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Single-pixel Imaging Classification Method Based on Improved Lenet-5 Network 4

Single-pixel Imaging Classification Method Based on Improved...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Yang, Changchang Wang, Huibin Xiao, Bingrui Chen, Zhe Bu, Yang Hohai University College of Computer and Information Nanjing China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai China

ISBN: (纸本)9798350324303

Single-pixel imaging, as a new imaging technique, requires a large amount of data processing when reconstructing images. If the classification is based on the reconstructed images of single-pixel imaging, a large amount of data redundancy will be generated. In view of this defect, the 'image-free classification' method can significantly reduce the data throughput in the step of image reconstruction in single-pixel imaging, and directly send the value of the single-pixel detector before image recovery into the convolutional neural network for classification. However, there is still some room for improvement in the classification accuracy and time of MNIST dataset. this paper combines Lenet-5 network with 'image-free classification', innovatively adjusts Lenet-5 network to a structure suitable for 'image-free classification', completes the classification of MNIST and Fashion-MNIST dataset using 'image-free classification', and adds residual structure to improve accuracy. Using the structural illumination trained by the improved neural network to project the target can increase the test accuracy from 89% to 99%, reduce the number of projection times of digital micromirror devices from 15 to 6, and reduce the total time of testing and training from 2 hours to 4.7 minutes, which indicates that the improved convolutional neural network reduces the classification time and improves the classification accuracy. © 2023 IEEE.

关键词： Classification (of information)

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Human Activity recognition for Office Surveillance 4

Human Activity Recognition for Office Surveillance

引用

4th international conference for Emerging Technology, INCET 2023

作者： Subrahmanya Hande, P.J. Rakeshgowda, D.S. Kumar, Naveen Nandana, K.A. Kanwal, Preet PES University Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350335750

Human activity surveillance video systems are gaining popularity in the field of computer vision due to user demands for security as well as their growing importance in many applications such as elder care, home nursing, and unusual event alarming. Automatic activity recognition is the key to video surveillance. this paper presents a method for human activity recognition in office surveillance videos using machine learning models including convLSTM, GRCNN and LRCN with three main steps: pre-processing, feature extraction and activity classification. the main targeted activities are walking, sleeping on desk, handshaking, typing, opening or closing door. Experimental results demonstrate the effectiveness of the proposed LRCN approach in accurately recognizing human activities in office surveillance videos with acceptable training and testing accuracy. © 2023 IEEE.

关键词： Long short-term memory

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Construction of CNN-based image recognition model for halloysitum rubrum in mineral medicine 4

Construction of CNN-based image recognition model for halloy...

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4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Sun, Yangbo Huang, Ziwen School of Information Engineering Hubei University of Chinese Medicine China Hubei Shizhen Laboratory China

ISBN: (纸本)9781510686106

Mineral medicines are pivotal in traditional Chinese medicine, with their identification technology being a focal point of research in this field. Over the years, various identification methods for mineral medicines have emerged. Integrating computer vision technology and deep learning theories makes intelligent identification of mineral medicines feasible. this study employs a convolutional neural network (CNN) algorithm to successfully construct an image recognition model for halloysitum rubrum (Chinese medicine name: Chishizhi). Utilizing the TensorFlow deep learning framework, we preprocess and augment a limited dataset of mineral images, substantially expanding the data scale and addressing sample imbalances. Based on image analysis, accuracy, and loss are evaluated as performance metrics for image recognition. the results demonstrate a high recognition accuracy of 92% with a stable loss between 0.1 and 0.2. Furthermore, the optimized model exhibits faster convergence speed and lower training time, markedly enhancing the efficiency of network training and achieving intelligent recognition of halloysitum rubrum images. © 2024 SPIE.

关键词： Convolutional neural networks

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Mongolian Compound Word Semantic Web Word Vector Evaluation Research 4

Mongolian Compound Word Semantic Web Word Vector Evaluation ...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： La, Du Hasi Ding, Yang Inner Mongolia Normal University College of Computer Science and Technology Hohhot China College of Computer Science and Information Engineering Hohhot Minzu College Hohhot China

ISBN: (纸本)9798350324303

Semantic dictionaries are the basis of natural language processing tasks. With the construction of WordNet, CCD, HowNet and other semantic dictionaries, the semantic resources of Chinese and English have been greatly improved, and so has the construction of Mongolian lexical semantic network. As an important part of the lexical system, the construction project of Mongolian lexical semantic network is an essential step to fill in the perfect compound words. In this study, the Mongolian compound word semantic web based on the optimal word vector model is automatically generated by comparing the Word2Vec model, Glove model and FastText model for Mongolian word vectors in different dimensions and performing similarity and similarity on the test set. © 2023 IEEE.

关键词： Semantic Web

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Automated Wood Carving Profiling using Modified Autocollimator Setup 4

Automated Wood Carving Profiling using Modified Autocollimat...

引用

4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Tomar, Riya Natarajan, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349856

Wood carving involves chiselling, which is a process of using a chisel and a mallet to obtain a particular shape profile in a wooden block. A perfect measuring tool decides the perfection of the shape profile in the chiselled wooden block. Automated tools are available to measure the dimensions of the wooden block but not to measure its shape profile and to compare with reference profile information. One of the solutions is to use machine vision, which involves using a camera and a processing system. It is only for shape analysis of chiselled wood blocks and not during the chiselling process, attributed to the wooden dust accumulating on the camera lens hindering the image acquisition process. So, we propose a technique for measuring the shape profile of wooden blocks during the chiselling process itself. this technique is not susceptible to wooden dust accumulation. the entire shape profiler is designed as an embedded module with custom hardware and software components. © 2024 IEEE.

关键词： Dust

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Research on image classification based on fusion of global features and local features 4

Research on image classification based on fusion of global f...

引用

4th international conference on Sensors and Information Technology, ICSI 2024

作者： Jiang, Jinzheng School of Information Engineering Nanjing University of Finance & Economics 3 Wenyuan Ave. JiangSu Nanjing210023 China

ISBN: (数字)9781510678699

ISBN: (纸本)9781510678682

image classification has always been a classic research topic in the field of image processing. In recent years, the development of deep learning has triggered a new wave of artificial intelligence research, further promoting the development of computer vision related technologies. the leaf image recognition method based on deep learning has become a research hotspot, such as convolutional neural networks that are good at solving image recognition problems, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, ResNet, DenseNet and other neural networks, constantly refreshing their achievements in classification tasks. Most deep learning networks focus on optimizing network structure, while ignoring the complementarity of different receptive fields. Based on this, this article focuses on the complementarity between global and local features of images, and proposes the FGL network model. FGL extracts image features from both large and small receptive fields, and combines their complementarity to propose a hierarchical fusion module, which enhances the extraction of effective image features and improves classification accuracy. FGL achieved better results in CIFAR10, CIFAR100, and SVHN. Reached 96.8%, 84.2%, and 97.9% respectively. FGL has the following advantages: it is an end-to-end network;More feature information can be extracted under the same data conditions. © 2024 SPIE.

关键词： image classification

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A Review of Pre-processing Techniques for Weed-Plant Detection and Classification in Precision Agriculture 4th

A Review of Pre-processing Techniques for Weed-Plant Detecti...

引用

4th international conference on Data, Engineering, and Applications, IDEA 2022

作者： Sonawane, Sandip Patil, Nitin N. R. C. Patel Institute of Technology Shirpur India

ISBN: (纸本)9789819700363

Precision agriculture has recently gained significant importance in computer vision technologies. Various processes as a part of agricultural production cycle from planting to harvesting can be carried out automatically and effectively by using computer vision. the lack of publicly available image datasets is a major obstacle to the rapid design and assessment of computer vision based applications and also to machine learning algorithms which support these applications. To reduce this bottleneck, numerous image dataset collections have been discovered and made publicly available since 2015. In spite of this development, there is still a need to focus on survey of these datasets. Two most important concerns—choosing the right dataset and knowing how to pre-process and prepare the images in datasets, are considerably challenging task in every application. this review paper gives a thorough analysis of the public image datasets and numerous pre-processing techniques carried out in the field of precision agriculture. this thorough study can lead to development of suitable methods for improved quality and productivity of the crop along with proper weed management. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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A Survey on image Captioning Using Object Detection and NLP 4

A Survey on Image Captioning Using Object Detection and NLP

引用

4th international conference on Advanced Research in Computing, ICARC 2024

作者： De Silva, Vathila Sumanathilaka, T.G.D.K. School of Computing Informatics Institute of Technology 06 Colombo Sri Lanka Swansea University Department of Computer Science Wales Swansea United Kingdom

ISBN: (纸本)9798350384864

Recent years have seen the emergence of image captioning as a revolutionary technical development that seamlessly blends computer vision and natural language processing. the integration of various domains facilitates the production of captivating captions for pictures, promoting a more profound comprehension of visual material. this study offers a thorough analysis of the rapidly developing field of image captioning, examining its uses in various settings such as social media, online platforms, assistive technology, and content indexing. It emphasizes the critical importance of advanced techniques like You Only Look Once (YOLO) for accurate item detection and Natural Language processing (NLP) for creating subtle captions. While NLP gives the resulting text a layer of contextual depth, YOLO guarantees precise object detection, which helps with caption accuracy. Using these cutting-edge methods boosts the overall efficacy of image captioning systems and represents a noteworthy trend in the literature. Examining the evolution of image captioning, the review paper highlights how important it is for multimodal comprehension. image captioning is a transformative tool that enhances the readability of visual content and influences contemporary digital experiences in various contexts. the development of image captioning is summarized in this abstract, which also highlights the tendency to use cutting-edge techniques for more accurate and nuanced caption production. © 2024 IEEE.

关键词： Object detection

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Research on Vibration Signal Diagnosis Platform System Based on computer Fuzzy Logic 4

Research on Vibration Signal Diagnosis Platform System Based...

引用

4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Bo, Liu Yang, Song Zhenwang, Lv Zhaoran, Meng Dalian Scientific Test and Control Technology Institute Dalian China

ISBN: (纸本)9798350361643

An on-line vibration detection method for power transformers is introduced. this design includes sensor module, real-time processing module, PC module. the sensor module collects the vibration, voltage, load voltage and other signals of the motor and transmits them to the processing unit on site. the real-time processing module transmits the processed information to the PC for display, storage, analysis and other functions. A real-time monitoring system for power transformer vibration based on virtual instrument /Compact RIO is developed. A new signal detection algorithm is established by combining wavelet energy information entropy theory with SVM. A frequency domain analysis algorithm based on wavelet analysis is proposed. the normalized feature vector is the input signal of support vector machine. then a support vector machine (SVM) multi-classifier model based on multi-class learning is proposed. Experimental results show that the SVM classifier proposed in this paper has high classification accuracy under multiple working conditions of power transformer. © 2024 IEEE.

关键词： pattern recognition

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Pakistani Currency recognition and Classification for Visually Impaired People Using Convolutional Neural Network 4

Pakistani Currency Recognition and Classification for Visual...

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4th international conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Nasir, Asra Zafar, Sidra Nasir, Zaeem Kinnaird College for Women Department of Computer Science Lahore54000 Pakistan Forman Christian College Department of Computer Science Lahore54600 Pakistan

ISBN: (纸本)9798350391862

Contemporary advancements aim to enhance the quality of life across various domains, including healthcare and education, through the integration of innovative technologies. In healthcare, computer vision technology is emerging as a valuable tool to assist visually impaired individuals in their daily activities, fostering greater independence and reducing reliance on others. Our focus is on aiding visually impaired individuals in distinguishing between different denominations of Pakistani banknotes. Employing Deep Learning techniques, we propose a multiclass classification model to categorize banknotes. this system will generate auditory cues corresponding to recognized banknote images, facilitating comprehension of their denominations by visually impaired individuals. Our study utilizes a dataset comprising 4,713 image samples of seven Pakistani currency denominations. We will implement a Convolutional Neural Network (CNN) VGG-16 architecture using this database to accurately differentiate between different denominations of Pakistani banknotes. this methodology bridges the gap between the generalization capabilities of existing models and the unique characteristics of Pakistani banknotes, promising effective banknote denomination classification for visually impaired individuals. © 2024 IEEE.

关键词： Convolutional neural networks

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