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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1643 条记录，以下是31-40 订阅

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AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool for Non-Invasive Medical Diagnostics

AI-Driven Thermal Imaging: A Comprehensive Web-Based Tool fo...

引用

international Symposium on Advanced Computing and Communication (ISACC)

作者： Aniruddha Ghosh Sayan Snigdha Pal Sumedha Das Spandan Bhattacharya Arijit Bhattacharya Nilanjana De Sudipta Sahana Computer Science and Engineering(Artificial Intelligence and Machine Learning) Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata West Bengal India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Thermography is an innovative technique that creates a heatmap of different portions of the human body by using infrared (IR) radiation where color scales vary for various temperature regions. This work's scope aims to extend thermal imaging applications with machine learning algorithms in conjunction with Azure API services to analyze thermal images and return a preliminary diagnosis. The project consists of generating heatmaps for better visualization results, analyzing them through image processing techniques, and feeding the information to Azure’s Custom vision API to detect anomalies and classify health conditions and finally producing a proper report. This research allows individuals and healthcare professionals to have a quick, non-invasive diagnostic tool that empowers health monitoring and preventive care. It combines the capabilities of contemporary technologies with uses directed toward health practice, leading to inimical patient outcomes and true practices.

关键词： Visualization Machine learning algorithms image color analysis Infrared heating Infrared imaging Thermal analysis Medical diagnosis Predictive analytics Intelligent systems Monitoring

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Advanced Motion Recognition Using High-Resolution Infrared Imaging and Enhanced image processing Techniques

Advanced Motion Recognition Using High-Resolution Infrared I...

引用

Disruptive Technologies (ICDT), international conference on

作者： Prafull Mishra Rishabh Srivastava Prerna Singh Vineet Shrivastava Devesh Garg Department of Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Over the past few years, infrared (IR) motion detection has become more important in domains such as security, wildlife monitoring, and nighttime surveillance, when standard vision-based systems frequently fail. The approach of infrared video detection using convolutional neural networks is been implemented in this research work. Infrared imaging is particularly useful for collecting temperature-based data in low-visibility or dark conditions, enabling for the monitoring of activities that would otherwise be difficult to trace. The suggested approach preprocesses grayscale infrared pictures, normalizing them and scaling to fit the input requirements of a custom-built CNN. Through a sequence of convolutional and pooling layers, the CNN collects characteristics related to motion patterns in IR pictures, learning to discriminate between frames having motion and those that do not. The proposed system was developed with the help of pre-trained data values of infrared pictures, and performance was tested using multiple parameters that includes various visualization figures such as accuracy-precision graph, recall, and ROC-AUC, to show its usefulness. This real-time approach combines a CNN with a video processing pipeline to identify motion in an infrared movie. It uses visualizations like as confusion matrices, accuracy-loss curves, and precision-recall curves to give insights into model design, implementation details, and experimental results. The results reveal that CNNs can accurately discern motion in infrared pictures, paving the way for automated low-light surveillance.

关键词： Surveillance Wildlife Data visualization Infrared imaging Streaming media Real-time systems Motion detection Convolutional neural networks image preprocessing Standards

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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...

引用

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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Squared Fault and Biased Entropy for Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image Synthesis Using Firefly Algorithm 3rd

Squared Fault and Biased Entropy for Magnetic Resonance Imag...

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3rd international conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Abidi, Nazia Abbas Khalaf, Hameed Hassan Almulla, Ausama A. Hussein, Mustafa Asaad Jawad, Israa Abed Joint Step Consultants Pvt. Limited Jharkhand Ranchi834010 India Department of Medical Laboratories Technology Al-Manara College for Medical Sciences Maysan Iraq Department of Medical Laboratories Technology Al-Hadi University College Baghdad10011 Iraq College of Nursing National University of Science and Technology Dhi Qar Iraq Department of Medical Laboratories Technology AL-Nisour University College Baghdad Iraq

ISBN: (纸本)9783031691140

Multi-modal In recent years, medical image fusion has emerged as an all-encompassing analytic strategy that frequently makes use of a variety of various alteration methods. This trend began in the field of radiology. By integrating a large number of images obtained using a single or many imaging modalities, the purpose of multi-modal medical image fusion is to improve imaging quality while preserving certain properties of the images. image processing, computer vision, pattern recognition, machine learning, and artificial intelligence are just a few of the many cutting-edge topics that can be categorised as part of the broader category of medical image fusion. In order to better understand the lesion, medical professionals have been making substantial use of a technique that combines a number of different medical imaging modalities. In this article, we present a framework for fusion that is based on a number of learning models that are known together as the firefly algorithm. The objective of this framework is to get rid of the squared error and optimise through weighted entropy. The performance of the suggested workbench is simulated in MATLAB and compared against approaches that are considered to be state-of-the-art in order to determine how effective it is. The peak signal-to-noise ratio, mean square error, correlation, and precision are some of the most important measures. The performance of the suggested workbench is superior according to all criteria, and it also decreases the amount of computational load. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computerized tomography

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A Robust Watermarking Scheme for Copyright Protection 3rd

A Robust Watermarking Scheme for Copyright Protection

引用

3rd international conference on computer vision and image processing (cvip)

作者： Singh, Satendra Pal Bhatnagar, Gaurav Indian Inst Technol Karwar Dept Math Karwar India

ISBN: (纸本)9789813292918;9789813292901

In this paper, a robust image watermarking scheme based on all phase sine biorthogonal transform (APSBT), singular value decomposition (SVD) and dynamic stochastic resonance (DSR) is presented. Firstly, the cover image is transformed by APSBT and then a gray-scale logo is embedded through the singular value decomposition. After the authentication process which essentially resolves the false-positive extraction of SVD in watermarking, a phenomenon based on dynamic stochastic resonance is deployed for the logo extraction from the watermarked image. The simulation results demonstrate that the proposed scheme has better performance in the aspect of robustness and invisibility.

关键词： image watermarking Singular value decomposition Dynamic stochastic resonance (DSR) All phase sine biorthogonal transform (APSBT)

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Detection of Down Syndrome Using Deep Facial Recognition 3rd

Detection of Down Syndrome Using Deep Facial Recognition

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3rd international conference on computer vision and image processing (cvip)

作者： Mittal, Ankush Gaur, Hardik Mishra, Manish Graph Era Deemed Univ Dehra Dun Uttarakhand India

ISBN: (纸本)9789813290884;9789813290877

Down syndrome is a genetic disorder that affects 1 in every 1000 babies born worldwide. The cases of Down syndrome have increased in the past decade. It has been observed that humans with Down syndrome generally tend to have distinct facial features. This paper proposes a model to identify people suffering from Down syndrome based on their facial features. Deep representation from different parts of the face is extracted and combined with the aid of Deep Convolutional Neural Networks. The combined representations are then classified using a Random Forest-based pipeline. The model was tested on a dataset of over 800 individuals suffering from Down syndrome and was able to achieve a recognition rate of 98.47%.

关键词： KNN CNN HOG Machine learning computer vision Random forest Healthcare Down syndrome

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Robust Detection of Defective Parts Using Pattern Matching for Online Machine vision System 3rd

Robust Detection of Defective Parts Using Pattern Matching f...

引用

3rd international conference on computer vision and image processing (cvip)

作者： Singh, Namita Jaju, Abhishek Sharma, Sanjeev Bhabha Atom Res Ctr Div Remote Handling & Robot Mumbai 400085 Maharashtra India

ISBN: (纸本)9789813290884;9789813290877

Reliable detection of defective parts is an essential step for ensuring high-quality assurance standards. This requirement is of primary importance for online vision-based automated pellet stacking system. During nuclear fuel pin manufacturing, the image of a single row (consecutively placed components with no gap) is processed and analyzed to extract meaningful edges. Generally, these edges follow a regular pattern;however, the presence of surface cracks and chips can alter this pattern. In this paper, we formalize the detection of defective parts as a pattern matching problem. Three different patterns are proposed and evaluated for sensitivity, specificity, and accuracy. An experiment performed with the proposed pattern matching techniques show that multi-pattern matching is the most effective method for identifying defective parts.

关键词： Multi-pattern matching Aho-Corasick algorithm Finite state automata Fuel pellet Stack Defects

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image Embedding for Detecting Irregularity 3rd

Image Embedding for Detecting Irregularity

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3rd international conference on computer vision and image processing (cvip)

作者： Sharma, M. K. Sheet, D. Biswas, Prabir Kumar IIT Kharagpur Adv Technol Dev Ctr Kharagpur W Bengal India IIT Kharagpur Elect Engn Kharagpur W Bengal India IIT Kharagpur Elect & Elect Commun Engn Kharagpur W Bengal India

ISBN: (纸本)9789813292918;9789813292901

Detecting irregularity in an image or video is an important task in quality control or automatic visual inspection. This paper presents an image embedding technique for detecting an irregularity or abnormality in images. This can further be utilized in image screening application. In the proposed architecture, deep adversarial autoencoder is trained to extract the features from images. Using these features and skip-gram model, we develop the image2vec architecture to capture contextual probability in an image. Various score aggregation techniques are explored and its performance is reported. As a case study, we present a scenario of foreign body object detection in clinical-grade X-ray images. The proposed approach is found to correctly detect and localize abnormality in images.

关键词： Visual inspection image embedding Contextual abnormality detection Adversarial autoencoder Skip-gram Negative sampling

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Robust Single image Super Resolution Employing ADMM with Plug-and-Play Prior 3rd

Robust Single Image Super Resolution Employing ADMM with Plu...

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3rd international conference on computer vision and image processing (cvip)

作者： Rahiman, V. Abdu George, Sudhish N. Govt Coll Engn Kannur Kannur India Natl Inst Technol Calicut Kozhikode India

ISBN: (纸本)9789813292918;9789813292901

image super resolution is a signal processing technique to post-process a captured image to retrieve its high-resolution version. Majority of the conventional super resolution methods fail to perform in presence of noise. In this paper, a noise robust reconstruction based single image super resolution (SISR) algorithm is proposed, using alternating directionmethod of multipliers (ADMM) and plug-and-play modeling. The plug-and-play prior concept is incorporated to the two variable update steps in ADMM. Therefore, a fast SISR model and a denoiser are used in ADMM to implement the proposed robust SISR scheme. The experimental results show that the noise performance of the proposed approach is better than the conventional methods. The impact of parameter selection on the performance of the algorithm is experimentally analyzed and the results are presented.

关键词： Single image super resolution Plug-and-play prior Noise robust

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Two-View Triangulation: A Novel Approach Using Sampson's Distance 3rd

Two-View Triangulation: A Novel Approach Using Sampson's Dis...

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3rd international conference on computer vision and image processing (cvip)

作者： Verma, Gaurav Poddar, Shashi Kumar, Vipan Das, Amitava CSIR Cent Sci Instruments Org CSIO Chandigarh 160030 India Acad Sci Innovat & Res AcSIR CSIR CSIO Campus Chandigarh 160030 India

ISBN: (纸本)9789813290884;9789813290877

With the increase in the need for video-based navigation, the estimation of 3D coordinates of a point in space, using images, is one of the most challenging tasks in the field of computer vision. In this work, we propose a novel approach to formulate the triangulation problem using Sampson's distance, and have shown that the approach theoretically converges toward an existing state-of-the-art algorithm. The theoretical formulation required for achieving optimal solution is presented along with its comparison with the existing algorithm. Based on the presented solution, it has been shown that the proposed approach converges closely to Kanatani-Sugaya-Niitsuma algorithm. The purpose of this research is to open a new frontier to view the problem in a novel way and further work on this approach may lead to some new findings to the triangulation problem.

关键词： Triangulation Stereovision Monocular vision Fundamental matrix Epipolar geometry

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