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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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A review of real-time human action recognition involving vision sensing

A review of real-time human action recognition involving vis...

引用

conference on Real-Time image processing and Deep Learning

作者： Majumder, S. Kehtarnavaz, N. Univ Texas Dallas Embedded Machine Learning Lab Richardson TX 75083 USA

ISBN: (数字)9781510643109

ISBN: (纸本)9781510643109

Human action recognition has been utilized in many applications such as human-computer interaction, video surveillance, assistive living, and gaming. Deployment of human action recognition demands the processing to be carried out in real-time or in a computationally efficient manner. The real-time requirement is addressed by only a subset of the developed methods in the literature. This paper provides a review of computationally efficient human action recognition methods in which a vision sensor is used. The reviewed papers are categorized in terms of conventional and deep learning approaches as well as in terms of single vision and multi-vision modality sensing.

关键词： Human action recognition Review of vision-based human action recognition Real-time human action recognition

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Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function

Illumination Normalization by Partially Impossible Encoder-D...

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IEEE Winter conference on applications of Computer vision (WACV)

作者： Da Cruz, Steve Dias Taetz, Bertram Stifter, Thomas Stricker, Didier IEE SA Bissen Luxembourg Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence Kaiserslautern Germany

ISBN: (纸本)9780738142661

images recorded during the lifetime of computer vision based systems undergo a wide range of illumination and environmental conditions affecting the reliability of previously trained machine learning models. image normalization is hence a valuable preprocessing component to enhance the models' robustness. To this end, we introduce a new strategy for the cost function formulation of encoder-decoder networks to average out all the unimportant information in the input images (e.g. environmental features and illumination changes) to focus on the reconstruction of the salient features (e.g. class instances). Our method exploits the availability of identical sceneries under different illumination and environmental conditions for which we formulate a partially impossible reconstruction target: the input image will not convey enough information to reconstruct the target in its entirety. Its applicability is assessed on three publicly available datasets. We combine the triplet loss as a regularizer in the latent space representation and a nearest neighbour search to improve the generalization to unseen illuminations and class instances. The importance of the aforementioned post-processing is highlighted on an automotive application. To this end, we release a synthetic dataset of sceneries from three different passenger compartments where each scenery is rendered under ten different illumination and environmental conditions: https://***

关键词： Computer vision Computational modeling Lighting Lightning machine learning Cost function Robustness

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Case Study of a Low-Cost IoT Device with a Thermal vision to Monitor Human Stool Behavior in the Home 21st

Case Study of a Low-Cost IoT Device with a Thermal Vision to...

引用

21st International conference on image Analysis and processing (ICIAP)

作者： Montoro-Lendinez, Alicia Diaz-Jimenez, David Luis Lopez-Ruiz, Jose Medina-Quero, Javier Espinilla-Estevez, Macarena Univ Jaen Dept Comp Sci Jaen 23071 Spain

ISBN: (纸本)9783031133213;9783031133206

Among the instruments for early detection are those for analysing gases in people's faeces, as it has been found that the presence of the intensity of certain compounds is related to the presence of cancer, diabetes or Alzheimer. The availability of sensor devices in recent years, together with the Internet of Things (IoT) paradigm, has made it possible to create low-cost systems that allow initial solutions to be tested for various real applications. Therefore, the aim of this contribution is to present the use case of a stool gas monitoring system in order to be the beginning of a solution for the early detection of this type of diseases. The proposed prototype integrates a thermal camera and MOX sensors to collect temperature and gas measurements immediately after a person has made deposition in their home. The measurements are monitored through an IoT platform and stored on a cloud server.

关键词： Stool monitoring system Volatile organic compounds (VOCs) MOX sensors Thermal camera Internet of Things (IoT)

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Preprocessing to improve the accuracy of recognition of radioelement components in automated assembly devices 9

Preprocessing to improve the accuracy of recognition of radi...

引用

conference on Optical Metrology and Inspection for Industrial applications IX Part of SPIE/COS Photonics Asia conference

作者： Semenishchev, Evgeny A. Zelensky, Aleksandr Zdanova, Marina Sizyakin, Roman Ilukhin, Yuriy Voronin, viacheslav Tula State Univ TulSU Lab Cognit Technol & Simulat Syst 92 Sq Lenina Tula 300012 Tula Region Russia Moscow State Tech Univ STANKIN Ctr Cognit Technol & Machine Vis 1a Vadkovsky Moscow 127055 Russia

ISBN: (纸本)9781510657052;9781510657045

The article presumes a data processing algorithm that improves the accuracy of recognition of radio-electronic components in devices for automated installation. The paper proposes the use of a multicriteria filtering method that allows you to automatically change the smoothing coefficient. Varying the coefficient allows both reducing the noise component and preserving the boundaries of the radio elements without blurring. In order to enhance the contours of objects, a data simplification method is applied using the technique of reducing the range of clusters of color gradient histograms while preserving the shapes of objects. At the stage of detecting the boundaries of the elements and forming the structure of the elements of the radio component base, a modified one-dimensional two-criteria method is used. The combined analytical approach allows detection of the boundaries of radioelements and increases the productivity of the process. As test data used to evaluate the effectiveness, pairs of test images obtained by sensors fixed at various magnifications with a resolution of 1024x768 (8 bit, color image, visible range) are used. images of simple shapes are used as analyzed objects.

关键词： control systems preprocessing machine vision electronics radioelement robotic system

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Semantic Threads Enabling image-Text Retrieval via VQA Transformers

Semantic Threads Enabling Image-Text Retrieval via VQA Trans...

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Computational Intelligence for Security, Communication and Sustainable Development (CISCSD), International conference on

作者： Junnubabu Noorbhasha Rohitha Guddeti Satvika Lingutla Sujitha Etikikota Santhosh Kumkumkari Computer Science & Technology Madanapalle Institute of Technology & Science Madanapalle India

ISBN: (数字)9798350365405

ISBN: (纸本)9798350365412

The integration of vision and language has propelled the advancement of artificial intelligence systems. visual Question Answering (VQA) stands at the nexus of computer vision and natural language processing, enabling machines to comprehend and respond to image-related queries. This paper introduces a novel VQA approach harnessing the capabilities of the BLIP (Bootstrapping Language image Pretrained) model, a transformer-based architecture esteemed for its natural language understanding prowess. The methodology involves image preprocessing, and question translation into a standardized language for efficient processing by BLIP. Mainly, the study integrates multilingual support into the VQA framework, facilitating seamless interaction with users across diverse linguistic backgrounds. Through rigorous experimentation, this paper demonstrates the effectiveness of our approach in accurately answering questions in various languages These findings underscore the robustness and adaptability of the BLIP model in handling multilingual inputs, thereby enhancing accessibility and usability in real-world applications. This research contributes to advancing the state-of-the-art in VQA systems by addressing language barriers and promoting inclusivity in human-machine interaction.

关键词： visualization Attention mechanisms Translation Computational modeling Semantics Transformers Question answering (information retrieval) Multilingual Usability Sustainable development

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A Real-Time Monitoring Computer Algorithm System for Occlusion of Photovoltaic Panels Developed by machine vision Technology

A Real-Time Monitoring Computer Algorithm System for Occlusi...

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image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Xixing Hu Weiyan Zheng Fang Su Chaoyue Zhu Zhejiang Dayou Industrial Co. Ltd. Hangzhou Science and Technology Development Branch Hangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In this paper, a multi-feature detection method based on graph cut for photovoltaic panels is proposed. Combined with multi-dimensional features such as optical flow field and light intensity, an interactive feature recognition method is constructed. The combination of each feature forms a pixel feature vector and is fed into a random forest classifier. The interaction information between adjacent pixel pairs is extracted. The energy function and adaptive algorithm of object-based occlusion detection are constructed. This Paper build an undirected graph. The object is segmented by the graph cutting principle. The findings from the experiments indicate that the new technique surpasses traditional methods in precision and offers enhanced responsiveness for detecting obstructions.

关键词： Photovoltaic systems Photovoltaic cells Software algorithms Real-time systems Vectors Software Monitoring Surface treatment Random forests Optical flow

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Capturing Prior Knowledge in Soft Labels for Classification with Limited or Imbalanced Data 5th

Capturing Prior Knowledge in Soft Labels for Classification ...

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5th Chinese conference on Pattern Recognition and Computer vision (PRCV)

作者： Zhong, Zhehao Zhao, Shen Wang, Ruixuan Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Peoples R China Key Lab Machine Intelligence & Adv Comp MOE Guangzhou Peoples R China

ISBN: (纸本)9783031189098;9783031189104

Successful applications of deep learning often depend on large amount of training data. However, in practical image recognition tasks, available training data are often limited or imbalanced across classes, causing the over-fitting issue or the prediction bias issue during model training. In this paper, based on word embedding models from studies in natural language processing, the prior knowledge about the relationships between image classes is utilized to help train more generalizable classifiers under the condition of limited or class-imbalanced training data. Such inter-class relational knowledge is captured in the word embedding vectors for the textual names of image classes. Using these word embedding vectors as soft labels for corresponding image classes, the feature extractor part of a deep learning model can be guided to learn to extract visual features which contain both class-specific and class-shared information. Experiments on multiple image classification datasets confirm that the proposed learning framework helps improve model performance when training data is limited or class-imbalanced.

关键词： Prototype learning image classification Limited data Imbalance data Soft label

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An Augmented image Captioning Model: Incorporating Hierarchical image Information 20

An Augmented Image Captioning Model: Incorporating Hierarchi...

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20th IEEE International conference on machine Learning and applications (ICMLA)

作者： Funckes, Nathan Carrier, Erin Wolffe, Greg Grand Valley State Univ Sch Comp Allendale MI 49401 USA

ISBN: (纸本)9781665443371

Despite published accessibility standards many websites remain non-compliant, containing images lacking accompanying textual descriptions. This leaves visually-impaired individuals unable to fully enjoy the rich wonders of the web. To help address this inequity, our research seeks to improve the ability of autonomous systems to generate accurate, relevant image descriptions. Our model enhances training efficacy by incorporating the use of category labels, high-level object superdasses, which are derivable using modern object-detection models. We show that this simple augmentation to an existing architecture results in a statistically significant improvement in caption quality.

关键词： Caption generation Computer vision image captioning Natural language processing

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Handwriting Recognition System Using YOLO and CTC

Handwriting Recognition System Using YOLO and CTC

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2023 International conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Garg, Neeraj Sharma, Neelam Jain, Gautam Jain, vipul Upreti, Vaibhav Maharaja Agrasen Institute of Technology Department of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350380880

In a multitude of applications, handwriting recognition holds significant importance, including tasks like transforming handwritten documents into digital format, automating form processing, and intelligent character recognition. This paper introduces a novel handwriting recognition system that integrates the You Only Look Once (YOLO) object detection algorithm with the Simple Handwritten Text Recognition (HTR) model. The YOLO algorithm is widely recognized for its ability to perform real-time object detection. By adapting it to the task of handwriting recognition, we can detect and localize handwritten text regions within images. The YOLO algorithm is trained on a large dataset of annotated images, enabling it to learn features specific to handwriting. Once the text regions are detected, the HTR model is employed for accurate recognition of the detected text. HTR is a deep learning-based approach that leverages recurrent neural networks (RNNs) to transcribe handwritten text into machine-readable format. The model is trained on a diverse set of labeled handwriting samples, enabling it to effectively recognize and convert handwritten characters into digital text. Our proposed system combines the strengths of YOLO's robust object detection capabilities with HTR's accurate text recognition. The integration of these two components allows for efficient and reliable handwriting recognition, even in challenging scenarios with varying handwriting styles and image qualities. To evaluate the performance of our system, we conducted experiments on benchmark datasets and compared the results with existing approaches. The experimental results demonstrate that our system achieves superior performance in terms of both detection accuracy and recognition accuracy, surpassing state-of-the-art methods. Overall, our proposed handwriting recognition system utilizing YOLO and HTR presents a robust and effective solution for converting handwritten text into digital format. This system can

关键词： Computer vision

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Non-Linear Time Sequence Problems Based on GA-BP Neural Network Prediction Model

Non-Linear Time Sequence Problems Based on GA-BP Neural Netw...

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image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Teng Wang Yuxuan Chen College of Technology and Data Yantai Nanshan University Yantai China School of Software Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In the fields of image recognition and computer vision, data mining and predictive analytics, BP neural network as a kind of simple structure and powerful model has been widely concerned by scholars, and the improvement of BP neural network is very important. In this paper, we used genetic algorithm to optimize BP neural network, and optimize the selection of weights and thresholds of BP neural network through the characteristics of local optimization of genetic algorithm. Compared with the single BP neural network, the root mean square error (RMSE) of the optimized BP neural network had been reduced by 0.82%, the mean absolute error (MAE) had been reduced by 0.85%, and the coefficient of determination had been improved by 1.34%• The experimental results have shown that the optimization of BP neural network with genetic algorithm is more advantageous than the single BP neural network, and the optimized BP neural network has a strong ability to deal with the complex problems with high dimensions and multi-objectives.

关键词： machine learning algorithms image recognition image coding Neural networks Predictive models Genetics Root mean square Predictive analytics Optimization Genetic algorithms

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