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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems IV"

共 13603 条记录，以下是151-160 订阅

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Guided View Approach for Lane Detection and Caution Analysis using Computer Vision and Deep Learning Techniques

Guided View Approach for Lane Detection and Caution Analysis...

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Artificial Intelligence and Data Engineering (AIDE), International conference on

作者： Manjula Gururaj Rao Vinyas S Gururaj Rao H Priyanka Vishal Aisiri NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karkala Sasken Technologies Limited Bangaluru Computer Science & Engineering PESU Bangaluru

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

In recent years, advancements in computer vision technology have enabled innovative applications in various fields, including transportation safety. One such application is a Lane Detection and Caution Measurement Analyzer system aimed at enhancing road safety. This system uses computer vision techniques to provide real-time analysis of road conditions, helping drivers navigate more safely. The Lane Detection module employs image processing algorithms such as edge detection, Hough transform, and perspective transformation to identify lane boundaries accurately. Additionally, the Caution Measurement Analyzer module utilizes object detection and tracking algorithms to identify potential hazards, such as potholes, and measures the caution required for safe driving. The proposed model integrates CNN, OpenCV, and YOLO, achieving 87% accuracy in object detection and caution measurement, and 94% accuracy in lane boundary detection.

关键词： YOLO Computer vision Accuracy Lane detection image edge detection Transforms Road safety Real-time systems Hazards Vehicles

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Design and implementation of interactive image measurement software based on Tkinter and PIL library 25

Design and implementation of interactive image measurement s...

引用

Proceedings of the 4th International conference on Computer, Artificial Intelligence and Control Engineering

作者： Xiao He Zhulin Wang Shaozheng Zhan National Key Laboratory of Strength and Structural Integrity Xi' an Shaanxi China Aircraft Strength Research Institute of China Xi' an Shaanxi China

ISBN: (纸本)9798400712647

In this paper, we design and implement a high-precision image processing and measurement software based on Python. The software utilizes the Tkinter and PIL libraries to build a graphical user interface and integrates a variety of commonly used image processing algorithms, including grayscale conversion, edge extraction, Gaussian blur, image sharpening, binarization, and median filtering, enabling effective image enhancement and segmentation. In addition, the software provides interactive measurement functionality, allowing users to draw rectangular regions on the image using the mouse and automatically calculate the dimensions of these rectangles. Measurement results can be stored, displayed, and managed, facilitating users in analyzing and processing the data. To evaluate the performance and reliability of the software, we conducted experiments on multiple datasets. The experimental results demonstrate that the software can accurately process images of different types and qualities and provide stable and reliable measurement results. Compared with the existing C-scan measurement software of current equipment, the software developed in this study has certain advantages in terms of functional integration, interactivity, and measurement accuracy. The design and implementation of this software can meet the need to replace the existing C-scan system software, while also providing valuable references and insights for the further development of intelligent and automated image analysis systems.

关键词： PIL

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An Enhanced Pre-processing Technique For Eye image Segmentation

An Enhanced Pre-Processing Technique For Eye Image Segmentat...

引用

Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International conference on

作者： Satrughan Kumar Arun Kumar Deepti Sahu Computer Science and Engineering Sharda University Greater Noida U.P India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Early diagnosis is essential to controlling glaucoma, a progressive disorder that results in irreversible vision loss. Retinal fundus imaging has emerged as a crucial diagnostic technique for retinal disorders, such as glaucoma, in recent times. But because of the intricate vascular patterns and intricate architecture of the retina, it can be difficult to analyze these images appropriately. Areas of interest in fundus pictures are frequently found using image segmentation algorithms; however, this can be challenging. This study examines a variety of segmentation techniques, emphasizing both conventional and cutting-edge ways for splitting retinal fundus pictures. It also covers assessment metrics that are used to evaluate how well segmentation algorithms perform in identifying minute structural alterations associated with glaucoma. The study also looks at how deep learning algorithms can be implemented to separate optic disc and cup. Deep learning models typically perform worse with larger datasets, even though they have demonstrated good accuracy with smaller datasets. The investigation underscores the need for more sophisticated deep learning methodologies and efficient segmentation techniques, as it shows that effectively segmenting the optic disc (OD) and cup (OC) remains a considerable challenge, particularly for large and complex datasets.

关键词： Deep learning Glaucoma Measurement Learning systems image segmentation Accuracy Refining Lighting Retina Optical imaging

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Deepfake image and Video Detection Using Deep Learning algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

引用

Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks Artificial intelligence Research and development

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Construction Waste image Classification Algorithm Based on Fusion of Multiple Features

Construction Waste Image Classification Algorithm Based on F...

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Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Yi You Boon Cheong Chew Institute of Technology Management and Entrepreneurship Universiti Teknikal Malaysia Melaka Malacca Malaysia Construction College Taizhou Vocational & Technical College Taizhou China Faculty of Technology Management and Technopreneurship Universiti Teknikal Malaysia Melaka Malacca Malaysia

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

The rapid development of global urbanization has triggered the rapid expansion of the construction industry. The consequent construction waste problem has caused serious pollution to the environment and occupied a large amount of valuable land resources. In this context, effective classification and treatment of construction waste has become particularly urgent, which can help reduce environmental burdens and promote sustainable economic development. This study proposes a fine-grained construction waste classification algorithm based on image processing technology. The algorithm achieves automatic classification through a multi-feature fusion model and can accurately distinguish five major types of construction waste, including concrete, bricks, metal, wood, and plastic. This study further explores the impact of spatial positional relationships, semantic correlation, and internal structure analysis on improving classification accuracy. These research results not only provide technical support for the effective processing of construction waste, but also provide new methods and perspectives for other fine-grained image classification tasks. Experimental results prove that the algorithm proposed in this study significantly improves the classification accuracy. Specifically, the algorithm achieved high accuracy of 98.97%, 98.69%, 98.70%, 98.72% and 98.68% respectively in the classification tasks of concrete, bricks, metal, wood and plastic, which is significantly better than those based on CNN and enhanced FCM. Although these models are close to the performance of the algorithm in this study in some cases, the overall accuracy is lower.

关键词： Accuracy Urban areas Semantics Metals Stability analysis Real-time systems Classification algorithms Plastics Reliability image classification

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AI and Cloud for Real-Time Evaluation of Sports Injury Severity and Recovery

AI and Cloud for Real-Time Evaluation of Sports Injury Sever...

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Visual Analytics and Data Visualization (ICVADV), International conference on

作者： Blessy Y M Palaniraj Rajidurai Parvathy Kishore Verma S B. Sreedevi S. Jayaprakash S. Murugan Department of Electornics and Communication Engineering R.M.K. Engineering College Chennai Tamil Nadu India Mphasis Corporation Chandler Arizona USA Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus (GNITC) Hyderabad Telangana India Department of Computer Science and Engineering Sri Sairam Institute of Technology Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

This research provides a revolutionary artificial intelligence (AI) and cloud-based system for assessing the severity of sports injuries in real time and tracking healing progress. The objective is to develop an AI and Cloud-based system for evaluating sports injuries in real time, providing accurate severity assessment, recovery tracking, and personalised treatment recommendations with powerful Natural Language processing (NLP) and image analysis algorithms. The proposed method uses modern NLP and deep learning-based image analysis algorithms to process and analyze data from a variety of sources, such as IoT-enabled wearable devices, medical reports, and athlete feedback. NLP is used to extract useful insights from unstructured text, while image analysis finds damage patterns in medical images. By combining these technologies with cloud computing, the system provides a scalable and economical solution for real-time injury assessment, including personalized rehabilitation programs and predictive modelling. AI enables automated, data-driven decision-making, which reduces human error while increasing diagnostic speed and accuracy. Future study will look at the addition of more AI algorithms, expanding the dataset to include more injury kinds, and improving the system's real-time feedback capability. This method aims to transform sports injury care and recovery optimization.

关键词： Cloud computing image analysis Accuracy Transforms Real-time systems Natural language processing Artificial intelligence Injuries Medical diagnostic imaging Sports

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ResNet-50 for Flower image Classification: A Comparative Study of Segmentation and Non-Segmentation Approaches

ResNet-50 for Flower Image Classification: A Comparative Stu...

引用

International conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Abdul Rachman Manga Nirmala Huzain Azis Farniwati Fattah Yulita Salim Herdianti Darwis Departement of Computer Science Universitas Muslim Indonesia Makassar Indonesia

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

Flower image classification poses a challenge in digital image processing, requiring effective methods for feature extraction and classification. The aim of this research is to improve the accuracy of flower image classification by employing ResNet-50 for feature extraction, with and without segmentation, and evaluating the effectiveness of various classification algorithms. The dataset consists of images of Calendula and Coreopsis flowers, totaling 2,025 samples. Four segmentation techniques-Canny, Thresholding, Otsu, and Mean Shift-along with a non-segmentation approach are applied. The features extracted using ResNet-50 are classified with Naive Bayes, Support Vector Machine (SVM), Decision Tree, and K-Neighbors Classifier (KNN). Performance evaluation is conducted using accuracy, precision, recall, and F1-score with 5-fold cross-validation. The results show that SVM delivers the best performance in most scenarios. The highest accuracy in segmentation scenarios was achieved with the Mean Shift technique at 0.91, while the non-segmentation approach yielded the highest accuracy of 0.95. The non-segmentation approach proves more effective, indicating segmentation is not always required for high classification accuracy with ResNet-50. This study shows that ResNet-50, especially without segmentation, can significantly improve flower image classification compared to traditional methods, opening opportunities for more efficient systems.

关键词： Support vector machines Performance evaluation image segmentation Accuracy Thresholding (Imaging) Flowering plants Streaming media Feature extraction Classification algorithms image classification

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Optimization of Micro-Object Identification with Factorization and Correction Mechanisms for Distorted image Points

Optimization of Micro-Object Identification with Factorizati...

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International Russian Smart Industry conference (SmartIndustryCon)

作者： Isroil I. Jumanov Rustam A. Safarov Olimjon I. Djumanov Intelligent systems and computer technologies Samarkand state university Samarkand Uzbekistan

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The scientific and methodological foundations for the optimal identification of micro-objects with mechanisms for using information structural components, features, statistical and dynamic characteristics of images have been developed. Mechanisms for tracking, detecting and correcting distorted points, factorizing the structural components of the image, filtering, segmentation, tracking distorted points under conditions of a priori insufficiency, parametric uncertainty, and low reliability of information processing are proposed. Methods for converting the initial brightness of a point into homogeneous objects as a function of two variables have been studied and nonlinear optimization functionals have been used. Mechanisms for factorization of the structural components of the image in the form of an indicator of linear connectivity, “neighborhood relationship” of three separate algorithms have been implemented. An alternative image factorization algorithm has been developed. A comparative analysis of effectiveness was carried out. The effectiveness of the mechanisms for detecting and correcting distorted points was studied using the criterion of the probability of undetected errors, the root-mean-square error, according to Shannon's estimates, the average level of intensity of the points, and the total compactness parameter. Rank estimates of integral indicators of image factorization were obtained for 30 images with 100 tests. A software package for visualization, recognition, and classification of images of microobjects has been developed, the implementations of which have been tested using real medical diagnostic data.

关键词： image segmentation Uncertainty Software packages Nonlinear distortion Brightness Noise Information processing Reliability Object recognition Optimization

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Design and Implementation of an Autonomous Line-Following Minidrone Using image processing and PID Control

Design and Implementation of an Autonomous Line-Following Mi...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International conference

作者： Rekha P Maaz Hussain Misrah Naveed Dept. of ECE BNM Institute of Technology Bangalore India 7th Sem ECE BNM Institute of Technology Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

This paper presents a comprehensive approach to controlling a Parrot Mambo minidrone in a simulation environment using MATLAB. The primary objective is to guide the drone along a predefined path while maintaining stability and precision. Optical flow technique and Inertial Measurement Unit sensor data are used for real-time position and velocity estimation. Path planning algorithms, coupled with control strategies such as Kalman filtering and dynamic error correction, ensure the drone follows the optimal path to reach its target. Simulation results demonstrate the effectiveness of the proposed system in successfully navigating the drone through complex maneuvers with minimal drift.

关键词： Filtering Simulation Heuristic algorithms Real-time systems Error correction Kalman filters MATLAB Optical flow Tuning Drones

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Depth and Dimension Estimation Using Computer Vision

Depth and Dimension Estimation Using Computer Vision

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International conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： Ravindran S Srikanth S Raagul T S Madhankumar R M Ganesan M Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The growing importance of depth and dimension estimation in robotics, autonomous vehicles, and construction industries highlights the need for accurate environmental perception. Monocular depth-based cameras face challenges such as scale ambiguity and data scarcity, making precise distance measurements difficult. Stereo-vision setups provide improved scene information, thereby reducing the error rate. Deriving depth from stereo images using a Pseudo-LIDAR based approach enhances depth data. Pixel-to-length conversion from this depth information enables precise dimension estimation of objects. Developing a computer vision algorithm that calculates pixel-to-length ratios at various distances and adjusting for calibration discrepancies will streamline decision-making in autonomous systems, improving efficiency across industrial operations.

关键词： Wireless communication Computer vision Accuracy image edge detection Depth measurement Estimation Signal processing algorithms Streaming media Stereo vision Autonomous vehicles

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