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Classification by learning of wavelet and texture features 1st

Classification by learning of wavelet and texture features

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1st international conference on Frontiers in Smart Systems Technologies, ICFSst 2019

作者： Bagavathi, C. Saraniya, O. Department of Electronics and Communication Engineering Government College of Technology Coimbatore641013 India

ISBN: (纸本)9789811550287

image classification is one among the significant tasks that can benefit industry through advancement in science and technology. Texture images possess a specific pattern that can be utilized to uniquely identify a texture class. Classification of Texture images can be done through proper selection of parameter that uniquely identifies an image in a group containing images of different classes. machine learning approaches are currently the choice of implementation to provide accuracy and robustness in various fields like automatic image recognition, registration, and analysis. This work addresses machine learning methods to classify learnt texture samples through feature fusion of parameters obtained through wavelet and texture analysis. © Springer Nature Singapore Pte Ltd 2021.

关键词： Support vector machines

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Colour Detection for Interior Designs Using machine learning 1st

Colour Detection for Interior Designs Using Machine Learning

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1st international conference on Advancements in Interdisciplinary Research, AIR 2022

作者： Dewingong, Tarcisius Forjong Afor, Mary Echabuo Mishra, Pradeep Kumar Mishra, Sasmita Mishra, Gouri Sankar Aliyu, Bello Ibrahim Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Business Management C. V. Raman Global University Odisha Bhubaneswar India

ISBN: (纸本)9783031237232

Colours have a significant impact on human psychology and emotions. An essential part of interior design is choosing appropriate colours for a facility’s areas. Simply gazing at the colours does nothing but satisfy the eyes. However, understanding its origins and selecting the best colour becomes difficult. This paper presents a solution for colour detection systems in interior design. Using machine learning to teach the system how to recognise colour in RGB space and predict its name and values. Open-Source computer vision (OpenCV) is used in this system for computer vision and to process the image which has to be detected while Pandas a python library collects the data from the dataset and analysis them to give the appropriate prediction of the colour name and values. The Colour Detection method is implemented using the Colour Detection and Segmentation approach in image processing and the result is displayed by double-clicking on any part of the image to detect the colour at that point of the image. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computer vision

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1st international conference on Intelligent Systems in Computing and Communications, ISCComm 2023

1st International Conference on Intelligent Systems in Compu...

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1st international conference on Intelligent Systems in Computing and Communications, ISCComm 2023

ISBN: (纸本)9783031756078

The proceedings contain 53 papers. The special focus in this conference is on Intelligent Systems in Computing and Communications. The topics include: Towards Hands-Free Computing: AI Virtual Mouse Interface Powered by Gestures;smartAgro: Precision Yield Prediction, Crop Insights, and Real-Time Dashboard;Artificial Intelligence with MRI-Guided Radiation Therapy for Cancer Treatment;Software Requirements to UML Class Diagrams Using machine learning and Rule-Based Approach;CFALEA_LstM: Adaptive Lotus Effect Algorithm Enabled Long Short-Term Memory for Rainfall Prediction Using Time Series Data;a Deep learning Survey on Diseases Prediction and Detection in Health Care;fault Diagnosis in Belts Using Signal processing Techniques and machine learning;machine learning Techniques Based Chronic Kidney Disease Detection with Performance Analysis of Fuzzy Rough Set and Correlation Attribute Selection;segmentation and Classification of Unharvested Arecanut Bunches Using Deep learning;enhanced Satellite image Fusion Using Deep learning and Feature Extraction Techniques: A Survey;intelligent Aircraft Antiskid Braking Systems – A Review;artificial Neural Networks Applied in the Detection of Breast Cancer;supervised and Unsupervised learning Techniques for Malware Classification Based on Opcode Frequency Features;predictive Analytics for Diagnosing Alzheimer’s Disease Using Artificial Intelligence and machine learning Algorithms;A Comprehensive study on Artificial Intelligence (AI) Driven Internet of Healthcare Things (IOHT);predictive Models for the Early Diagnosis and Prognosis of Knee Osteoarthritis Using Deep learning Techniques;predicting Salinity Resistance of Rice at the Seedling stage: An Evaluation of Transfer learning Methods;Multi-camera HD Pedestrian Dataset for Person Detection and Re-identification;Fire Detection System Using Deep CNN.

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Medical image Synthesis Workbench Supporting Multiple Modalities With Feature-Level Transforms

Medical Image Synthesis Workbench Supporting Multiple Modali...

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Innovations in High Speed Communication and Signal processing (IHCSP), international conference on

作者： Nitin Sharma Juan Carlos Cotrina-Aliaga John Philip Bhimavarapu Pavan Kumar Illa Priya Jyotiyana IMS Engineering College Uttar Pradesh India Faculty of Human Medicine Universidad Privada San Juan Bautista Chincha Peru Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Information Technology Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Hyderabad Computer Science and engineering Jaipur engineering college and research centre Jaipur India

Multi-modal In recent years, medical image synthesis has arisen as a holistic analytic strategy that typically employs many transformation methods. When it comes to medicine, multi-modal image synthesis refers to the technique of combining images from several imaging modalities to enhance image quality while maintaining detail. image processing, computer apparition, pattern acknowledgment, machine knowledge, and artificial intellect are just few of the many cutting-edge fields that fall under the umbrella of medical image synthesis. The synthesis of many medicinal imaging modalities has been used extensively in the clinic to help doctors better understand the lesion. This paper presents the firefly algorithm, a multi-learning model workbench that minimises squared errors and maximises synthesis quality via weighted entropy. MATLAB simulations are used to compare the proposed workbench against state-of-the-art methods in terms of peak signal-to-noise ratio, Mean square error, correlation, and precision. The proposed workbench performs well across the board and requires less computing power.

关键词： Technological innovation PSNR image synthesis Computed tomography Magnetic resonance imaging Computational modeling Signal processing algorithms

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1st international conference on Intelligent Systems in Computing and Communications, ISCComm 2023

1st International Conference on Intelligent Systems in Compu...

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1st international conference on Intelligent Systems in Computing and Communications, ISCComm 2023

ISBN: (纸本)9783031756047

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Skew Angle Detection and Correction in Text images Using RGB Gradient 21st

Skew Angle Detection and Correction in Text Images Using RGB...

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21st international conference on image Analysis and processing (ICIAP)

作者： Rocha, Bruno Vieira, Gabriel Pedrini, Helio Fonseca, Afonso Fernandes, Deborah de Lima, Junio Cesar Ferreira, Julio Cesar Soares, Fabrizzio Univ Fed Goias Inst Informat Goiania Go Brazil Fed Inst Goiano Comp Vis Lab Urutai Go Brazil Univ Estadual Campinas Inst Comp Campinas SP Brazil

ISBN: (纸本)9783031064302;9783031064296

Detecting and correcting skew angles is critical to success in document layout analysis and optical character recognition tasks, as they are more susceptible to failure when on uneven skews. In automation such as postal systems, library management, office business, and banking data entry, skew angle estimation is crucial to improve procedures response. Although different works have addressed this subject, due to the variability in the input data, many solutions are restricted to a specific language, texts whose contents are within a controlled scope, and entries that differentiate printed from handwritten texts. This paper introduces a new method based on RGB gradient capable of detecting and correcting skew angles in different types of documents. We evaluate the proposed method using two public databases and compare our results with other techniques cited in the literature. In general, our proposal achieved results superior to the approaches compared in all groups of documents in the database. Furthermore, we show that our method can work accurately in various text orientations, and it can work efficiently against documents containing short and sparse text lines, non-textual objects, and image noises caused by imperfect scanning.

关键词： RGB gradient Skew detection image rotation Text image processing

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Enhancing Depth Quality of stereo Vision using Deep learning-based Prior Information of the Driving Environment 25

Enhancing Depth Quality of Stereo Vision using Deep Learning...

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25th international conference on pattern recognition (ICPR)

作者： Li, Weifu John, Vijay Mita, Seiichi Huazhong Agr Univ Coll Sci Wuhan 430062 Peoples R China Toyota Technol Inst Res Ctr Smart Vehicles Tempaku Ku 2-12-1 Hisakata Nagoya Aichi 4688511 Japan

ISBN: (纸本)9781728188089

Generation of high density depth values of the driving environment is indispensable for autonomous driving. stereo vision is one of the practical and effective methods to generate these depth values. However, the accuracy of the stereo vision is limited by texture-less regions, such as sky and road areas, and repeated patterns in the image. To overcome these problems, we propose to enhance the stereo generated depth by incorporating prior information of the driving environment. Prior information, generated by deep learning-based U-Net model, is utilized in a novel post-processing mathematical framework to refine the stereo generated depth. The proposed mathematical framework is formulated as an optimization problem, which refines the errors due to texture-less regions and repeated patterns. Owing to its mathematical formulation, the post-processing framework is not a black-box and is explainable, and can be readily utilized for depth maps generated by any stereo vision algorithm. The proposed framework is qualitatively validated on the acquired dataset and KITTI dataset. The results obtained show that the proposed framework improves the stereo depth generation accuracy.

关键词： stereo vision

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A Model for Content-Based image Retrieval Using machine learning

A Model for Content-Based Image Retrieval Using Machine Lear...

引用

Circuits, Power and Intelligent Systems (CCPIS), international conference on

作者： Yash Mahajan Priya Batta Muskan Sharma Devansh Saxena Department of Computer Science and Engineering Chandigarh University Mohali India

With the value of digital data increasing in recent years, substantial improvements have been made in image processing. It is now simpler to produce large image datasets. picture processing is carrying out a variety of processes to improve a picture or extract informative data from it. With several theories and models relating to the retrieval of images from the collection of images called datasets, it has developed into a crucial study subject within the disciplines of engineering and computer science. A well-liked paradigm for the retrieval of images, image retrieval based on content is quicker and more advantageous than textual indexation. The authors of this study provide a paradigm for feature-based picture retrieval. Based on a user query, the model searches a database for the most relevant photographs and presents them with the least amount of vector distance. The suggested model can greatly enhance image retrieval systems and the applications of such systems in numerous sectors. Overall, this study makes a positive contribution to attempts to advance image processing technologies and their usefulness in the contemporary world.

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The AIRES-CH Project: Artificial Intelligence for Digital REstoration of Cultural Heritages Using Nuclear Imaging and Multidimensional Adversarial Neural Networks 21st

The AIRES-CH Project: Artificial Intelligence for Digital RE...

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21st international conference on image Analysis and processing (ICIAP)

作者： Bombini, Alessandro Anderlini, Lucio Dell'Agnello, Luca Giaocmini, Francesco Ruberto, Chiara Taccetti, Francesco Ist Nazl Fis Nucl Florence Sect Via Bruno Rossi 1 I-50019 Sesto Fiorentino FI Italy INFN CNAF Viale Carlo Berti Pichat 6 I-40127 Bologna BO Italy

ISBN: (纸本)9783031064272;9783031064265

Artificial Intelligence for digital REstoration of Cultural Heritage (AIRES-CH) aims at building a web-based app for the digital restoration of pictorial artworks through Computer Vision technologies applied to physical imaging raw data. Physical imaging techniques, such as XRF, PIXE, PIGE, and FTIR, are capable of exploring a wide range of wavelengths providing spectra that are used to infer the chemical composition of the pigments. A multidimensional neural network, specifically designed to automatically restore damaged or hidden pictorial work, will be deployed on the INFN-CHNet Cloud as a web service, freely available to authenticated researchers. In this contribution, we report the status of the project, its current results, the development plans as well as future prospects.

关键词： image processing for cultural heritage Deep learning X-ray fluorescence imaging Web technologies

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Detection and Classification of Brain Tumor from MR images using an Improvised image Analysis Algorithm 1

Detection and Classification of Brain Tumor from MR Images u...

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1st international conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Babu, K. Dinesh Singh, C. Senthil Department of Electronics and Communication Engineering Adhi College of Engineering and Technology Tamil Nadu Kanchipuram631 605 India Department of Electronics and Communication Engineering Infant Jesus College of Engineering Tamil Nadu Thoothukudi628 851 India

ISBN: (纸本)9781607685395

Early detection of brain tumour and its classification are predominantly achieved by conventional methods of image processing and machine learning. Brain tumour, known for its rapid development in terms of size and effects over human health, has to be detected immediately from the onset for an effective diagnosis. Unless a treatment plan is defined to mitigate further growth, there are high chances of fatality. Detecting and classifying processes are an exigent task even for experienced radiologists. Segmentation and feature extraction are traditional image processing techniques used for the purpose, nowadays, a computer aided diagnosis system is merged with machine learning and deep learning strategies for distinguishing tumour cells. The proposed system is an investigation of classifying brain tumour in its early stages using enriched feature sets support vector machine classifiers for increasing the precision of accuracy. Dice Coefficient was introduced along with the said methods to ensure classification accuracy. Enrichment of the features was achieved by area based analysis with prospective growth metrics on a four quadrants designated on two dimensional plane. Genetic algorithm with soft computing techniques improvised the classification accuracy. Two standard and open source data sets deployed the proposed model which resulted in an accuracy of 98%, sensitivity of 92% and the specificity of 98.1% as an average. The proposed model also consumed lesser time than the conventional standards with respect to detection and classification of brain tumours. © The Electrochemical Society

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