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Study on image style transfer for graphic creation using VGG19 and Pix2Pix model 4

Study on image style transfer for graphic creation using VGG...

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4th international conference on computer Graphics, image, and Virtualization, ICCGIV 2024

作者： Min, Long Shao, Mingrui Liu, Mengwen Li, Xiaozhou Ma, Dengyue Li, Xuelin Liu, Jingjing Faculty of Light Industry Qilu University of Technology 3501 Daxue Road Jinan250300 China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of Technology Shandong Academy of Sciences 3501 Daxue Road Jinan250300 China Key Laboratory of JiNan Digital Twins and Intelligent Water Conservancy Jinan250013 China School of Art & Design Qilu University of Technology 3501 Daxue Road Jinan250300 China Practice Teaching Center of Art & Design Shandong University of Art & Design Jinan250300 China

ISBN: (纸本)9781510683242

In computer vision field, the style transfer technique is to synthesize the style of one image with the content features of another image to generate a new migrated image, thus creating a unique graphic art image. In this thesis, the image style transfers achieved based on the VGG19 network model and pre-trained Pix2Pix network model separately were investigated by the comparison method, and the COCO dataset was selected as the dataset. the experiments used the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) to evaluate the performance of the style transfer images. It can be found that the PSNR of the migrated image of the VGG19 network model is 12.6776 and the SSIM is 0.2981, while the PSNR of the migrated image of the Pix2Pix network model is 12.9153 and the SSIM is 0.3182. the result indicates that the migrated image generated by the Pix2Pix network model is better than that by the VGG19 with respect to the image quality features. © 2024 SPIE.

关键词： Feature extraction image processing Data modeling

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image Classification Using Federated Learning 6th

Image Classification Using Federated Learning

引用

6th international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2023

作者： Byeon, Haewon Ullah, Ajdar Syed, Ziauddin Siddiqui, Alighazi Aftab, Aasif Sarfaraz, Mohd Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of School of Mechanical and Manufacturing Engineering National University of Science and Technology Islamabad44000 Pakistan Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan45142 Saudi Arabia Department of Information Technology and Security College of Computer Science and Information Technology Jazan University Jazan45142 Saudi Arabia

ISBN: (纸本)9783031530814

Multiple users can jointly train a universal model using federated learning, a revolutionary AI approach, without having to reveal their personal data. With this strategy, anonymity is maintained while a secure learning environment is guaranteed. In contrast to the Workplace 31 dataset, this study presents a brand-new dataset made up of 23,326 photos obtained from eight different corporate sources and painstakingly organised into 31 classifications. It is noteworthy that this dataset is the first image classification dataset created specifically for federated learning. Along with the dataset, we also suggest the revolutionary federated learning algorithms Handled Cyclic and Handled Star. A cycle progression is established via Handled cycle, whereby a user gets weights from a previous user, adjusts them through localised training, and then transmits them to the following user. the essential job of image categorization within the context of computer vision is considerably improved by this novel approach, which highlights the development of federated learning. Traditional centralised deep learning model training requires centralised access to enormous datasets, which raises questions about data security and privacy. Federated learning addresses these issues by facilitating dispersed device collaboration for model training while restricting raw data exposure. this paper highlights the crucial role that federated learning has had in furthering AI research by giving an overview of its use in the context of image categorization. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image classification

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Maize Pests Identification Based on Improved YOLOv4-Tiny 5

Maize Pests Identification Based on Improved YOLOv4-Tiny

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5th international conference on computer Information Science and Artificial Intelligence, CISAI 2022

作者： Lin, Haiying Zhang, Yuyue Zhang, YuKun Zhang, Dexue Shandong University of Science and Technology Qingdao10424 China Binzhou University Binzhou10449 China

ISBN: (纸本)9781510662520

Agricultural pest identification occupies a key position in agricultural economy and development. the accurate identification of pests is the premise of agricultural pest control. In recent years, image processing technology and deep learning technology have rapidly developed. Some researches have been applied to the field of insect recognition. thus, some insect recognition deep learning models with good recognition accuracy and speed have been established. However, there is still much room for improvement when they were applied to the insect monitoring system deployed in the field. Considering the target recognition accuracy and speed, this paper selects the target detection algorithm YOLOv4-Tiny as the base model for insect recognition. the major advances are the attention mechanism and Spatial Pyramidal Pooling (SPP) structure as shown in: applying Convolutional Block Attention Module (CBAM) reduce computation and number of parameters;adopting SPP structure multi-scale pooling of input feature layers which increases the perceptual field and improves the robustness of the model. the experimental results show that the improved YOLOv4-Tiny model can significantly enhance the insect recognition accuracy. © 2023 SPIE.

关键词： Deep learning

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WSports-50: An image Dataset for Women’s Sport Action Classification 4th

WSports-50: An Image Dataset for Women’s Sport Action Class...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. the Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. then, these features are pooled from a collection of regular regions representing local discriminative information. the spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. the proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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FFDR: Design and implementation framework for face detection based on raspberry pi 12

FFDR: Design and implementation framework for face detection...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Alhajim, Dhafer Akbarizadeh, Gholamreza Ansari-Asl, Karim Islamic University Technical Engineering College Department of Computer Technical Engineering Najaf54001 Iraq Shahid Chamran University of Ahvaz Faculty of Engineering Department of Electrical Engineering Ahvaz Iran Shahid Chamran University of Ahvaz Faculty of Engineering Department of Electrical Engineering Ahvaz Iran

ISBN: (纸本)9781665412162

In today's world, we are surrounded by data of many types, but the abundance of image and video data available offers the data set needed for face recognition technology to function. Face recognition is a critical component of security and surveillance systems that analyze visual data and millions of pictures. In this article, we investigated the possibility of combining standard face detection and identification techniques such as machine learning and deep learning with Raspberry Pi face detection since the Raspberry Pi makes the system cost-effective, easy to use, and improves performance. Furthermore, some images of a selected individual were shot with a camera and a python program in order to do face recognition. this paper proposes a facial recognition system that can detect faces from direct and indirect images. We call this system FFDR, which is characterized by high speed and accuracy in the diagnosis of faces because it uses the Raspberry Pi 4 and the latest libraries and advanced environments in the Python language. © 2022 IEEE.

关键词： Face recognition

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Revolutionizing Human–computer Interaction: Unraveling the Power of Deep Learning Convolutional Neural Networks in Face recognition 4th

Revolutionizing Human–Computer Interaction: Unraveling the ...

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4th international conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Baharum, Aslina Halamy, Suhaida Ismail, Rozita Abdul Rahim, Emelia Mat Noor, Noorsidi Aizuddin Deris, Farhana Diana Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Selangor Malaysia School of Information Science College of Computing Informatics and Mathematics Universiti Teknologi MARA Cawangan Sarawak Kota Samarahan Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Kajang Malaysia Faculty of Business Management Universiti Teknologi MARA Cawangan Sarawak Sarawak Kota Samarahan94300 Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (纸本)9789819729760

Face recognition, being one of the most effective applications of image analysis, has recently received a lot of attention due to the huge implication in human–computer interaction (HCI). As the availability and eligibility to detect a person’s facial features, face recognition technology has been used in biometric detection applications as uses certain aspects of a person’s physiology to identify them. In addition, Deep Learning, under the subset of machine learning, can solve various problems, especially in image processing and face recognition. the advancement and development of Deep Learning can also enhance the use of the Convolution Neural Network (CNN) as the predominant model in the field of face recognition. the paper discusses the systems based on CNN approaches and algorithms and provides a review of the CNN face recognition approach. Furthermore, each paper’s details, such as used datasets, techniques, architecture, and obtained findings, hence the application are fully summarized and analyzed. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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A Non-extensive Entropy-Based Adaptive Multi-threshold image Segmentation Algorithm 5

A Non-extensive Entropy-Based Adaptive Multi-threshold Image...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Kou, Qiaoying Xiong, Jing Sun, Mingjie Ou, Congjie Huaqiao University College of Information Science and Engineering Xiamen China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

image segmentation plays important role in computer vision and image processing, and the threshold segmentation is known by its powerful and efficient performance. Entropy-based algorithm and variance-based algorithm are two main threshold segmentation algorithms. In present paper, non- extensive Tsallis entropy is adopted to multi-level image thresholding. the non-extensive parameter is determined by the gray-level histogram in a self-adaptive way, which indicates the long-range correlations among pixels in an image. the segmentation results show that the proposed algorithm is superior to the well-known Kapur multi-level thresholding and variance- based Otsu algorithm in terms of PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and uniformity measure. Furthermore, it is found that the superiority of the proposed algorithm becomes stable as the number of thresholds increases. © 2022 IEEE.

关键词： Entropy

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Study on visual odometry and optimization of SLAM system 4

Study on visual odometry and optimization of SLAM system

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4th IEEE international conference on Civil Aviation Safety and Information Technology, ICCASIT 2022

作者： Chengxu, Yao Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

With the development of computer technology and the increasing data processing capability and speed, the robot vision-based SLAM problem is gradually emerging. In this paper, based on the traditional vision odometry implementation scheme, the ORB feature extraction algorithm is used to extract the feature points of the image and calculate the BRIEF descriptors, and then a combination of PnP and improved ICP is used to solve the bit pose of the camera. In order to improve the accuracy and stability of the algorithm, the PROSAC algorithm is used to reject the mis-match in this paper. Finally, the algorithm is tested using the public dataset of TUM, which proves that the algorithm has faster computational speed and stability. © 2022 IEEE.

关键词： image enhancement

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Lip reading using external viseme decoding 12

Lip reading using external viseme decoding

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Peymanfard, Javad Reza Mohammadi, Mohammad Zeinali, Hossein Mozayani, Nasser Iran University of Science and Technology School of Computer Engineering Tehran Iran Amirkabir University of Technology Department of Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Lip-reading is the operation of recognizing speech from lip movements. this is a difficult task because the movements of the lips when pronouncing the words are similar for some of them. Viseme is used to describe lip movements during a conversation. this paper aims to show how to use external text data (for viseme-to-character mapping) by dividing video-to-character into two stages, namely converting video to viseme and then converting viseme to character by using separate models. Our proposed method improves word error rate by an absolute rate of 4% compared to the typical sequence to sequence lipreading model on the BBC-Oxford Lip Reading dataset (LRS2). © 2022 IEEE.

关键词： Speech recognition

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recognition of Characters using PCE based Convolutional LSTM Networks from Palaeographic Writings 4

Recognition of Characters using PCE based Convolutional LSTM...

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4th international conference on Innovative Trends in Information Technology, ICITIIT 2023

作者： Ezhilarasi, S. Umamaheswari, P. Raghavi, S. CEG Anna University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665494144

the historic paleographic writings that contributes to cultural heritage of India were inscribed on various materials such as stone inscriptions, rock carving, palm manuscripts, pots, coins, copper plates etc. Archaeological departments throughout the world have undertaken massive digitization projects to digitize the historical contents. But it is highly complicated as it involves images with complex backgrounds, noises and various illumination conditions. the paleographic writings are camera captured and processed for recognition of characters. A character recognition system is an inevitable tool to offer global visibility to the paleographic writings. Automatic character recognition is a challenging problem as in the proposed work it needs a cautious blend of image enhancement, patch extraction, feature extraction, classification and recognition techniques. this involves extracting the sequence of image patches and feature vector of the patches using Convolutional Neural Network and feeding the feature vectors using attention mechanism to recognize the character with LSTM model. As paleographic writings have lengthy sequence of characters which requires special attention during character recognition. the proposed work is an attempt to identify and recognize the historical Tamil paleographic writings by extracting the sequence of patches from the image and feeding them into a CNN-LSTM framework. the proposed method mainly consists of pre-processing, feature extraction, and character-level recognition. the LSTM network is built and the sequence of feature vectors is fed to the network and trained. the sequence of characters is recognized. the performance of the proposed method recorded an character recognition accuracy of 97.9%. © 2023 IEEE.

关键词： Character recognition

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