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检索条件"任意字段=Image Processing: Algorithms and Systems V"

共 2735 条记录，以下是461-470 订阅

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Improving the Detection of Kidney Tumours Using Firefly Algorithm Optimisation

Improving the Detection of Kidney Tumours Using Firefly Algo...

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Intelligent Cyber Physical systems and Internet of Things (ICoICI), International Conference on

作者： Rajagopal K v. Sheeja Kumari Saraswathy S S. Ponmaniraj v. Suresh Kumar A. Deepa Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Sciences SIMATS University Thandalam Chennai Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai Department of Computational Intelligence Institute of ECE Saveetha Institute of Medical and Technical Sciences SIMATS University Thandalam Chennai Department of Computer Science and Engineering Saveetha Engineering College Thandalam Chennai

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Accurate and timely diagnosis of kidney tumors is crucial for effective treatment. This study proposes a novel approach utilizing deep learning techniques and the firefly algorithm (FA) to enhance kidney tumor segmentation. By optimizing feature selection and CNN hyperparameters, FA improves the accuracy and efficiency of the detection system. The proposed method involves pre-processing medical images, extracting features using CNNs, and fine-tuning the model with FA. Experiments on a public dataset demonstrate significant improvements in classification metrics compared to traditional methods. The FA-optimized model provides clinicians with a valuable tool for accurate kidney tumor detection and classification, leading to improved patient outcomes.

关键词： Deep learning image segmentation Accuracy Feature extraction Classification algorithms Convolutional neural networks Kidney Optimization Medical diagnostic imaging Tumors

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An Intelligent Lane, Object Detection and Speed Estimation for Ego-vehicles using Perception algorithms

An Intelligent Lane, Object Detection and Speed Estimation f...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Kukkamudi Sunidhi J. N.v.R. Swarup Kumar Gorla Srinivas Cetty Yamini Dept. of CSE GST GITAM Deemed to be University India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Autonomous vehicles promise to revolutionize transportation by expanding mobility and improving traffic safety. All of this future auto tech hinges on autonomous vehicle perception - the processing of visual input to vital Advanced Driving Assistance functions such as lane detection, vehicle and pedestrian recognition, and traffic sign detection. The goal of this work is to increase the level of autonomy in ego vehicles, which can drive through challenging traffic situations (completely autonomously). This research will then take on one of the portions with a low latency and high accuracy target, which is safety + efficiency (using ADAS – Advanced Driver Assistance systems). The system proposes an ego vehicle-based computer vision solution for pedestrian, vehicle, and traffic sign detection along with lane detection. The entire system consists of many small systems like the YOLO-NAS Module, Gaussian Blur Module, Hough line transform, and Canny Edge Detection. A detailed flow diagram is given at the end and is curated during the experiment analysis meeting.

关键词： YOLO Pedestrians Accuracy Lane detection image edge detection Estimation Transforms Real-time systems Safety Autonomous vehicles

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AI-Powered Monitoring for Mitigating Human-Animal Conflicts in Agricultural and Forest Zones

AI-Powered Monitoring for Mitigating Human-Animal Conflicts ...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Murugadoss Department of Artificial Intelligence and Data Science V.S.B. College of Engineering Technical Campus Coimbatore Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Human-animal conflicts pose a significant challenge in forest zones and agricultural fields, leading to resource losses and threats to endangered wildlife. These conflicts have escalated in recent years, necessitating innovative solutions for continuous monitoring and intervention. This study presents a novel approach for mitigating such conflicts by leveraging image processing and Artificial Intelligence (AI). Motion detection techniques are employed to identify activity, and content-based image classification algorithms analyze the captured visuals. The proposed method integrates advanced feature extractors, data augmentation, and AI to develop a robust detection network. Additionally, the system enhances safety analysis and certification for high-speed trains by identifying objects and animals in real time. Using the COCO dataset for training and validation, the study demonstrates the potential of AI to streamline conventional safety measures and ensure the coexistence of humans and animals in ecologically sensitive areas.

关键词： Animals Wildlife Forestry Feature extraction Data augmentation Real-time systems Safety Object recognition Artificial intelligence Monitoring

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Dronexscan: Detection and Identification of vehicles in Aerial images Captured by Unmanned Aerial vehicles(UAvs)

Dronexscan: Detection and Identification of Vehicles in Aeri...

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International Conference on Communication and Electronics systems (ICCES)

作者： J.v. Anchitaalagammai S. Kavitha S. Murali Rlk. Sarvesh S. Seshanand A. Javanesh Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Computer Science and Engineering Velammal College of Engineering and Technology. (of Affiliation) Madurai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research study proposes Drone-TOOD, the low-power consumption task-aligned object detection algorithm for vehicle detection that was designed from the images captured by UAvs. In the aim of bringing low-power, real-time use into this methodfrom the ESP32-CAM image-processing module, it brings the principles of TOOD into it to balance precision and velocity while detecting vehicles through aerial views and challenges such as variation in altitude, lighting conditions, and orientations of the vehicles. In this regard, the low computational capabilities of ESP32-CAM are supplemented by optimized deep learning methods offering high performance in effective detection with minimal power consumption. Drone-TOOD has performed outstandingly well within the domain of UA v-based vehicle detection and hence asserts suitability for surveillance, traffic monitoring, and search-and-rescue applications.

关键词： Technological innovation Machine learning algorithms Surveillance vehicle detection Traffic control Autonomous aerial vehicles Robustness Real-time systems Sensors Object recognition

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Bacterial Blight and Cotton Leaf Curl virus Detection Using Inception v4 Based CNN Model for Cotton Crops

Bacterial Blight and Cotton Leaf Curl Virus Detection Using ...

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International image processing, Applications and systems Conference (IPAS)

作者： Sohail Anwar Abdul Rahim Kolachi Shadi Khan Baloch Shoaib R. Soomro Dept. of ElectronicEngineering Mehran University of Engineering & Technology Jamshoro Pakistan Dept. of Mechatronics Engg. Mehran University of Engineering & Technology Jamshoro Pakistan

ISBN: (纸本)9781665462204

Agriculture sector is an important pillar of the global economy. The cotton crop is considered one of the prominent agricultural resources. It is widely cultivated in India, China, Pakistan, USA, Brazil, and other countries of the world. The worldwide cotton crop production is severely affected by numerous diseases such as cotton leaf curl virus (CLCv/CLCuv), bacterial blight, and ball rot. image processing techniques together with machine learning algorithms are successfully employed in numerous fields and have also used for crop disease detection. In this study, we present a deep learning-based method for classifying diseases of the cotton crop, including bacterial blight and cotton leaf curl virus (CLCv). The dataset of cotton leaves showing disease symptoms is collected from various locations in Sindh, Pakistan. We employ the Inception v4 architecture as a convolutional neural network to identify diseased plant leaves in particular bacterial blight and CLCv. The accuracy of the designed model is 98.26% which shows prominent improvement compared to the existing models and systems.

关键词： Training Learning systems Machine learning algorithms image processing Crops Production Cotton

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Real-Time image Detection With Braille Output

Real-Time Image Detection With Braille Output

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Archana Nazarkar Maheshuni Saarthak Mosha Pranav Naidu Terli Jeshwanth Dept. of Biomedical Engineering B V Raju Institute of Technology Narsapur India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

This project focuses on developing an innovative assistive device designed to enhance navigation for deaf-blind individuals across various environments. The device utilizes an integrated camera module, such as those found in laptops or phones, for real-time object recognition. A compact Braille display, driven by solenoids, provides immediate tactile feedback upon recognizing common objects like bottles, persons, and cell phones through advanced image processing algorithms. A dedicated mobile application facilitates object recognition, text extraction, and translation into tactile Braille output using custom code. This tactile feedback empowers users with enhanced spatial awareness and navigation capabilities, promoting greater independence and safety in everyday surroundings. By integrating cutting-edge technology with inclusive design principles, the project aims to meet the unique accessibility needs of the deaf-blind community effectively.

关键词： Braille Translation Portable computers Navigation Tactile sensors Object detection Real-time systems Safety Object recognition Solenoids

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Digital Transformation of the Flotation Monitoring Towards an Online Analyzer 4th

Digital Transformation of the Flotation Monitoring Towards...

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4th International Conference on Smart Applications and Data Analysis, SADASC 2022

作者： Bendaouia, Ahmed Abdelwahed, El Hassan Qassimi, Sara Boussetta, Abdelmalek Benhayoun, Abderrahmane Benzakour, Intissar Amar, Oumkeltoum Zennayi, Yahia Bourzeix, François Baïna, Karim Baïna, Salah Khalil, Abdessamad Cherkaoui, Mouhamed Hasidi, Oussama FSSM Cadi Ayyad University Marrakech Morocco Moroccan Foundation for Advanced Science Innovation and Research MAScIR Rabat Morocco FST Cadi Ayyad University Marrakech Morocco Reminex Research and Engineering Center Managem Marrakech Morocco Mohammed V University Rabat Morocco Rabat Morocco

ISBN: (纸本)9783031204890

Accurate and timely investigation to concentrate grade in mining industry is a premise of realizing real time and efficient control in a froth flotation process. This study seeks to use image processing and artificial intelligence technologies to predict the elemental composition of minerals in the flotation froth. The online analyzer is a flotation soft sensor solution that predicts the concentrate grade content of the flotation using froth images and physio-chemical parameters. A froth image dataset from the lead flotation circuit was collected and prepossessed. Frame selection and data augmentation was used for this dataset. Feature extraction includes texture and color distribution using image processing algorithms. Then, several state-of-the-art machine learning algorithms (Linear regression, Random forest, Decision tree, GR Boost) are trained to predict the concentrate grades of minerals. A Convolutional neural network architecture is used on the image dataset to predict the Lead Pb concentrate grade which indicates that the deep learning has a good industrial performance. The promising results of this study demonstrate the significant potential of machine vision and deep learning neural networks in froth image analysis, which is of great importance for development of the mining industry. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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An Indispensable Contemplation on Natural Language processing Using Ensemble Techniques for Text Classification 8

An Indispensable Contemplation on Natural Language Processin...

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8th International Conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Chahar, Ankit Patil, Ninad Walunj, Darshan Sai Rohith, T. Shah, Rajat Saratkar, Himanshu R.V. College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India College of Engineering Pune Department of Computer Science and Engineering Maharashtra Pune India P.E.S Modern College of Engineering Department of Information Technology Maharashtra Pune India Department of Computer Science and Engineering Karnataka Manipal India Kj Somaiya College of Engineering Department of Mechanical Engineering Maharashtra Mumbai India Yeshwantrao Chavan College of Engineering Department of Computer Technology Maharashtra Nagpur India

ISBN: (纸本)9781665408165

NLP is a computer-based software technology that used primarily to translate the natural human language into machine understandable format efficiently. The inputs for the machine could be text, audio, video, image, and speech. NLP is emerging globally and is being used in many applications. One popular application of NLP is Siri introduced in Apple Inc. This created a greater impact on iPhone community. Machines being trained with respect to voice search will facilitate the future generation in performing day-to-day activities. Machine learning (ML) algorithms have contributed extensively for building this popular framework. The paper aims to provide an NLP solution to a text classification problem and solve it using NLP and machine learning to get the best results and the best accuracy. © 2022 IEEE.

关键词： Regression analysis

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Road Radar: Integrated Road Monitoring and Maintenance System

Road Radar: Integrated Road Monitoring and Maintenance Syste...

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Emerging Technologies in Computer Science for Interdisciplinary Applications (ICETCS), International Conference on

作者： B Santosh Kumar Meenakshi Sundaram Malvika Golekar Prerna Ubana v Pruthvi Reddy Computer Science and Engineering New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

Potholes pose a significant threat to road safety in India, contributing to serious traffic accidents. To address this issue, a camera-based system mounted in cars is proposed to detect any road irregularities in real time. The system utilizes You Only Look Once (YOLO) object detection algorithm to identify road irregularities, followed by image processing algorithms to refine detections and improve accuracy. Additionally, Convolutional Neural Networks (CNN) are employed to classify the severity of detected potholes. Real-time notifications are displayed on the dashboard to alert the driver when an obstacle is detected, and a buzzer inside the car sounds when the vehicle approaches an obstruction, in this case, ten meters, prompting the driver to slow down while simultaneously notifying the maintenance team about the pothole’s location. A graph will be generated which will include location of the pothole versus area of the pothole. The biggest pothole will be detected, then the maintenance team will try to make this location their priority and will attempt to fix that first in order to prevent major accidents. At the same time, the location that gets the highest number of notifications will be viewed and made a priority. The primary objective is to enhance road safety by alerting drivers to hazards like potholes in real-time, especially prevalent in India. Through YOLO, image processing, and CNN integration, the system aims to decrease accidents, promote cautious driving, and possibly reroute to safer paths, mitigating pothole-related accidents and enhancing overall road safety nationwide.

关键词： YOLO visualization image processing Market research Road safety Real-time systems Maintenance

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IPARS: An image-based Personalized Advertisement Recommendation System on Social Networks

IPARS: An Image-based Personalized Advertisement Recommendat...

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13th International Conference on Ambient systems, Networks and Technologies, ANT 2022 / 5th International Conference on Emerging Data and Industry 4.0, EDI40 2022

作者： Jouyandeh, Farzaneh Zadeh, Pooya Moradian School of Computer Science University of Windsor Canada

Social media has become a primary source of information for decision-makers, organizations, and scientists in today's fast-paced world. Indeed, because of the large volume of user-generated data available on social media, these online platforms are viewed as computable data sources that potentially mirror reality. It could be an authentic environment for the task of target customers identification for marketing. This paper presents a novel image-based personalized advertisement recommendation system named IPARS to identify target customers in social media using image processing and machine learning techniques for an online advertisement. Assume having a set of advertising images;The problem is identifying a group of social media users who are likely to be the potential target of those images. In IPARS, a given social network is first converted into a weighted bipartite graph where the nodes are the users and keywords. Then, another bipartite graph is formed by decomposing the advertising images into their objects, labels, concepts, and sentiments. We propose a couple of formulas to calculate the weight of edges for both graphs. An algorithm is proposed to search the social graph and identify and rank the best group of users. We have evaluated our proposed model on a set of images and users from the Flickr dataset and Twitter. The results showed that IPARS has a better performance compared with other algorithms. © 2022 Elsevier B.v.. All rights reserved.

关键词： Advertisement Object Identification Recommendation System Social Network Analysis

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