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Intelligent Computing and Control for Engineering and Business systems (ICCEBS)

作者： M. Nalini P. Sriharipriyan P. Matheswaran K.v. vikash M. Sudharsan Electronics and Instrumentation Engineering Sri Sairam Engineering College Chennai India

A non-invasive method for early anemia identification. The technology uses image processing and analysis to extract important characteristics from pertinent regions, including the fingernails or conjunctiva. With the use of sophisticated algorithms, these characteristics will be connected to hemoglobin levels to provide an accessible smartphone application for anemia screening. The goal of the project is to advance non-invasive, effective diagnostic techniques that will facilitate early intervention and improve access to healthcare.

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Automated Defect Detection and Segregation in Dyed Fabrics Using image processing Techniques

Automated Defect Detection and Segregation in Dyed Fabrics U...

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Electronics and Sustainable Communication systems (ICESC), 2020 International Conference on

作者： K. R. Prabha Aathavan. v Harshit. K Indhumathi. K Department of ECE Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This study addresses the critical need for efficient quality control in textile manufacturing, aiming to enhance the production of high-quality fabric to meet consumer demands and industry benchmarks. Despite the meticulous cleaning and dyeing processes involved in fabric production, there remains a challenge in identifying and segregating defective sections, which can potentially compromise the overall quality of the fabric. To overcome this challenge, this research study proposes an innovative solution: Automated Quality Control in Textile Manufacturing, utilizing advanced image processing techniques. The proposed system employs high-resolution cameras and image processing algorithms to analyze the fabric's surface in real-time. By leveraging predefined criteria and machine learning models, the system can accurately detect and isolate flawed sections within the fabric. These criteria encompass a range of defects, including stains, discolorations, or irregular patterns that deviate from established quality standards. This approach improves quality control in textile manufacturing, providing manufacturers with a reliable and efficient means of identifying and addressing defects in fabric production. By automating the quality control process, manufacturers can enhance productivity, reduce waste, and ultimately deliver higher-quality products to consumers.

关键词： Productivity image processing Process control Quality control Fabrics Real-time systems Manufacturing Reliability Surface treatment Standards

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Building Structures image Matching Using Convolutional Neural Network

Building Structures Image Matching Using Convolutional Neura...

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systems of Signals Generating and processing in the Field of on Board Communications

作者： N. A. Andriyanov R. G. Magdeev v. E. Dementyiev Financial University under the Government of the Russian Federation Moscow Russia Ulyanovsk State Technical University Ulyanovsk Russia National Research Mordovia State University Saransk Russia

The paper examines the features of combining multi-temporal and multi-angle images of building structures in order to identify critical changes. It is proposed to carry out such a combination on the basis of high-speed pseudogradient algorithms. The focus of the work is the problem of identifying areas of attention that are the source of combined points. It is proposed to perform such selection using convolutional neural networks of the YOLO family. The consistency of this approach is shown and estimates of the resulting gains are also calculated. It is also suggested to use different optimization methods for improving the processing speed. So the YOLO detector will be very high performance and efficient real time detector.

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A Novel Strategy to Enhance the Accuracy of Human Eye Pupil Detection using Hybrid Convolutional Neural Network

A Novel Strategy to Enhance the Accuracy of Human Eye Pupil ...

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Intelligent algorithms for Computational Intelligence systems (IACIS), International Conference on

作者： D. Gokulavani J. Mohana Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

This study describes the implementation of Hybrid Convolutional Neural Network techniques for the segmentation of human pupils as compared with Random Forest. Recognizing and separating the pupil area in ocular images such that eye tracking can be achieved. Technique components include feature extraction, training, testing, evaluation, hybrid model selection, post-processing, and data set preparation. During preprocessing high-quality eye images are generated to identify and classifies the dataset's pupil regions. Hybrid CNN, the combination of Mednet and LSTM(Long short term Memory) model and Random Forest are compared after the image characteristics have been determined. Models are trained and adjusted using CASIA,MMU, Iris v4 dataset, and metrics like accuracy, precision, recall, and F1 score are evaluated. After the initial segmentation, the segmented pupil regions have been altered in post-processing. The accuracy of Hybrid CNN was 96.27, while that of Random Forest was 95.17. By comparing the accuracy acquired using Hybrid CNN to Random Forest, the outcome analysis indicates that Hybrid CNN performs better overall.

关键词： Measurement Training image segmentation Accuracy image recognition Convolutional neural networks Pupils Random forests Testing Iris recognition

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Smart Healthcare: A Logical Development of Medical image processing Based Sleep Apnea Disease Detection Using Elevated Neural Classification Principle

Smart Healthcare: A Logical Development of Medical Image Pro...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Jagadeesh. P Radhamani. v T. Ravi G. Charulatha D.Y. Shahare Hamzeh J Aljawawdeh Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering college Chennai Department of ECE Rajalakshmi Engineering College Chennai Department of ECE Sathyabama Institute of Science and Technology Chennai Department of Biomedical Engineering Selvam College of Technology Namakkal Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Department of Software Engineering faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Entirely common sleep disorder is the dangerous wakefulness (Apnea) of breathing during snoring. In this study, we explore the possibility of how medical image processing can be useful and accurate method for sleep disordered breathing detection. The new approach named as Sleep Apnea Analyzing Strategy (SAAS) is firstly tested with the help of existing method namely image base Sleep Apnea Analyzer (ISAA) that use less number or data to be processed and hence running on portable device delivering results much faster than other available methods, both being cross-validated against each other. Sleep Apnea image-based Detector using Ephysiology - A new Algorithm (SAAS) We rely on sophisticated image processing techniques to extract relevant characteristics from medical images (such as x-rays, CT scans or MRIs) which are indicative of patterns typically associated with sleep apnea. The proposed methodology includes image preprocessing to improve the quality of images, feature extraction for quantification and characterization of all relevant information during OSA trials in GUI interface through well-known classification algorithms to classify patients with at least 20 de-saturations without sleep apnea. Our research aims to help in the creation of automatic diagnostic methods that will lead to early detection and treatment for sleep apnea. Our objective with this work is to provide a fair evaluation of the proposed technique in terms of sensitivity, specificity and accuracy through using a large dataset.

关键词： Accuracy Sensitivity Computational modeling Signal processing algorithms X-rays Signal processing Feature extraction Sleep apnea Biomedical image processing Medical diagnostic imaging

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Application of PyTorch3D and NERF Computer vision Tools for Building a Point Cloud of a Three-Dimensional Model and Determining the Camera Position of Still images in Space

Scientific Visualization

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Scientific visualization 2025年第1期17卷 65-85页

作者： Konkov, v.v. Zamchalov, A.B. Institute of Intelligent Cybernetic Systems National Research Nuclear University MEPhI Moscow Russia

Recently, computer graphics plays a key role in solving computer vision problems. The problem of converting 2D images into 3D models continues to be urgent, as it requires precise determination of camera position and construction of accurate 3D models of objects. Traditional methods are often limited in application and do not offer a comprehensive solution. This study examines the use of PyTorch3D and NERF libraries to determine the camera position in 3D space and create a 3D model of an object from a single 2D image. As a method of data preparation, a hardware and software system was used, including a stepper motor control device that provides manual and sequential positioning of the camera and its return to the initial position, a shooting control system to generate a comprehensive set of photos at each camera position, and a mechanism for sending data to a remote computer for further processing. The PyTorch3D library was selected during the study to explore the possibilities of converting 2D images into 3D models or determining the position of an object in the photos. The processing process included several steps: building a point cloud to generate a 3D volumetric model of the object, determining the camera position in 3D space from a single 2D image using inverse problem algorithms, and constructing a 3D object using differentiable rendering, creating 3D voxels and 3D meshes. The results of this study showed successful determination of camera position in 3D space and construction of a 3D object model from a single 2D image, demonstrating the advantages of using the PyTorch3D library over other existing models. These findings can be applied in the development of software and hardware systems for creating 3D images from 2D photographs. The study confirmed the relevance and effectiveness of using PyTorch3D library to solve the problems of converting 2D images into 3D models. Further work will be aimed at expanding the functionality of the system and its use in vari

关键词： 3D reconstruction

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An Asynchronous RISC-v-based SNN Processor with Custom ISA Extensions for Programmable On-Chip Learning

An Asynchronous RISC-V-based SNN Processor with Custom ISA E...

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International Symposium on Advanced Research in Asynchronous Circuits and systems

作者： Xuanyu Zhang Jilin Zhang Haoyang Huang Hong Chen School of Integrated Circuits Tsinghua University Beijing China

ISBN: (数字)9798331503109

ISBN: (纸本)9798331503116

Spiking Neural Network (SNN) processors have garnered increasing attention because of their high energy efficiency. Besides, on-chip learning ability allows them to adapt to various environments. However, most existing SNN processors with on-chip learning are limited by fixed learning rules, restricting their applicability across diverse tasks. In this paper, we propose an asynchronous RISC-v-based SNN processor with custom ISA extensions, which has programmable neuron models, network topology, and on-chip learning algorithms. These features enable the SNN processor to support various learning algorithms for different application domains. Furthermore, a parallel training method is proposed, which reduces training processing time by 33.9%. To verify the programmability, we use the processor to deal with three tasks: image classification, navigation, and keyword spotting. The measurement results on FPGA show that our processor achieves 96.0%, 98.0%, and 94.2% accuracy in the three tasks with three different network topologies and learning algorithms, respectively, which outperform the state-of-the-art works in both programmability and accuracy.

关键词： Training Program processors Accuracy Network topology Navigation Neurons Spiking neural networks System-on-chip Classification algorithms Asynchronous circuits

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ML Algorithm-Based Detection of Leaf Diseases 4

ML Algorithm-Based Detection of Leaf Diseases

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4th International Conference on Smart systems and Inventive Technology, ICSSIT 2022

作者： Kathiravan, M. Priya, K. Hari Sreesubha, S. Irumporai, A. Kumar, v. Sukesh Reddy, vishnu vardhan Hindustan Institute of Technology and Science Deemed to be University Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9781665401180

In day-to-day life, while the plant is important for every human being to get rich vitamins of vegetables, the plant disease rate is increasing proportionally. The disease on the plant causes severe suffering to farmers economically. So, the plant disease is either to be controlled or predicted before it leads to big damages. To solve this issue, machine learning algorithms have been proposed to analyze the root causes of the disease and to prescribe suitable pesticides to cure the plant. Though many methods have been encountered to detect the disease of a crop, the existing methods failed to give accurate results. While many existing methods failed to apply image processing techniques and databases to store the crop data, the proposed CNN algorithm adopted these features by creating web applications to know the different types of disease that have occurred on the plant. The result witnesses that the proposed algorithms have shown improved accuracy (93.3%) which helps the farmer to raise the productivity of a crop. The developed app also helps young farmers to read and select suitable image options to improve the good yielding of their agriculture. © 2022 IEEE

关键词： Pattern recognition

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Deep Learning video Analytics Solutions for Ocean Surveillance systems 14

Deep Learning Video Analytics Solutions for Ocean Surveillan...

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14th International Conference on Digital image processing, ICDIP 2022

作者： Harikrishnan, v.S. Dixit, Shivam Pal Amutha, K. Kamnani, Jitesh Ramasundaram, S. venkatesan, R. Centre for Development of Advanced Computing Chennai India National Institute of Ocean Technology Chennai India

ISBN: (纸本)9781510657564

Moored data buoys are floating platforms at sea. These buoys serve as in-situ Weather, Ocean and Tsunami observatories. These buoys transmit real-time data through 3G/GSM/GPRS and satellite telemetry. Damage to the buoy systems by humans, boats, ships etc., intentional or otherwise, causes loss of data, and inhibits early warning systems. It also has financial implications due to the loss of the instruments, repair & reinstallation charges, and the time a ship spends to fix the buoy. Challenges arise while analyzing the video footage as they are unstable and shaky, due to the continuous movements of floating ocean buoy platforms caused by the state of the sea. This paper explores object detection algorithms for detecting eight different objects commonly found in the camera video footage transmitted by the buoy platforms at sea. The object detection training implementation gave us a best accuracy of 0.867MAP@0.5IOU. The object detection will help in solutions like Object Search, detection of floating marine plastic debris, understanding the direction of motion of ships, boats etc. In a broader perspective, it can help in Surveillance, Market Survey and Fish Detection in underwater cameras for fish abundance study. © 2022 SPIE.

关键词： Ships

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Ant Colony Optimization based Medical image Preservation and Segmentation 2

Ant Colony Optimization based Medical Image Preservation and...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Nandihal, Priya Pareek, Piyush Kumar De Albuquerque, victor Hugo Costa Madhumala, R.B. Khanna, Ashish Sunil Kumar, v. Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Design Bangalore India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceara Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9781665493963

image segmentation is an essential component in many different types of computer vision systems. image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work. © 2022 IEEE.

关键词： Impulse noise

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