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5th International Conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Kate, Chennaiah Kalpana, C. Sharma, Arvind Yadav, Ajay Singh Kumar, Ashok Kumar, S. Sandeep St. Peter's Engineering College Department of Information Technology Telangana Hyderabad500100 India Npr College of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu Dindigul624401 India Government Women Engineering College Department of Electronics and Communication Engineering Rajasthan Ajmer305002 India Srm Institute of Science and Technology Department of Mathematics Uttar Pradesh Ghaziabad201204 India BanasthaliVidyapith Department of Computer Science Rajasthan 304022 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh 522502 India

ISBN: (纸本)9798350321425

In order to recognize patterns in images, this study tests the performance of many 'machine learning algorithms' and feature extraction methods. Here, synthetic photographs of handwritten digits are used to compare the performance of four machine learning methods ('deep learning, support vector machines, decision trees, and random forests') and two feature extraction strategies (raw pixel values and Histogram of Oriented Gradients). The efficacy of each algorithm is measured in terms of its 'accuracy, precision, recall, and F1 score', among others. Our findings also demonstrate that the Histogram of Oriented Gradients feature extraction method is good at collecting local gradient information in pictures and that deep learning and support vector machines obtain the best accuracy overall. The results of our research have significant ramifications for the future of machine learning techniques used in computer vision and handwriting recognition. Research in the future may test these methods on other datasets and picture kinds, or look into alternative feature extraction strategies and machine learning algorithms. © 2023 IEEE.

关键词： Feature extraction

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Facial Expression Recognition Using Transfer Learning with ResNet50 7th

Facial Expression Recognition Using Transfer Learning with R...

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7th International Conference on Inventive Systems and Control, ICISC 2023

作者： Hiremath, Shantala S. Hiremath, Jayaprada Kulkarni, Vaishnavi V. Harshit, B.C. Kumar, Sujith Hiremath, Mrutyunjaya S. Image Processing Sony India Software Centre Pvt. Ltd. Karnataka Bangalore560103 India Department of Computer Science and Engineering Visvesvaraya Technological University Belgaum590018 India Department of CSE Alvas Institute of Engineering and Technology Mijar Karnataka Moodbidri574225 India Department of Image Processing eMath Technology Pvt. Ltd. Karnataka Bangalore560072 India Artificial Intelligence and Machine Learning VVDN Technologies Pvt. Ltd. Karnataka Bangalore560066 India

ISBN: (纸本)9789819916238

Facial expression recognition mimics human coding abilities and delivers non-verbal human–robot communication cues. machine learning and deep learning techniques enable real-world computer vision applications. Deep learning-based facial emotion recognition models have under-fitted or over-fitted due to inadequate training data. They are using FER2013’s 7 picture categories. Face detection using AdaBoost, scaling with OpenCV, and contrast improvement with histogram equalization preprocess these pictures. These pre-processed pictures are given to the ResNet50 pre-trained network, which obtained 77.3% accuracy. Transfer learning improves this outcome. By running pre-processed pictures through ResNet50’s FC1000 layer, features are retrieved and trained using a multiclass nonlinear support vector machine (SVM) classifier with seven classes. Training with 89.923% accuracy creates a knowledge base. Emotion recognition techniques let robots understand people, which can improve HCI. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Embedded vision System Controlled by Dual Multi-frequency Tones 16th

Embedded Vision System Controlled by Dual Multi-frequency To...

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16th International Conference on Information Technology and applications, ICITA 2022

作者： Orlando Guerrero, I.J. Ruiz, Ulises Corte, Loeza Hernadez Paxtian, Z.J. Universidad de la Cañada Teotitlán de Flores Magón Oax Mexico Instituto nacional de astrofísica óptica y electrónica. Sta María Tonantzintla San Andrés Cholula Pue Mexico

ISBN: (纸本)9789811993305

An embedded vision system, based on the conjunction of a mobile, a DTMF (dual-tone multi-frequency) module, and a four-bit relay module, is presented in this paper. The mobile camera is employed to distinguish color characteristics of analyzed objects by means of digital processing. Each time a feature is distinguished, the mobile generates a different tone through the audio port, which is sent to the DTMF module to generate one of four available digital outputs. The booster module allows to amplify this digital signal, which can be used for a power amplifier. In this research, the linear velocity of the system was evaluated, from the moment the image is acquired until the power signal is activated, for this purpose, an oscilloscope was used to perform a timing analysis. The results show that the system cannot distinguish color characteristics of objects at a speed greater than 48 cm/s. This system is intended to be used in the food industry, as an alternative vision machine, object selector. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Power amplifiers

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image Style Transfer Based on VGG Neural Network Model

Image Style Transfer Based on VGG Neural Network Model

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2022 IEEE International Conference on Advances in Electrical Engineering and Computer applications, AEECA 2022

作者： Tao, Yilin Beijing Language and Culture University Beijing China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

image style transfer is an important research content related to image processing in computer vision. Compared with traditional artificial computing methods, deep learning-based convolutional neural networks in the field of machine learning have powerful advantages. This new method has high computational efficiency and a good style transfer effect. To further improve the quality and efficiency of image style transfer, the pre-trained VGG-16 neural network model and VGG-19 neural network model are used to achieve image style transfer, and the transferred images generated by the two neural networks are compared. The research results show that the use of the VGG-16 convolutional neural network to achieve image style transfer is better and more efficient. © 2022 IEEE.

关键词： Neural network models

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The Analysis of Srgb Color Space Based Density for Brain Tumor Segmentation 7th

The Analysis of Srgb Color Space Based Density for Brain Tum...

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7th International Symposium on Intelligent Informatics, ISI 2022

作者： Gangadharappa, S. Naveena, C. Aradhya, V. N. Manjunath Department of Computer Science and Engineering SJBIT Karnataka Bengaluru India Department of Computer Applications JSS Science and Technology University Mysuru Mysore India

ISBN: (纸本)9789811980930

Medical image processing is one of the significant fields to identify the diseases as earlier to diagnose them appropriately. The brain tumor segmentation process is sub branch of a medical image processing field. The computer vision and machine learning techniques provide an effective channel for the medical practitioners for diagnosing the diseases in an effective method. This research article implements the Srgb-based density analysis for isolating the brain tumor space in MRI images. Intensity values of a given input are normalized using Srgb color space and Gaussian filter to distinguish the tumor region from the background. The adaptive threshold technique helps identify the possible tumor space in brain MRI samples. The actual brain tumor space is extracted by performing the region properties such as area and density function. Finally, the accurate tumor space is detected by applying morphological functions with eliminating possible false positives. Performance metrics including recall, precision, and F-measure are used to assess the effectiveness of the proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tumors

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Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

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Multimedia Tools and applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

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DGNet: Effective image Rain Removal through Comprehensive Detection and Generation

DGNet: Effective Image Rain Removal through Comprehensive De...

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image processing, Computer vision and machine Learning (ICICML), International Conference on

作者： Xiaotian Wan Xuefeng Yan Computer Science and Technology Department Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Rain in real-world scenes is influenced by a multitude of environmental factors, presenting considerable challenges for single image deraining (SID) techniques. Current methodologies predominantly depend on intricate feature extraction modules to enhance visual quality, albeit on a limited subset of synthetic data. Nevertheless, due to the pronounced discrepancy between synthetic and real-world data, the efficacy of these methods in practical applications is diminished. To mitigate these limitations, we introduce a novel rain detection and generation network. In particular, we refocus the learning objective on rain mask identification, for which we develop a dedicated rain detection module. Subsequently, a pixel-wise filtering module is employed to utilize the derived mask information, thereby refining the image restoration process. Furthermore, we introduce a Rain Generation Module designed to bridge the gap between synthetic and real data during network training. The experimental results, derived from both synthetic and real-world datasets, substantiate the superior performance of our proposed approach.

关键词： Training Computer vision Visualization Rain Filtering Refining machine learning image restoration Object recognition Synthetic data

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Challenges in image Matching for Cultural Heritage: An Overview and Perspective 21st

Challenges in Image Matching for Cultural Heritage: An Overv...

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

作者： Bellavia, F. Colombo, C. Morelli, L. Remondino, F. Univ Palermo Palermo Italy Univ Florence Florence Italy Bruno Kessler Fdn FBK Trento Italy Univ Trento Trento Italy

ISBN: (纸本)9783031133213;9783031133206

image matching, as the task of finding correspondences in images, is the upstream component of vision and photogrammetric applications aiming at the reconstruction of 3D scenes, their understanding and comparison. Such applications are of special importance in the context of cultural heritage, as they can support archaeologists to digitally preserve, restore and analyze antiquities, but also to compare their changes over time. The success of deep learning, now firmly established, paired with the evolution of computer hardware, has led to many advances in image processing, including image matching. Despite this progress, image matching still offers challenges, in terms of the matching process itself but also on other practical and technical aspects. This paper gives an overview of the current status of the research in image matching with a particular focus on cultural heritage, presenting both strengths and weaknesses of the most recent approaches by means of visual comparisons on exemplar challenging image pairs. Besides assisting researchers and practitioners in the choice of the most suitable solution for a given task, this analysis also suggests lines of research worth to be investigated by the community in the near future.

关键词： image matching Cultural heritage SIFT Deep learning SfM

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Application of Computer 3D image vision Algorithm in Intelligent image Recognition System

Application of Computer 3D Image Vision Algorithm in Intelli...

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IEEE International Conference on Artificial Intelligence and Computer applications (ICAICA)

作者： Yuan Li Xin Yu Modern Finance Industry School Shandong Institute of Commerce and Technology Jinan Shandong China

In this paper, the 3D space imaging model of machine vision is constructed. Starting from the traditional machine vision image processing algorithm flow, the image denoising process and target tracking process are optimized. The method uses the camera to collect the image and video information of the measured object, and transmits it to the controller. The controller corrects the signal obtained by the wireless sensor in the database to reproduce the position of the measured object and the 3D image. A real-time tracking method of motion trajectory based on computer vision is presented. The object autonomous capture, 3D position and motion trajectory tracking. Simulation experiments show that this method is quite different from conventional image processing methods. This method has the advantages of small computation, fast running speed and good real-time performance. It meets the needs of embedded image processing.

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Multi-level Taxonomy Review for Sign Language Recognition: Emphasis on Indian Sign Language

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION processing 2023年第1期22卷 1-39页

作者： Bahia, Nimratveer Kaur Rani, Rajneesh Natl Inst Technol Jalandhar India

With the phenomenal increase in image and video databases, there is an increase in the human-computer interaction that recognizes Sign Language. Exchanging information using different gestures between two people is sign language, known as non-verbal communication. Sign language recognition is already done in various languages;however, for Indian Sign Language, there is no adequate amount of work done. This article presents a review on sign language recognition for multiple languages. Data acquisition methods have been over-viewed in four ways (a) Glove-based, (b) Kinect-based, (c) Leap motion controller, and (d) vision-based. Some of them have pros and cons that have also been discussed for every data acquisition method. applications of sign language recognition are also discussed. Furthermore, this review also creates a coherent taxonomy to represent the modern research divided into three levels: Level 1 Elementary level (Recognition of sign characters), Level 2 Advanced level (Recognition of sign words), and Level 3 Professional level (Sentence interpretation). The available challenges and issues for each level are also explored in this research to provide valuable perceptions into technological environments. Various publicly available datasets for different sign languages are also discussed. An efficient review of this article shows that the significant exploration of communication via sign acknowledgment has been performed on static, dynamic, isolated, and continuous gestures using various acquisition methods. Comprehensively, the hope is that this study will enable readers to learn new pathways and gain knowledge to carry out further research work in the domain related to sign language recognition.

关键词： Indian sign language (ISL) sign language recognition (SLR) vision-based feature extraction support vector machine (SVM) region of interest (ROI)

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