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检索条件"任意字段=13th International Conference on Computer Analysis of Images and Patterns"

共 3117 条记录，以下是21-30 订阅

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Suppressing reflected radiation to enhance infrared thermography inspection 18

Suppressing reflected radiation to enhance infrared thermogr...

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18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive Computing (MobiSPC) / 13th international conference on Sustainable Energy Information Technology (SEIT)

作者： Costa, Antonio Pitarma, Rui Polytech Guarda Ave Dr Francisco Sa Carneiro 50 P-6300559 Guarda Portugal

Infrared thermography is commonly used in a variety of applications. It is a fast, passive, non-contact, non-invasive alternative to conventional techniques. Nevertheless, its use has several associated errors that must be minimized or eliminated. One source of error is the radiation reflected on the surface of the studied object, coming from the surroundings, and captured by the thermographic camera lens. A measurement of the reflected apparent temperature may compensate for this error. In fact, during inspections, the operator generally assumes that the reflected apparent temperature is equal to the ambient temperature, resulting in a less accurate thermal image pattern. this study aims to verify the impact of suppressing reflected radiation on improving the thermal image details of the thermograms obtained during thermographic inspections. For this purpose, a wood sample was observed in the presence and absence of heat and light sources. the results suggest that the suppression of reflected radiation leads to better-quality thermograms and enhances the accuracy of defect detection and identification.

关键词： Reflected radiation thermal images thermographic analysis thermographic inspection Reflected apparent temperature

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Application of the CTPN-CRNN Model with Attention Mechanism in Scene Text Recognition 13

Application of the CTPN-CRNN Model with Attention Mechanism ...

引用

13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhang, Weijie Luo, Xiaonan Zhu, Xiaoshu School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

With the rapid development of the internet, textual information contained in online images has become a key resource for automated information extraction. However, this information is often embedded in images with complex backgrounds or irregular layouts, posing significant challenges to traditional text recognition methods. this paper proposes a method based on CTPN (Connectionist Text Proposal Network) and attention mechanism-enhanced CRNN (Convolutional Recurrent Neural Network) for extracting key information from online images, effectively supporting information extraction and data analysis tasks. the method first uses CTPN to accurately detect text areas in the image and generate text box candidates, then employs the CRNN enhanced with an attention mechanism for character recognition within the candidate boxes. We conducted experimental validation on the ICPR MTWT 2018 dataset, which includes a variety of real-world application scenarios. the results show that the CRNN model combined with CTPN and the attention mechanism outperforms traditional OCR methods on this dataset. the high precision and robustness of this method provide strong technical support for automated information extraction, product data analysis, and intelligent search, offering broad application potential. © 2025 IEEE.

关键词： Convolutional neural networks

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Improving Facial Emotional Recognition Using Convolution Neural Network with Minimal Layer 13

Improving Facial Emotional Recognition Using Convolution Neu...

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13th international conference on Electronics, Communications and Networks, CECNet 2023

作者： Tshibangu, Rogerant Tapamo, Jules R. Electrical Engineering Department Mangosuthu University of Technology Durban4031 South Africa Discipline of Electrical Electronic and Computer Engineering University of Kwazulu-Natal Durban4041 South Africa

ISBN: (纸本)9781643684802

Human emotions identification has many applications, including human-computer interaction, illogical analysis, medical diagnosis, data-driven animation, and human-robot interaction. this paper presents a classification model, ConvNet that extracts features from facial images using techniques such as local binary patterns (LBP), convolutional neural networks (CNN), and region-based oriented FAST and rotational BRIEF (ORB). this model converges quickly. Experiment show that ConvNet outperforms existing methods with a precision of 98.13% on the CK+ dataset and 92.05 % on the JAFFE dataset. © 2024 the authors and IOS Press.

关键词： Local binary pattern

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Stress Detection in Working Professionals 13

Stress Detection in Working Professionals

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13th international conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Gopalakrishnan, K. Kavitha, K. thanusree, S. Niranjan, A.R. Pooja, S. Harini, K. Subha Velalar College of Engineering and Technology Information Technology Tamil Nadu Erode12 India Kongu Engineering College Computer Science and Engineering Tamil Nadu Erode60 India Velalar College of Engineering and Technology Department of Information Technology Tamil Nadu Erode12 India

ISBN: (纸本)9798350380583

Digital workplace demands high levels of productivity, often at the cost of levels of employee well-being. the stress that builds up in professionals over time, if left unchecked, can harm mental health and reduce efficiency. this project focuses on developing an intelligent system that automatically identifies stress in working professionals by analysing facial expressions through image processing, utilizing Recurrent Neural Networks (RNNs). this paper gathers real-time images from various office environments, use RNNs to model time-based features and detect stress patterns with remarkable accuracy. the system aims to contribute to a supportive organizational culture by enabling early interventions and enhancing overall employee productivity. © 2024 IEEE.

关键词： Intelligent systems

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Deep Learning-Based analysis of Neuronal Signal Streams: Estimating the Activity Range of Cultured Neuronal Networks in Consecutive Stimulus Inputs Using CNN 13

Deep Learning-Based Analysis of Neuronal Signal Streams: Est...

引用

Joint 13th international conference on Soft Computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Asada, Hiroki Kudoh, Suguru N. Dept. of Human System Interaction Sanda Japan Artificial Intelligence and Mechanical Engineering Course Dept. of Engineering Sanda Japan

ISBN: (纸本)9798350373332

We utilized a Convolutional Neural Network (CNN) -that incorporates a structure designed to integrate local relationship features. the CNN-based deep learning approach enabled us to identify response patterns following stimulus input, using images of neural activity that depict the activity of individual neurons. the feature of CNN-based neural network, integrating local features, considered to contribute to decipher the relationship within time-series data, which are interpreted as streams of signals passing through a single neuron. Utilizing this feature, we attempted to determine the scope of activity that preserves the history of ongoing stimulation within a cultured living neuronal network. We repeatedly subjected the network to the same stimulus, and from the post-stimulus response activity, we extracted spatial combinations of neurons that fired simultaneously within brief 1 ms time windows. We calculated the firing rates for each of these 1 ms intervals in response to 10 different stimuli. By arranging these firing rates into a distinctive configuration that sustains temporal continuity, we generated images that illustrate neuronal activity. We then attempted to discriminate response images obtained at various stimulus intervals, and compared the accuracy of the discrimination to gauge the scope and degree of history retention in sequential activation. © 2024 IEEE.

关键词： Convolutional neural networks

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Analyzing Numerical patterns in Twitter Data: Unveiling Fake and Bot Accounts During Telangana State Elections 13

Analyzing Numerical Patterns in Twitter Data: Unveiling Fake...

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13th IEEE international conference on Communication Systems and Network Technologies, CSNT 2024

作者： Singha, Amrit Kumar Paul, Arnov Sonti, Sasank Guntur, Karthik Chiranjeevi, Mondikathi Dhuli, Sateeshkrishna Srm University Department of Ece Andhra Pradesh Amaravati India

ISBN: (纸本)9798350305463

Online Social Networks (OSNs) have become ubiquitous platforms for the dissemination of diverse content, en-compassing text, images, and videos. However, the proliferation of fake accounts poses a formidable challenge to the integrity of current OSN systems. Exploiting these fraudulent profiles, malicious actors distribute misleading information, ranging from deceptive surveys to fabricated reports of election rigging and false narratives about the government. Our proposal involves utilizing the latest developments in deep learning, namely in computer vision to address the widespread problem of phony accounts. namely, we suggest implementing an Artificial Neural Network (ANN) and certain Machine Learning methods. through a series of experiments, our findings reveal a promising outcome, demonstrating superior accuracy and minimal loss when compared to prevalent learning algorithms in the realm of fake account classification. this research contributes to the ongoing discourse on enhancing the robustness of OSN systems against the propagation of misleading information through fake accounts. © 2024 IEEE.

关键词： Social networking (online)

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A Systematic Review on Machine Learning Techniques Used for Early Detection of Skin Cancer 13

A Systematic Review on Machine Learning Techniques Used for ...

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13th IEEE international conference on Communication Systems and Network Technologies, CSNT 2024

作者： Chandragiri, Revanth Reddy, Dwarampudi Balaji Badarla, Koti Satya Prabhudev Syed, Anwar Hussain Modepalli, Kavitha Sadam, Kavitha Koneru Lakshmaiah Education Foundation Green Fields Department of Computer Science & Engineering AP Vaddeswaram522302 India

ISBN: (纸本)9798350305463

Skin cancer can be one of the most lethal diseases, caused by the abnormal development of skin cells exposed to UV radiation. It can cause metastasis and have significant mortality rates if not identified at the earliest. Detecting skin cancer at the early stages improves patient recovery rate to 99 % for 5 years. Melanoma can be identified at early stages by using the images of affected spots on the body of a patient. Diagnosing skin diseases through images is foremost challenging tasks in medical analysis. It's quite a complex task for human to recognize the type of cancer of the skin, accurately by looking at patient's skin lesion directly or through images. Machine learning comes into the picture by introducing various approaches in medical analysis to make these tasks simple. In this work, the role of machine learning algorithms is analysed systematically by considering the researcher's contributions to the early diagnosis of skin cancer. the HAM10000 dataset is considered for the implementation of five ML models. the results are analysed in terms of accuracy, precision, recall, and f1-score. the results evidence that ML techniques are working well in the prediction of skin cancer. © 2024 IEEE.

关键词： Deep learning

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Classification of Bloodstain Spatter patterns with Convolutional Neural Networks 39

Classification of Bloodstain Spatter Patterns with Convoluti...

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39th international conference on Image and Vision Computing New Zealand

作者： Rough, Rosalyn Bhagat, Swapnil Bainbridge-Smith, Andrew Batchelor, Oliver Green, Richard Inst Environm Sci & Res Christchurch Sci Ctr Christchurch New Zealand Univ Canterbury Dept Software Engn Christchurch New Zealand Univ Canterbury Dept Comp Sci & Software Engn Christchurch New Zealand

ISBN: (纸本)9798331518783;9798331518776

Bloodstain Pattern analysis is a forensic discipline focused on the examination of bloodstains at crime scenes with a view to assisting with the reconstruction of events. analysis of the physical characteristics of individual bloodstains and the overall pattern can assist with determining the mechanism that caused them, a task referred to as classification. Currently, the analysis of bloodstain patterns is still a manual and somewhat subjective task. With error rates of 13-23% for classification of spatter patterns, a more objective approach is recommended. the application of CNNs to the task of classification of three distinct spatter patterns, impact, cast-off and expirated is explored. A dataset of high-resolution laboratory-generated pattern images was used for training. To capture the entirety of the pattern that contains numerous small bloodstains spread over large areas, these images were composite images, ranging in size from 7 to 81 megapixels. this created a challenge for training the CNNs, with preprocessing steps required to create appropriate training data. Several pre-processing techniques, including dilation, resizing and cropping, were investigated and used to train two pre-trained CNN models, ResNet-50 and VGG-16. Dilated and resized images representing the full extent of the bloodstain patterns reached accuracies of >95%, but < 90% for partial patterns created through cropping.

关键词： bloodstain pattern analysis classification deep learning CNN

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2023 13th international conference on computer and Knowledge Engineering, ICCKE 2023

2023 13th International Conference on Computer and Knowledge...

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13th international conference on computer and Knowledge Engineering, ICCKE 2023

ISBN: (纸本)9798350330151

the proceedings contain 102 papers. the topics discussed include: a vision-based method for human activity recognition using local binary pattern;DPRNN-FORMER: an efficient way to deal with blind source separation;diagnosis of depression based on new features extractive from the frequency space of the EEG;spatio-temporal graph neural networks for accurate crime prediction;classification of benign and malignant tumors in digital breast tomosynthesis images using radiomic-based methods;intensity-image reconstruction using event camera data by changing in LSTM update;the Internet of things-enabled smart city: an in-depth review of its domains and applications;analysis of insect-plant interactions affected by mining operations, a graph mining approach;and leveraging the power of object detection models in identifying litter for a significant reduction in environmental pollution.

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Recognition of Valid QR Codes with Machine Learning 13

Recognition of Valid QR Codes with Machine Learning

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13th IEEE international conference on Communication Systems and Network Technologies, CSNT 2024

作者： Minocha, Arshia Goyal, Aman Gandhi, Rashmi Amity School of Engineering and Technology Amity University Auup Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350305463

the widespread use of Quick Response (QR) codes has made QR codes an attractive target for cyberattacks, posing a security and privacy concern. Quick Response (QR) codes have revolutionized marketing strategies by providing a fast and efficient way to connect offline and online platforms. However, the security issues arise as the usage of QR codes. this paper introduces an automatic detector that identifies QR code images and lets the user know whether it is malicious or benign. To achieve this, a dataset of 10,000 original and malicious QR code images (both) is taken. Convolutional Neural Network (CNN) is employed to classify original and malicious codes. Additionally, histogram density analysis is incorporated to enhance feature identification, leading to improved classification accuracy. this paper implemented ResNet50, MobilenetV3 and InceptionV3 CNN models for evaluating the abilities to predict the malicious codes. the InceptionV3 model emerges as the most promising, attaining an accuracy of 98.13%, offering an efficient and accurate solution for document forensics and counterfeit detection. © 2024 IEEE.

关键词： Convolutional neural networks

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