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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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USING machine learning ON DEPTH MAPS AND imageS FOR TUNNEL EQUIPMENT SURVEYING

USING MACHINE LEARNING ON DEPTH MAPS AND IMAGES FOR TUNNEL E...

引用

conference on Optical 3D Metrology (O3DM)

作者： Barcet, Florian Tual, Maxime Foucher, Philippe Charbonnier, Pierre Cerema Project Team ENDSUM Strasbourg France

In this contribution, we illustrate how to use high-resolution 3D data and images to model the global shape of a tunnel and to survey its equipment, in a semi-automatic way using pattern recognition and machine learning techniques. We first implement a robust Bspline fitting algorithm, based on a parametric family of M-estimators that allows an efficient deterministic optimization strategy, to accurately model the tunnel lining using range data, despite the presence of acquisition artifacts and significant perturbations related to the equipment and some surface defects. The residual maps from the robust fit can be exploited to segment the equipment in an unsupervised manner using clustering algorithms, but at the cost of post-processing which makes the method rather ineffective for routine use. However, we deploy it to annotate data for the supervised learning of a deep learning model, namely Mask R-CNN. We comment on the first results, obtained on a still limited number of examples, and from image data only, and discuss the possibilities of improving the method, in the immediate or longer term.

关键词： Tunnel surveying Laser Scanning Mobile acquisition Documentation Building Information Modeling

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Software to Assist Visually Impaired People During the Craps Game Using machine learning on Python Platform 2nd

Software to Assist Visually Impaired People During the Craps...

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2nd international conference on Smart Technologies, Systems and Applications (SmartTech-IC)

作者： Hernandez Diaz, Nicolas Penaloza, Yersica C. Yuliana Rios, Y. Magre Colorado, Luz A. Univ Tecnol Bolivar Parque Ind & Tecnol Carlos Velez Pombo Cartagena De Indias Colombia Univ Pamplona Km 1 Via Bucaramanga Pamplona Colombia

ISBN: (纸本)9783030991708;9783030991692

pattern recognition is a prominent area of research in computer vision, where different methods have been proposed in the last 50 years. This work presents the development of a Python API to identify the result of two six-sided dice used in the game called "Craps" as a no-controlled environment to help visually impaired people. The software is structured in four stages. The first one is capturing images through a device with a digital camera connected to the web via IP address. The second stage corresponds to the captured image processing;it is necessary to establish a standard image size and resize and equalize the digitized image. The third stage seeks to segment the object of study by artificial vision techniques to identify the result of the dice after being thrown. Finally, the fourth stage is to interpret the result and play it through a speaker. The expected possible result is a system that integrates the four stages mentioned above through an intuitive and accessible low-cost Python API, mainly aimed at visually impaired people.

关键词： Craps game Visually impaired people Non controlled environment Python API Artificial vision techniques image processing

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1st international Scientific and Practical conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2021

1st International Scientific and Practical Conference on Inf...

引用

1st international Scientific and Practical conference on Information Technologies and Intelligent Decision Making Systems, ITIDMS 2021

ISBN: (纸本)9783031213397

The proceedings contain 13 papers. The special focus in this conference is on Information Technologies and Intelligent Decision Making Systems. The topics include: Tone image processing with Discrete Data structure;the Concept of Client-Server Architecture for Remote Control of a Distributed Knowledge Base;experimental study of Russian-Language News Text Categorization Performance Using machine learning Algorithms;experimental Evaluation of the Efficiency of Compression of Files by Fractal-Spectral Codec;an Intelligent Decision Support System for Solving Complex Territorial Planning Problems;fault Tolerance of Distributed Worker Processes in Corporate Information Systems and Technologies;functioning of the Thermal Memory Cell;using Data Mining Technology in Monitoring and Modeling the Epidemiological Situation of the Human Immunodeficiency Virus in Kazakhstan;information Technology and Artificial Intelligence in Improving of Center Pivot Irrigation Control;KOA Pathology Screening Using Multi-channel VAG Signal Fusion Method;prioritization of Distributed Worker Processes Based on Etcd Locks.

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Unlocking The Power Of Deep learning: A Guide To Automatic License Number recognition

Unlocking The Power Of Deep Learning: A Guide To Automatic L...

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Optimization Techniques for learning (ICOTL), international conference on

作者： Hemalatha D Almas Begum S Alex David Gaddam Rishitha Pothanaboyina Dimple Praharsha Chundi Nandini Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunathala R & D Institute of Science and Technology Chennai Tamil Nadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamilnadu India

An image processing technique called Automatic Number Plate recognition (ANPR) utilizes the vehicle’s number plate to identify it. The goal is to create a successful automatic authorized car identification system that makes use of the license plate. In the majority of applications involving the movement of vehicles, the detection and recognition of a vehicle’s license plate (LP) is a crucial method. Additionally, it is a hot research subject in the area of image processing. To find and identify LPs, numerous techniques, methods, and algorithms have been created. Prior to taking a picture of the car, the developed system first recognizes it. The region of an image that contains the car number plate is retrieved using image segmentation. An optical character recognition technique is used to recognize characters. However, due to the LP’s characteristics which include the numbering system, colors, character languages, fonts, and size varying from one nation to the next. To make the detection and recognition procedure very effective, more research in this area is still required. Despite the fact that many scholars have studied this field, many current systems function in defined and regulated environments. For instance, some frameworks need sophisticated hardware in order to produce high-quality images or record video from cars moving slowly. Because of this, it is still challenging to successfully discover and identify LPs under various environmental circumstances and climatic variations. This article presents a deep learning-based automatic system for LP detection and recognition that is divided into three sections: character recognition, segmentation and detection.

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Emotion Detection and Quotations using CNN 1

Emotion Detection and Quotations using CNN

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1st international conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Bikku, Thulasi Viswanadha, Lavanya Madhulatha, Maddoju Jyothirgamai, Penumuchu Shabana, Shaik Sreedevi, Saka Sai Vignan's Nirula Institute of Technology and Science for Women Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9781665497107

Emotions frequently aid in the facilitation of human connections. These emotions can be detected in various ways, which include body language, speech, and facial expressions. Many strategies have been used to determine a person's emotion over the years as Artificial Intelligence and machine learning have advanced. This paper represents a system that recognizes a person's emotions by looking at their face and adds some quotations to it. A neural network-based method along with image processing is utilized to classify the universal emotions: disgust, scared (fear), sad, surprise, happy and anger. In the proposed model, the 'dropout' mechanism which is a regularized method to reduce overfitting and the extended Cohn kanade (CK+) dataset have been used. To improve training efficiency and classification performance, pre-processing and data augmentation approaches are performed. The best structure to capture facial expressions can be obtained from the existing CNN structure by altering convolution layer's feature map count and making changes corresponding to the fully-connected layer's nodes. The traditional models were not able to achieve more than 70% accuracy. However, this proposed model achieves 85.7% accuracy with relevant quotations which will be helpful to uplift your mood when you feel low. © 2022 IEEE.

关键词： machine learning

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Deep Autonomous Agents Comparison for Self-driving Cars 1

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7th international conference on machine learning, Optimization, and Data Science (LOD) / 1st Symposium on Artificial Intelligence and Neuroscience (ACAIN)

作者： Riboni, Alessandro Candelieri, Antonio Borrotti, Matteo Univ Milano Bicocca Milan Italy Inst Appl Math & Informat Technol Milan Italy

ISBN: (数字)9783030954673

ISBN: (纸本)9783030954673;9783030954666

Autonomous driving is one of the most challenging problems of the last decades. The development in recent years is mainly due to the continuous expansion of Artificial Intelligence. Nowadays, most self-driving systems use Deep learning techniques. In recent years, however, thanks to the successful learning demonstrations of Atari games and AlphaGo by Google DeepMind, new frameworks based on Deep Reinforcement learning are being developed. The objective is to combine the advantages of image processing and feature extraction of convolutional networks, and the learning process through the interaction of one or multiple agents with their environment. This work aims to deepen and explore these new methodologies applied to autonomous driving cars. In particular, we developed a framework for controlling a car in a simulated environment. The agent learns to drive within a neighborhood with constant speed, variable light conditions, and avoiding collisions with external objects. The proposed techniques are based on Double Deep Q-learning and Dueling Double Deep Q-learning. We implemented two variants of the algorithms: one trained from random weights and one exploiting the concepts of Transfer learning. After a simulation study, the Dueling Double Deep Q-learning with Transfer learning has showed promising performance.

关键词： Autonomous driving Deep Q-learning Transfer learning

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A survey on deep learning: Convolution neural network (cnn) 1st

A survey on deep learning: Convolution neural network (cnn)

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1st international conference on Intelligent and Cloud Computing, ICICC 2019

作者： Sahu, Madhusmita Dash, Rasmita BhubaneswarOdisha751030 India BhubaneswarOdisha751030 India

ISBN: (纸本)9789811562013

Deep learning is a subfield of machine learning and plays a vital role in the area of image processing, natural language processing, computer vision, etc. As compared to traditional machine learning methods, it has a strong ability of self-learning and self-debugging. Convolution neural network (CNN) is the most widely used technique of deep learning for better feature extraction from large datasets. Many researchers adopted CNN for object classification, face recognition, automatic handwritten, etc. In this paper, the detailed concepts behind CNN are discussed with their broad applications. © Springer Nature Singapore Pte Ltd 2021.

关键词： learning algorithms

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Plant Disease detection using machine learning and CNN on leaf images

Plant Disease detection using Machine learning and CNN on le...

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Advanced Computing and Emerging Technologies (ACET), international conference on

作者： Rashmi Bhargava Santosh Kumar Upadhyay Rakshita Sharma Nittyansh Srivastava Harsh Mavi Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

This paper proposes a system for detection of the diseases in plants using machine learning and image processing technique to analyze the images of leaves and fruits. Leveraging convolutional neural networks (CNNs) and various machine learning approaches, our system aims to accurately detect and classify plant diseases for improved agricultural management. We draw inspiration from previous studies, incorporating methodologies such as data augmentation, feature extraction, and outlier identification to enhance detection accuracy and efficiency. The proposed system is evaluated using diverse datasets, encompassing a range of plant species and disease types, to ensure robustness and generalization. Through experimentation, we demonstrate the efficiency of CNNs and algorithms of machine learning in detecting diseases and classifying plant species, highlighting their potential for real-world applications in agriculture. Additionally, we focused on the importance of selection of dataset, evaluation metrics, and optimization of model for achieving reliable disease detection results.

关键词： Measurement Plant diseases machine learning algorithms Plants (biology) machine learning Feature extraction Data augmentation Robustness Convolutional neural networks Optimization

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A Deep learning Approach for Non - invasive Body Mass Index Calculation 1st

A Deep Learning Approach for Non - invasive Body Mass Index ...

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Nandhan, S. Harish Zean, J. Remoon Mahi, A.R. Meena, R. Mahalakshmi, S. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9783031730641

A person’s body mass index (BMI) is a vital indicator of their health. Through extensive research, it was determined that traditional methods of BMI calculation can be time-consuming. We developed a technique that uses deep learning to predict a person’s BMI, age, and gender from facial images. Our system uses Multi-Task Cascaded Convolutional Neural Networks (MTCNN) to detect faces by cropping the face out of an input image and detecting facial landmarks based on the 5-point facial landmark detection algorithm from the input image. The cutting-edge pre-trained models will be finetuned, including VGG-Faces, ResNet (Residual Neural Network), and VGG16 (Visual Geometric Groups with 16 layers) on a public dataset of 1530 prisoners from Polk County Prison. This dataset contains a multifarious range of faces, including different races, ages, and genders. The uploaded CSV file contains the heights, weights, and gender of the training images. After the image is passed into the network, it generates BMI, age, and gender predictions for the input image. This system uses an efficient face recognition mechanism to identify the age, gender, and BMI of a person using a multi-task BMI prediction model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Detecting Turmeric Taphrina Maculans Disease using machine learning Algorithms 1

Detecting Turmeric Taphrina Maculans Disease using Machine L...

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1st international conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Vanitha, C.N. Malathy, S. Shenbagavalli, P. Krishna, S.A. Kavin, K. Kongu Engineering College Department of CSE India Karunya Institute of Technology and Science Department of Digital Sciences Coimbatore India Kongu Engineering College Student Department of Mechatronics India Kongu Engineering College Department of CT-UG India

ISBN: (纸本)9781665497107

Disease management in agriculture is a major task in agriculture for farmers. Turmeric is a major cultivated crop in the Erode region of south India. In turmeric cultivation, farmers find major diseases like leaf spot (Colletotrichum capsici) and (Anthracnose) etc. It is a label used to define a group of fungal infections in plants which can spread vastly, and the plants will be destroyed. For most farmers, in a cultivation of more than 20 acres, it is very challenging to identify the disease-affected areas. To overcome this problem, the sample images are collected and dataset of the turmeric leaf is prepared. It is an image processing technique. The conversion of image is done by analyzing the image. Plant illness ID is the main area in farming. The ID of plant sicknesses require close observing, and subsequently this paper embraces advances to oversee turmeric plant infections brought about by organisms to empower creation of top-notch crop yields. Different picture handling and ML methods are utilized to recognize and arrange the illnesses in turmeric leaf. The leaf pictures of various classes are pre-handled and sectioned to advance productive component extraction. ML calculations like k-implies calculation are utilized. © 2022 IEEE.

关键词： machine learning

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