490 条记录，以下是11-20 订阅

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Fine-grained image recognition based on multi-branch and multi-scale learning 3

Fine-grained image recognition based on multi-branch and mul...

引用

3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Li, Peng-Song Lin, Tong Zhou, Bing-Qian Ji, Zhi-Yi Northeastern Electric Power University 169 Changchun Road Jilin Jilin China

ISBN: (纸本)9781510667563

To address the problems of accurate region localization and effective data enhancement, this paper proposes a bilinear convolutional neural network model based on multiple branches and multiple scales. The method uses three branching networks to supervise the image recognition process, which enables the network model to learn fine-grained image features of different scales and different parts. The experimental results show that the bilinear convolutional neural network model based on multi-branch and multi-scale proposed in this paper has practical application value. © 2023 SPIE.

关键词： image recognition

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Multi-Scale CNN Architectures for Enhanced Feature Extraction in image Sentiment analysis 3

Multi-Scale CNN Architectures for Enhanced Feature Extractio...

引用

3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Indhumathi, S. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

imagery sentiment analysis is a complex task, the aim of which is the interpretation of both local and global visual signs in order to correctly classify the emotions ordered by the image. CNNs old architectures commonly have trouble with the proper balance of the tip-oriented extraction and the overall understanding of the context. This research suggests a new multi-scale CNN architecture in order to improve the extraction of features by the hierarchical representations of the visual sentiment cues. The architecture operates by adding convolutional layers of nucleotide scale and pyramid pooling modules to take features from different spatial resolutions. Besides, the attention mechanism is applied to the sentimental areas, thus dealing with the noise from unimportant parts. That is modeling a CNN with multi-scale convolutional layers in the construction of an image where each kernel size will deliver what is a fine signal versus a contextual pattern. A pooling module based on a pyramid structure is collected to the resolution to obtain features from different spatial scales, so the model will have the view of the entire image at a context level. Then, in addition, the proposed attention mechanism that finds and highlights regions with more probability of emotional content is used. The suggested architecture is prepared and tested on a number of datasets, that reflect the real-world sentiment contexts and the evaluation metrics like accuracy, precision, recall, and F1-score, which show the effectiveness of the proposed architecture. The proposed approach seeks to give a more in-depth definition of visual sentiment which, in turn, will lead to more precise classification as well as allow for better understanding with attention-based visualizations. The results are anticipated to drive the image sentiment analysis field further, making it relevant for social media analytics, advertising, and human-computer interaction. © 2025 IEEE.

关键词： Emotion recognition

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Research on image Target recognition of Defects in Steel Structure Welded Joints 3

Research on Image Target Recognition of Defects in Steel Str...

引用

3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Zhao, Rongguo Shandong Institute of Commerce and Technology Jinan250000 China

ISBN: (纸本)9781510667563

In this paper, the nondestructive testing and defect image signal recognition system for special welding of steel structure is researched and developed. The system not only makes use of the powerful computing power of the computer, integrates the relative software and hardware resources, but also realizes the functions of defect location, identification, display, channel control, storage and printing, etc., which greatly improves the cost performance of the instrument, and at the same time improves the intelligence of the instrument, making manual flaw detection more simple and flexible. The combination of modern digital signal processing and pattern recognition technology promotes the welding nondestructive testing technology, which not only improves the accuracy of testing, but also obtains the quantitative results of testing, such as the size, location and nature of welding defects. © 2023 SPIE.

关键词： Defects

来源：评论

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Third International conference on image Processing, Object Detection, and Tracking, IPODT 2024

Third International Conference on Image Processing, Object D...

引用

2024 3rd International conference on image Processing, Object Detection, and Tracking, IPODT 2024

ISBN: (纸本)9781510685512

The proceedings contain 35 papers. The topics discussed include: design of a book misplacement detection system based on image recognition;automatic detection of ultrasonic water meter LCD pattern elements based on ConvNeXt;deep intrinsic image popularity assessment by learning to rank;a late fusion framework for multi-vehicle collaborative perception;subpixel precision BGA chip localization method based on Lagrange interpolation;signal-to-noise ratio analysis and experimental verification of imaging laser detection system;comparison of cell image segmentation results based on U-Net and transformer;and deep learning based algorithm for classifying pedestrian behavior at crosswalks.

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Research on Flame Detection of fire-fighting robot 3

Research on Flame Detection of fire-fighting robot

引用

3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Tohti, Gulbahar Zhang, Guohui Geni, Mamtimin School of Mechanical Engineering Xinjiang University Xinjiang Urumqi830047 China Company of Baibo Electromechanical Technology Urumqi Xinjiang830011 China

ISBN: (纸本)9781510667563

Aiming at the problems of slow image processing and detection speed of flame recognition method and sensor measurement vulnerable to light environment interference, this paper studies how to improve the speed of flame recognition and overcome light environment interference. To improve the speed of flame recognition, this paper selects convolutional neural network technology, trains a classifier dedicated to flame, improves the preprocessing and post-processing methods of feature map, and develops a new flame recognition model, which greatly improves the speed of flame recognition;To overcome the interference of light environment, this paper proposes a new method by measuring the two output signals of the flame sensor in different light environments. It provides a new method for fast flame recognition of fire-fighting robot. © 2023 SPIE.

关键词： image enhancement

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Storage optimization method based on region of interest in part pose recognition 3

Storage optimization method based on region of interest in p...

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3rd International conference on Computer Vision and pattern analysis, ICCPA 2023

作者： Zhang, Peng Miao, Yangyang Shan, Dongri Jinan250353 China Jinan250353 China

ISBN: (纸本)9781510667563

In order to reduce the large amount of storage space occupied by model pose library in part pose recognition, a storage optimization method based on region of interest is proposed. First, perspective projection is used to render part CAD model, and sampling on view sphere uniformly to get model pose library. The region of interest of pose image is then cropped to optimize pose library storage space. The region of interest is identified as follows: transform pose image into gray image and then black and white image, mark largest connected domain in black and white image and crop it with external rectangle. Then, the part real image is obtained and the region of interest is cropped similarly. The part real image is matched to the pose library by template match. Finally, the optimal match image is recognized by template match algorithm and applied to adjust part CAD model. Experimental results show that the average storage optimization rate of the two models used in experiment reached 88.2%, and the average time of parts pose recognition is about 2 seconds. © 2023 SPIE.

关键词： image segmentation

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Identification Method of Dress pattern Drawing based on Machine Vision Algorithm 3

Identification Method of Dress Pattern Drawing based on Mach...

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3rd International conference on Computer Vision, image and Deep Learning and International conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Lyu, Ke Yan, Haizhang Zhejiang Sci-Tech University School of International Education Zhejiang310000 China Xidian University School of Computer Science and Technology Xi'an71000 China

ISBN: (纸本)9781665459112

This paper uses the machine vision method to identify the skirt module. We have constructed three kinds of machine recognition models of skirt profile processing, structure analysis of style drawing, and size estimation. The author constructs a relatively complete image recognition system for dress pattern drawing. In addition, we conducted an effect evaluation with a certain number of samples at the later stage of the experiment. This study has A good effect in distinguishing an A-type skirt from an H-type skirt, identifying the reasonable degree and length of the skirt, and determining the quantity statistics of each component element in the skirt pattern diagram. © 2022 IEEE.

关键词： image recognition

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Robust Fault Diagnostics of Industrial Motors: The Signal-to-image Approaches for Sensor Data Using Multi-Task Transformers with Diverse Attention Mechanism 3

Robust Fault Diagnostics of Industrial Motors: The Signal-to...

引用

3rd IEEE International conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Mukhopadhyay, Sudipta Kar, Indrajit Ralte, Zonunfeli Zensar Technologies Bangalore India

ISBN: (纸本)9798350313703

Vibration analysis serves as a valuable method to identify defects within mechanical systems. This process involves monitoring the auditory signals generated by the system and discerning patterns that signify potential issues. Nevertheless, due to the intricate nature of motors, relying solely on one modality (such as acoustic signals) might prove inadequate for comprehensively detecting all faults. Our research introduces a novel approach to enhance fault detection in mechanical systems, specifically focusing on motors. A more comprehensive system health assessment is achieved by utilizing multi-modal vibration analysis, combining acoustic signals and heat signatures. Effective information fusion is facilitated by integrating attention-based transformers, capable of capturing cross-modal correlations. The proposed neural network employs classification and regression loss functions for fault identification and severity assessment respectively. Evaluation on a motor fault dataset demonstrates significant classification accuracy improvements compared to conventional deep and machine learning methods. Incorporating a signal-to-image transformation further aids in fault pattern recognition. This method offers a promising avenue for accurate and comprehensive fault diagnosis in mechanical systems, showcasing the potential of multi-modal analysis and attention mechanisms for industrial equipment maintenance. © 2024 IEEE.

关键词： Fault detection

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3rd International conference on Optimization, Learning Algorithms and Applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd International conference on Optimization, Learning Algorithms and Applications, OL2A 2023

ISBN: (纸本)9783031530357

The proceedings contain 26 papers. The special focus in this conference is on Optimization, Learning Algorithms and Applications. The topics include: Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models;Using LiDAR Data as image for AI to Recognize Objects in the Mobile Robot Operational Environment;an Evaluation of image Preprocessing in Skin Lesions Detection;an Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills;vehicle Industry Big Data analysis Using Clustering Approaches;enhancing Forest Fire Detection and Monitoring Through Satellite image recognition: A Comparative analysis of Classification Algorithms Using Sentinel-2 Data;An Efficient GPU Parallelization of the Jaya Optimization Algorithm and Its Application for Solving Large Systems of Nonlinear Equations;Multi-objective Optimal Sizing of an AC/DC Grid Connected Microgrid System;sub-system Integration and Health Dashboard for Autonomous Mobile Robots;movement pattern recognition in Boxing Using Raw Inertial Measurements;optimization Models for Hydrokinetic Energy Generated Downstream of Hydropower Plants;deep Conditional Measure Quantization;fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods;Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation;deep Learning and Machine Learning Techniques Applied to Speaker Identification on Small Datasets;performance of Heuristics for Classifying Leftovers from Cutting Stock Problem;Deep Learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach;identification of Late Blight in Potato Leaves Using image Processing and Machine Learning;adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario;Impact of EMG Signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw Signal;assessing the 3D Position of a Car with a Single 2D Camera Usin

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3rd International conference on Optimization, Learning Algorithms and Applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd International conference on Optimization, Learning Algorithms and Applications, OL2A 2023

ISBN: (纸本)9783031530241

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