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Research on Extracting Clothing Style Images Based on Computer Vision Technology

Research on Extracting Clothing Style Images Based on Comput...

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Cybersecurity, Internet of Things and soft computing (CITSC), Asia-Europe conference on

作者： Linan Xu Min Kuang Wuhan Institute of Design and Engineering Wuhan Hubei China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

Deep learning-based image recognition and generation technology is a key aspect of visual artificial intelligence, and the rapid development of AI technology is driving the clothing industry towards digitization. How to automatically and accurately obtain relevant clothing demand information to meet the severe market challenges has become a focal point for major clothing e-commerce platforms. This paper employs deep learning techniques to construct CNN classification models and object detection models for the automatic recognition of multiple features, including clothing color, style, and patterns. The study utilizes an improved lightweight VGGNet network for recognizing colors and styles, combined with YOLOv3, Faster R-CNN, and SSD object detection algorithms to achieve pattern recognition and localization. Through comparative experiments, the recognition accuracy of the improved lightweight VGGNet in identifying clothing styles and colors reached 96.49%; in clothing pattern detection, YOLOv3 achieved the highest mean Average Precision (mAP) of 86.66%. Among the three categories of patterns, texture patterns demonstrated the best detection performance with a mAP of 96.14%, followed by animal patterns with an mAP of 83.69%, and text patterns with an mAP of 79.80%. The results indicate that the graphic recognition algorithm proposed in this paper can effectively extract and identify patterns on clothing, thereby promoting the digital transformation of the clothing industry.

关键词： YOLO Deep learning Computer vision Image recognition Image color analysis Clothing Feature extraction Clothing industry pattern recognition Artificial intelligence

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Lunar Ejecta pattern Detection and Analysis Using Faster R-CNN and GAN Augmentation 14

Lunar Ejecta Pattern Detection and Analysis Using Faster R-C...

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14th international conference on Cloud computing, Data Science and Engineering, Confluence 2024

作者： Ghadekar, Premanand Rathad, Chanchal Jangral, Sourav Unde, Rushikesh Mali, Adwait Karande, Aryan Vishwakarma Institute of Technology Dept. of Information Technology Pune India

ISBN: (纸本)9798350344837

Gaining understanding of the Moon's geological and impact history requires a thorough analysis and inspection of its surface. Understanding the history of impact events, planetary development, crater formation mechanisms, geological composition, lunar regolith, planetary defense strategies, space weathering effects, and the broader field of comparative planetary science depend greatly on the analysis of ejecta patterns. This study examines the automated recognition and interpretation of complex lunar patterns from high-resolution lunar pictures using region-based convolutional neural networks. The model is customized and fine-tuned to identify a wide range of craters with prominent ejecta patterns. An essential component for enhancing the model's effectiveness and reliability is the integration of Generative Adversarial Networks. GAN s, a class of generative models, are employed to generate additional lunar images that closely resemble real lunar surface features. This augmentation strategy serves multiple technical purposes, such as improving resistance to noise and artifacts, expanding the training dataset, and enhancing overall robustness. The initial step encompasses essential tasks such as orthorectification and radiometric calibration, aimed at optimizing image quality and ensuring accurate geometric and radiometric characteristics. Subsequently, this study trained the model for feature extraction and region proposal generation. This is followed by region classification and bounding box regression to accurately pinpoint and categorize lunar crater patterns. The analysis encompasses characterizing ejecta patterns based on their size, shape, distribution, and their relationship with other lunar surface features. Proposed model has achieved an outstanding accuracy rate of 85.35% in the automatic recognition and interpretation of intricate lunar patterns. © 2024 IEEE.

关键词： Generative adversarial networks

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MUSCAT: A Multimodal mUSic Collection for Automatic Transcription of Real Recordings and Image Scores 24

MUSCAT: A Multimodal mUSic Collection for Automatic Transcri...

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32nd ACM international conference on Multimedia, MM 2024

作者： Galan-Cuenca, Alejandro Valero-Mas, Jose J. Martinez-Sevilla, Juan C. Hidalgo-Centeno, Antonio Pertusa, Antonio Calvo-Zaragoza, Jorge U.I. for Computing Research Pattern Recognition and Artificial Intelligence Group Alicante Spain

ISBN: (纸本)9798400706868

Multimodal audio-image music transcription has been recently posed as a means of retrieving a digital score representation by leveraging the individual estimations from Automatic Music Transcription (AMT) - -acoustic recordings - -and Optical Music recognition (OMR) - -image scores - -systems. Nevertheless, while proven to outperform single-modality recognition rates, this approach has been exclusively validated under controlled scenarios - -monotimbral and monophonic synthetic data - -mainly due to a lack of collections with symbolic score-level annotations for both recordings and graphical sheets. To promote research on this topic, this work presents the Multimodal mUSic Collection for Automatic Transcription (MUSCAT) assortment of acoustic recordings, image sheets, and their score-level annotations in several notation formats. This dataset comprises almost 80 hours of real recordings with varied instrumentation and polyphony degrees - -ranging from piano to orchestral music - -, 1251 scanned sheets, and 880 symbolic scores from 37 composers, which may also be used in other tasks involving metadata such as instrument identification or composer recognition. A fragmented subset of this collection solely focused on acoustic data for score-level AMT - -the MUSic Collection for aUtomatic Transcription - fragmented Subset (MUSCUTS) assortment - -is also presented together with a baseline experimentation, concluding the need to foster research on this field with real recordings. Finally, a web-based service is also provided to increase the size of the collections collaboratively. © 2024 Owner/Author.

关键词： Image annotation

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Generalist Segmentation Algorithm for Photoreceptors Analysis in Adaptive Optics Imaging 27th

Generalist Segmentation Algorithm for Photoreceptors Analys...

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27th international conference on pattern recognition, ICPR 2024

作者： Kulyabin, Mikhail Sindel, Aline Pedersen, Hilde R. Gilson, Stuart Baraas, Rigmor Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany National Centre for Optics Vision and Eye Care Faculty of Health and Social Sciences University of South-Eastern Norway Kongsberg Norway

ISBN: (纸本)9783031781032

Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive optics scanning light ophthalmoscope (AOSLO) imaging enables visualization of the cones from reflections of waveguiding cone photoreceptors. While there have been significant improvements in automated algorithms for segmenting cones in confocal AOSLO images, the process of labeling data remains labor-intensive and manual. This paper introduces a method based on deep learning (DL) for detecting and segmenting cones in AOSLO images. The models were trained on a semi-automatically labeled dataset of 20 AOSLO batches of images of 18 participants for 0∘, 1∘, and 2∘ from the foveal center. F1 scores were 0.968, 0.958, and 0.954 for 0∘, 1∘, and 2∘, respectively, which is better than previously reported DL approaches. Our method minimizes the need for labeled data by only necessitating a fraction of labeled cones, which is especially beneficial in the field of ophthalmology, where labeled data can often be limited. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adaptive optics

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Exploring the Horizons of Speaker recognition: Contemporary Advancements and Prospective Trajectories in the Age of Deep Learning 6

Exploring the Horizons of Speaker Recognition: Contemporary ...

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6th IEEE Pune Section international conference, PuneCon 2023

作者： Sasmal, Sibadatta Dhote, Pavan Bhargava, Mahesh Nikam, Hruturaj Patil, Sanhita Mehta, Swati Singh, Lenali Kumar, Ajai Cdac Aaig Pune India Cdac Spg Pune India Cdac Aai & Gist Pune India

ISBN: (纸本)9798350324204

Speaker recognition is a prominent area of study within speech technology. In recent times, there has been a notable transition towards embedding-based end-to-end speaker recognition approaches. These methods enable speaker recognition without the need for retraining when encountering new speakers in real-world scenarios. Earlier models were trained on limited monotonous hand-crafted datasets, which worked on pattern matching, but failed to be robust because of architectural limitations and insufficient data. Research shows that training deep neural networks, which rely on attention mechanisms, on extensive datasets containing weakly labeled and diverse samples can significantly improve their resilience when applied to various classification tasks downstream. Drawing upon knowledge acquired from extensive datasets like VoxCelebl and VoxCeleb2, along with specially crafted proprietary data tailored to handle various modalities, we initiated an investigation into the capabilities of these versatile techniques for building speaker recognition models. Our attention was directed towards two reputable models, ECAPA-TDNN [1](Emphasized Channel Attention, Propagation, and Aggregation) and Titanet[2], as we aimed to comprehend the architectural enhancements and assess their impact on the model's ability to generalize effectively. Also, we propose a faster and scalable inference pipeline using Elasticsearch. © 2023 IEEE.

关键词： pattern matching

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Real Time pattern recognition with Support Vector Machines and Local Binary patterns 1

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1st international conference on Advanced computing Techniques in Engineering and Technology, ACTET 2023

作者： Baby, Ann Akhil Kumar, K.M. Kerala Kalamassery India

ISBN: (数字)9783031541629

ISBN: (纸本)9783031541612

This research introduces a facial recognition-based attendance system that leverages an open-source computer vision library and integrates with a real-time database system. The system comprises essential components, including a camera for image capture, a computer for image processing, and a database containing registered facial data. As individuals enter a room, the camera captures their images, which are then subjected to facial detection and recognition by the computer. The system conducts a comparison between the detected faces and the pre-registered facial data in the database, subsequently logging the attendance of individuals who are successfully recognized. Moreover, the system offers functionality for the inclusion of new facial data into the database, facilitating future attendance tracking. To gauge the system's performance, an assessment was carried out using a dataset of 100 images. The results indicate an accuracy rate of 95% in both facial detection and recognition tasks. This system holds promise for a wide range of practical applications in diverse environments, such as educational institutions, workplaces, and event management, by automating attendance management processes and alleviating the burdens associated with manual record-keeping. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cameras

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Accelerating Semantic Segmentation Labeling of Time Sequence Data via Assistive Label Generation with Conditional GANs 3

Accelerating Semantic Segmentation Labeling of Time Sequence...

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3rd international conference on computing and Machine Intelligence (ICMI)

作者： Akagic, Amila Buza, Emir Kecman, Stefani Saric, Rijad Lewsey, Mathew G. Custovic, Edhem Whelan, James Univ Sarajevo UNSA Fac Elect Engn Sarajevo 71000 Bosnia & Herceg La Trobe Inst Sustainable Agr & Food LISAF Dept Anim Plant & Soil Sci Melbourne Vic 3086 Australia La Trobe Univ Australian Res Council Res Hub Med Agr Melbourne Vic 3086 Australia SIA 2 Res Ave Bundoora Vic 3086 Australia Zhejiang Univ Coll Life Sci State Key Lab Plant Environm Resilience Hangzhou 310058 Peoples R China Zhejiang Univ Prov Int Sci & Technol Cooperat Base Engn Biol Haining 314400 Peoples R China

ISBN: (纸本)9798350372977;9798350372984

This paper presents a robust exploration of the capabilities of conditional Generative Adversarial Networks (GANs) in harnessing labeled data to produce high-quality labels for unlabeled samples. By leveraging conditional information, our approach guides the network to generate contextually relevant labels for specific time series data, accelerating the labeling process. A comprehensive evaluation of our model's performance, incorporating diverse metrics, visual representations, and histograms, illuminates the effectiveness of conditional GANs for the Assistive Label Generation (ALG) of time series Arabidopsis thaliana images. The Structural Similarity Index (SSIM) highlights an average similarity of 98.89% between the generated and manually labeled images. This innovative methodology holds the promise of significantly reducing labeling efforts.

关键词： Plant Phenotyping Generative Adversarial Networks Image Processing Computer Vision Deep Learning pattern recognition

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Intelligent Accident recognition and Notification System using Hybrid Deep Learning and IoT 4

Intelligent Accident Recognition and Notification System usi...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Sathish Kumar, P. Singh Rawat, Ramesh Shrimali, Manish Parikh, Swapnil Patil, Harshal Maranan, Ramya United Institute of Technology Department of Robotics & Automation Tamil Nadu Coimbatore641020 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Computer Science and IT Rajasthan Udaipur313 003 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Symbiosis Institute of Technology Symbiosis International Deemed University Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331515720

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-Accident data were collected. The model is evaluated using three metrics: Accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety. © 2024 IEEE.

关键词： Graphical user interfaces

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Neuro-Cognitive pattern recognition: Advancements in Memory-Related Disorders Identification 6

Neuro-Cognitive Pattern Recognition: Advancements in Memory-...

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6th IEEE international conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Vifert Jenuben Daniel, V. Archanaa, N. Faheem, Mohammed Kousihik, K. Suwin Kumar, J.D.T. Muthulakshmi, M. Amrita School of Engineering Amrita Vishwa Vidhyapeetham Department of Artificial Intelligence Chennai India Amrita School of Engineering Amrita Vishwa Vidhyapeetham Department of ECE Chennai India

ISBN: (纸本)9798350306613

The application of machine learning in medical imaging and diagnostics opens up new pathways for disease identification and classification. Our research investigates the use of various machine learning models for Alzheimer's disease detection and classification using MRI image data, including Support Vector Machines (SVM) with different kernels, Linear Discriminant Analysis (LDA), and the advanced convolutional neural network, EfficientNetB0. The comparative study is an important component of this research, concentrating not only on the models' performance but also on the theoretical assumptions that underpin their efficacy. This investigation sheds light on why certain models outperform others, notably in the context of Alzheimer's disease classification. The research emphasizes the robust performance of linear models such as SVM and LDA, as well as the complexities of convolutional neural networks. Notably, our findings show a tremendous success with a 98.8 % accuracy rate, highlighting the potential of machine learning in improving diagnostic processes. This study contributes to the expanding field of machine learning in medical diagnostics by providing in-depth analysis and theoretical understanding that guide model selection and optimization in high-stakes healthcare applications. © 2024 IEEE.

关键词： Alzheimer disease CNN EfficientNetB0 LDA MRI PCA SVM

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4th international conference on soft computing and its Engineering Applications, icsoftComp 2022

4th International Conference on Soft Computing and its Engin...

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4th international conference on soft computing and its Engineering Applications, icsoftComp 2022

ISBN: (纸本)9783031276088

The proceedings contain 36 papers. The special focus in this conference is on soft computing and its Engineering Applications. The topics include: Explainable AI for Predictive Analytics on Employee Attrition;graph Convolutional Neural Networks for Nuclei Segmentation from Histopathology Images;Performance Analysis of Cache Memory in CPU;extraction of Single Chemical Structure from Handwritten Complex Chemical Structure with Junction Point Based Segmentation;automatic Mapping of Deciduous and Evergreen Forest by Using Machine Learning and Satellite Imagery;RIN: Towards a Semantic Rigorous Interpretable Artificial Immune System for Intrusion Detection;SVRCI: An Approach for Semantically Driven Video Recommendation Incorporating Collective Intelligence;deep Learning Based Model for Fundus Retinal Image Classification;one True Pairing: Evaluating Effective Language Pairings for Fake News Detection Employing Zero-Shot Cross-Lingual Transfer;reliable Network-Packet Binary Classification;semKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization;extraction and Analysis of Speech Emotion Features Using Hybrid Punjabi Audio Dataset;human Activity recognition in Videos Using Deep Learning;Brain Tumor Classification Using VGG-16 and MobileNetV2 Deep Learning Techniques on Magnetic Resonance Images (MRI);five-Year Life Expectancy Prediction of Prostate Cancer Patients Using Machine Learning Algorithms;an Ensemble MultiLabel Classifier for Intra-Cranial Haemorrhage Detection from Large, Heterogeneous and Imbalanced Database;A Method for Workflow Segmentation and Action Prediction from Video Data - AR Content;convolutional Neural Network Approach for Iris Segmentation;reinforcement Learning Algorithms for Effective Resource Management in Cloud computing;FedCLUS: Federated Clustering from Distributed Homogeneous Data;corpus Building for Hate Speech Detection of Gujarati Language;a software

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