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CNN-RNN Deep Learning Networks for pattern recognition Problems 2

CNN-RNN Deep Learning Networks for Pattern Recognition Probl...

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2nd international conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Imran, Inas Ismael University of Baghdad College of Education for Women Dept. of Computer Iraq

ISBN: (纸本)9798350335644

Neural networks have remarkably solved many problems. A giant network with a high processing requirement and long training time is being investigated owing to having a massive amount of data. Therefore, parallel processing is essential for practical applications of network training. This study aims to develop the best neural network to accelerate a face recognition system. The standards used to compare tasks were speed and accuracy used to develop an algorithm providing the best performance in a parallel system. Moreover, this study aimed to combine neural networks and parallel programming to develop a swift person recognition system in a camera stream. Video frames were saved, and the images were fed to a neural network built in a parallel structure. This study provides readers with a better understanding of the new and current trends of artificial neural networks (ANN) models, successfully addressing PR issues to permit research emphasis and themes. The system used libraries, such as MPI, graphical computing compute unified device architecture (CUDA) libraries, and the softmax function to provide the required performance with high accuracy and good delay results. The results of this study showed that the parallel system using a conventional neural network (CNN) was 30% better than the serial system for face recognition and that the CNN networks provided the best accuracy among all the other systems. © 2023 IEEE.

关键词： Recurrent neural networks

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An Automatic Key-points Detection and Style Transfer based Method of Articulatory Animation Generation 23

An Automatic Key-points Detection and Style Transfer based M...

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9th international conference on computing and Artificial Intelligence, ICCAI 2023

作者： Liu, Wei Ling, Xufeng Liang, Jingxin Wang, Xiaobo Yang, Jie Shanghai Normal University Tianhua College AI School No. 1661 North Sheng Xin Road China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University 800 Dongchuan Road 200240 China

ISBN: (纸本)9781450399029

The method of generating articulatory animation based on automatic key point detection and style transfer. This paper proposes a system of key-points detection and registration based on image feature space, and uses the GAN for motion transfer, which is used to automatically generate oral and tongue articulatory animation of English. First, by obtaining the standard articulation image of the international Phonetic Alphabet from the public dataset, we train a deep-learning motion model according to the characteristics of the tongue, upper jaw, lower jaw, small tongue, soft palate and other motion features, according to the characteristics of articulatory organ, and automatically detect the key-points in the image feature space. According to the existing MRI articulatory videos, using different styles of articulatory images, a generative model is used to generate end-to-end animation of different styles automatically. For different combinations of consonants and vowels, generate animation of different syllables, and according to different categories of articulatory, such as stress and light tone, adjust the duration of articulatory to form different animation of stress and light tone of the same syllable. And form the animation of words through syllables, and then form the animation of each sentence. Experiments show that this method can simulate realistic articulatory animation according to input phonetic symbols, and has a very positive effect on English articulatory correction. © 2023 Copyright held by the owner/author(s).

关键词： Animation

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Artificial Immune-Based Semi-supervised Learning for Classification 15

Artificial Immune-Based Semi-supervised Learning for Classif...

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15th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC 2023

作者： Wang, Yi Hubei University of Economics School of Information Engineering Wuhan430205 China Hubei University of Economics Hubei Internet Finance Information Engineering Technology Research Center Wuhan430205 China

ISBN: (纸本)9798350308693

Recently, artificial immune algorithms have attracted great attention of researchers and been widely used in function optimization, pattern recognition and classification. Traditional artificial immune algorithms for classification are applied to supervised learning problems, which require completely labeled data for training models. However, in many real-world scenarios, it is difficult to obtain all labeled samples. To solve this problem, an efficient semi-supervised artificial immune algorithm for classification tasks is proposed. It employs a clonal selection algorithm to generate memory cells used for classification, which is achieved via selection, cloning, and mutation procedures. Moreover, it utilizes the ensemble learning technique to extend the co-training paradigm and improves classification performance by adding the most confident unlabeled samples into the labeled set. In addition, the theory of learning from noisy examples is adopted to decide whether there are enough newly labeled samples that are used to reduce the negative effects caused by noises. Experimental results show that the proposed method achieves better or comparable performance than well-known semi-supervised and supervised methods on four datasets. © 2023 IEEE.

关键词： pattern recognition

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To Improve the Identification of Images Using LBPH in Machine Learning 1

To Improve the Identification of Images Using LBPH in Machin...

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1st international conference on Intelligent computing and Research Trends, ICRT 2023

作者： Kumar Shukla, Ratnesh Kumar Tripathi, Praveen Kumar, Aman Prakash, Ved Kumar Singh, Dinesh Shambhunath Institute of Engineering & Technology U.P. Prayagraj India Jamia Millia Islamia Faculty of Engineering Dept. of Computer Engineering New Delhi India Kamla Nehru Institute of Engineering & Technology U.P. Sultanpur India

ISBN: (纸本)9798350336771

One of the most significant and extensively studied local texture descriptors is called Local Binary patterns (LBP). In the studies, a comprehensive analysis of the most renowned LBP related procedures is required in order to make sense of them all. Descriptors have been created using multistage convolutional neural networks (CNN) in deep learning. With low-resolution pixels, this work presents an enhanced real-time facial identification system that integrates posture, mood and resolution variations. We produced training and classification datasets. Face detection and recognition is performed using the different algorithm, which gets the face picture. With an updated database, the training and recognition process will resume automatically. The distance between a person and camera can be used for this facial recognition technology to help security forces in system. A surveillance system for airports, bus terminals and other public locations can also be installed to reduce the risk of criminal activity. To lessen the possibility of criminal behaviour, a surveillance system for airports, bus terminals and other public venues can be implemented. © 2023 IEEE.

关键词： Local binary pattern

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EXPLOITING ANNOTATORS' TYPED DESCRIPTION OF EMOTION PERCEPTION TO MAXIMIZE UTILIZATION OF RATINGS FOR SPEECH EMOTION recognition 47

EXPLOITING ANNOTATORS' TYPED DESCRIPTION OF EMOTION PERCEPTI...

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47th IEEE international conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Chou, Huang-Cheng Lin, Wei-Cheng Lee, Chi-Chun Busso, Carlos Univ Texas Dallas Dept Elect & Comp Engn Multimodal Signal Proc MSP Lab Richardson TX 75080 USA Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan

ISBN: (纸本)9781665405409

The decision of ground truth for speech emotion recognition (SER) is still a critical issue in affective computing tasks. Previous studies on emotion recognition often rely on consensus labels after aggregating the classes selected by multiple annotators. It is common for a perceptual evaluation conducted to annotate emotional corpora to include the class "other," allowing the annotators the opportunity to describe the emotion with their own words. This practice provides valuable emotional information, which, however, is ignored in most emotion recognition studies. This paper utilizes easy-accessed natural language processing toolkits to mine the sentiment of these typed descriptions, enriching and maximizing the information obtained from the annotators. The polarity information is combined with primary and secondary annotations provided by individual evaluators under a label distribution framework, creating a complete representation of the emotional content of the spoken sentences. Finally, we train multitask learning SER models with existing learning methods (soft-label, multi-label, and distribution-label) to show the performance of the novel ground truth in the MSP-Podcast corpus.

关键词： Emotion recognition Distribution-label learning soft-label learning Multi-label learning

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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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