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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Hyperspectral image Dimensionality Reduction Using Inter-Band Cross-Correlation and K-Means Clustering Algorithm

Hyperspectral Image Dimensionality Reduction Using Inter-Ban...

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Workshops on image processing Theory, Tools and applications, IPTA

作者： Henry Ndu Akbar Sheikh-Akbari Koushlendra K. Singh School of Built Environment Engineering and Computing Leeds Beckett University Leeds United Kingdom Machine Vision and Intelligence Lab National Institute of Technology Jamshedpur India

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

Hyperspectral imaging has become crucial in various domains, especially for the accurate detection of human veins in medical diagnostics, though managing the extensive data from hyperspectral (HS) images remains a challenge. To improve data handling during analysis, dimensionality reduction methods are frequently utilized. This paper presents a dimensionality reduction method for HS images using HS image inter-band cross-correlation and the K-means clustering algorithm. The proposed method computes inter-band correlations across all bands of the input HS image, which form a 2D correlation matrix. Eigen-decomposition is applied to the resulting matrix, extracting its eigenvectors and eigenvalues. The k-mean clustering algorithm is then applied to a selection of eigenvectors representing the largest eigenvalues, splitting the eigenvectors into several clusters. The reduced HS image is generated by averaging each cluster's image bands. The proposed dimensionality reduction method together with the Support Vector machine (SVM) classifier was then used for vein detection in HS images. The HyperVein image dataset was used to generate experimental results. Experimental results were generated for the proposed method and Principal Component Analysis (PCA) and Folded PCA (FPCA). Results show the proposed method outperforms PCA and FPCA in most performance metrics.

关键词： Dimensionality reduction Support vector machines Correlation Accuracy Clustering algorithms Eigenvalues and eigenfunctions Medical diagnosis Principal component analysis Hyperspectral imaging Morphological operations

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Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach

Road Condition Detection and Crowdsourced Data Collection fo...

引用

Workshops on image processing Theory, Tools and applications, IPTA

作者： Md Saroar Jahan Mominul Islam Md Sanjid Hossain Jhuma Kabir Mim Mourad Oussalah Nasrin Akter Centre for Machine Vision and Signal Processing (CMVS) Faculty of ITEE University of Oulu Oulu Finland Dept. of CSE Daffodil International University Dhaka Bangladesh Computer Vision and Pattern Recognition LUT Univerity Lappeenranta Finland CMVS Faculty of ITEE University of Oulu Oulu Finland

Bangladesh is one of the countries struggling to prevent road accidents, which is a global cause for concern. An early warning system that indicates road conditions can contribute to the prevention task. For this purpose, a deep-learning based approach using a Convolutional Neural Network (CNN) to learn from random road images the safety factor is developed. This results in a three-class categorization: (i) Severely risky roads, (ii) Mildly risky roads, and (iii) Normal roads. The application of deep learning techniques in this study yields an accuracy of 95.5% in detecting problematic road conditions. Furthermore, based on the study’s findings, a mobile application has been developed. The app enables real-time crowdsourced data collection of road conditions and provides a platform for users to share this information in real-time with other drivers, thereby, contributing to prevent accidents and raise awareness among drivers and users by pinpointing the location of the risky road. Finally, crowdsourced data has been reused to update the trained model, which further improves the classifier accuracy.

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Analytical Overview on Transfer Learning in processing Dental X-rays 22

Analytical Overview on Transfer Learning in Processing Denta...

引用

5th International conference on Control and Computer vision, ICCCV 2022

作者： Al Mokdad, Seba Al Houria, Anas Abu Talib, Manar Moufti, Mohammad Bouridane, Ahmed Nasir, Qassim College of Computing and Informatics Department of Computer Science University of Sharjah United Arab Emirates

ISBN: (纸本)9781450397315

Dental x-rays have been a standard piece of dental equipment for many years and are an indispensable diagnostic tool for dentists to detect tooth damage or disease. Recent research has focused on employing computer vision algorithms to automate analysis of dental x-rays. Our study aims to review the work done using transfer learning in dental image processing. AI solutions for dental images have been developed for many purposes, including examining tooth cavities (caries) and restorations and abnormalities in the maxillary sinuses. They have also been used to classify dental implants and determine gender in forensic studies. Transfer Learning is a new approach that is being used to solve a problem that classic deep learning and machine learning techniques could not solve: that of data limitation. Our search has investigated 80 research papers, of which 30 were relevant and analyzed in this paper. The identified studies have discussed a variety of transfer learning models to process different types of x-rays and have reported their efficacy using a variety of metrics. Transfer learning was used to solve various problems depending on the research question. Some papers compared the performance of transfer learning with that of dental experts in analyzing x-ray images, the accuracy of which were surprisingly close to equal. Although the results of the majority of dental applications performed using transfer learning models are encouraging, future research will need to solve the shortcomings highlighted in the present review. © 2022 ACM.

关键词： Diagnosis

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Proceedings of the International conference on image processing and vision Engineering, IMPROVE 2021

Proceedings of the International Conference on Image Process...

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2021 International conference on image processing and vision Engineering, IMPROVE 2021

ISBN: (纸本)9789897585111

The proceedings contain 21 papers. The topics discussed include: contactless optical respiration rate measurement for a fast triage of SARS-CoV-2 patients in hospitals;adversarial unsupervised domain adaptation guided with deep clustering for face presentation attack detection;limitations of local-minima gaze prediction;biometric authentication system based on hand geometry and palmprint features;benefits of layered software architecture in machine learning applications;deep visio-photoplethysmography reconstruction pipeline for non-invasive cuff-less blood pressure estimation;and advanced car driving assistant system: a deep non-local pipeline combined with 1D dilated CNN for safety driving.

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Computer vision for Supporting image Search

Computer Vision for Supporting Image Search

引用

7th International conference on Advances in visual Informatics, IviC 2021

作者： Smeaton, Alan F. Insight Centre for Data Analytics Dublin City University 9 Glasnevin Dublin Ireland

ISBN: (纸本)9783030902346

Computer vision and multimedia information processing have made extreme progress within the last decade and many tasks can be done with a level of accuracy as if done by humans, or better. This is because we leverage the benefits of huge amounts of data available for training, we have enormous computer processing available and we have seen the evolution of machine learning as a suite of techniques to process data and deliver accurate vision-based systems. What kind of applications do we use this processing for? We use this in autonomous vehicle navigation or in security applications, searching CCTV for example, and in medical image analysis for healthcare diagnostics. One application which is not widespread is image or video search directly by users. In this paper we present the need for such image finding or re-finding by examining human memory and when it fails, thus motivating the need for a different approach to image search which is outlined, along with the requirements of computer vision to support it. © 2021, Springer Nature Switzerland AG.

关键词： Computer vision

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image Captioning for Information Generation

Image Captioning for Information Generation

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International conference on Computer Communication and Informatics (ICCCI)

作者： Gurvansh Vohra Lakshay Gupta Deepika Bansal Bhoomi Gupta Department of IT Maharaja Agrasen Institute of Technology Delhi India

In the AI applications for natural language definitions, image captioning is a field that is expanding quickly. It attempts to capture meaningful interpretations of the interactions between the acquired picture data from datasets and the sentence meanings. It combines Long Short-Term Memory type RNN over phrases and CNN (Convolutional Neural Networks) picture reading techniques so that the image can be understood by the system and output it in a common language. This study intermingles the vision of the computer and natural language processing (NLP) to create machine that enhances visual information such as photos by giving braille legible captions for those who are blind to better understand what is going on around them.

关键词： visualization Braille Recurrent neural networks Natural language processing Convolutional neural networks Informatics Artificial intelligence

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The virtual Air Canvas Using image processing

The Virtual Air Canvas Using Image Processing

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International conference on

作者： K Pavithra A Geetha R Chinnaiyan Department of Computer Science and Engineering Alliance University Bengaluru Karnataka India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

One of the most interesting and challenging research focuses on pattern recognition and image processing has emerged in recent days is writing in the air. In many different applications, it can improve the interface between a machine and a human and offers a substantial contribution to the development of automated operations. In the field of computer vision, object tracking is considered a key challenge. The method of analyzing a video usually consists of three primary steps: recognizing the object, tracking its movement from frame to frame, and finally evaluating its behavior. Choosing an adequate object representation, selecting tracking features, identifying objects, and tracking them are the four problems taken into consideration for object tracking. Object tracking algorithms are widely used in many real-world applications, including autonomous surveillance, video indexing, and vehicle navigation. This work exploits this gap by developing a motion-to-text converter that may be used as software for wearable intelligent devices that allow writing in the air. The proposed work acts as a recorder of rare gestures. Computer vision will be utilized to track the finger’s path. With the generated text, messages, emails, and other kinds of correspondence can all be sent. It will enable effective communication for the deaf. Keywords— object, emoji’s, image color, camera

关键词： YOLO Handwriting recognition Computer vision Writing Software Object tracking Character recognition

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Recent Developments in the Quality Evaluation of Rice Disease Detection System

Recent Developments in the Quality Evaluation of Rice Diseas...

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Advances in Computing, Communication Control and Networking (ICAC3N), International conference on

作者： Pardeep Seelwal Tilak Raj Rohilla Department of Computer Science and Applications Baba Mastnath University Rohtak India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

The recent progress in the field of computer vision has facilitated the implementation of diverse applications across several domains of human existence. One notable domain of application pertains to the classification of plant diseases, with a particular focus on rice plants. However, the classification of rice plants has shown itself as a multifaceted challenge, necessitating additional advancements in this field. The classification of rice diseases poses considerable difficulties as a result of similarities between different classes and the irregular characteristics within each class. The careful selection of data acquisition sensors and the adoption of an appropriate feature representation approach are of utmost importance in light of the extensive range of applications in this field. In recent years, there has been a growing utilisation of computer vision techniques for the purpose of evaluating food quality. This paper provides an overview of the current advancements in techniques and their application in conjunction with diverse sensing devices for the purpose of detecting plant problems. The identification and resolution of significant challenges related to defect detection in rice plants entail different technical obstacles. This study provides an overview of the latest developments in computer vision methodologies used for assessing the quality of rice plants. This study showcases the potential of image processing techniques in the assessment of rice plant quality. Additionally, it highlights several unresolved difficulties that require additional investigation to facilitate the widespread implementation of image processing technology for evaluating the quality of rice plants.

关键词： Computer vision Plant diseases image resolution Plants (biology) Data acquisition Process control machine learning

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machine Learning: The Driving Force Behind Intelligent Systems and Predictive Analytics

Machine Learning: The Driving Force Behind Intelligent Syste...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International conference on

作者： Deepali virmani Md. Atheeq Sultan Ghori Nishant Tyagi R. P. Ambilwade Priyanka Rajesh Patil M. K. Sharma Vivekananda Institute of Professional Studies-Technical Campus School of Engineering and Technology Department of Computer Science and Engineering Telangana University Nizamabad Telangana State Management G.L. Bajaj Institute of Management and Research Greater Noida Computer Science National Defence Academy Pune Department of Electronics and Telecommunication Engineering Prof Ram Meghe College of Engineering and Management Badnera Mathematics Chaudhary Charan Singh University Meerut Uttar Pradesh India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This technical analysis explores the profound impact of machine learning on intelligent systems and predictive analytics. It examines algorithmic fundamentals and explores models such as linear regression, support vector machines and neural networks, which are the pillars of supervised learning. The research explores practical applications in natural language processing, where advanced neural network architectures such as Transformers redefine, language understanding and computer vision using convolutional neural networks for image recognition and object recognition. In addition, the analysis covers the basic steps of data processing, including data cleaning, transformation and refinement, as well as feature design and model optimization. In addition, the research addresses the challenges of machine learning, emphasizing the importance of reducing bias and ethical considerations. The study concludes with a look into the future of machine learning, envisioning advances in quantum computing to solve complex problems, combinatorial learning for distributed data problems, and synthetic data generation as a solution to data protection problems. This comprehensive study provides an overview of the current state of machine learning, challenges and promising developments in intelligent system innovation and predictive analytics.

关键词： Technological innovation Ethics Computer vision Quantum computing Computational modeling Neural networks Force

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Deep Learning Hybrid Technique for Generation of image Caption

Deep Learning Hybrid Technique for Generation of Image Capti...

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Signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International conference on

作者： Rakshith N Manoj Gowda B K Preetham N Tejas M Mohammed Ifraz Baig Dept. of Information Science and Engineering PES college of enginnering mandya Mandya India dept. of Information Science and Engineering PES college of enginnering mandya Mandya India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

image captioning is a fascinating and demanding work with applications in many different fields, including image retrieval, organizing and finding user-interested images, etc. It has enormous potential to replace the tedious process of creating captions for photos, and it works particularly well with massive amounts of picture data. Deep neural network-based techniques have recently shown significant success in the domains of language synthesis, machine translation, and computer vision. In this research, we present a paradigm based on encoder-decoders that may produce grammatically acceptable image captions. This model uses LSTM as the decoder and VGG16 Hybrid Places 1365 as the encoder. Model is evaluated using all standard metrics such as BLEU. Experimental results indicate that the proposed model obtained a BLEU-1 score 0.603774, BLEU-2 score 0.388514, BLUE-3 score 0.244706 on Flickr8k dataset. By comparison with the state-of-the-art techniques, the suggested strategy produced a noteworthy performance. We also present the outcomes of caption creation from real sample photographs, which supports the validity of the suggested method, in order to assess the model's effectiveness even further.

关键词： Measurement Deep learning Computational modeling image retrieval Signal processing Hybrid power systems Decoding

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