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

共 13662 条记录，以下是1171-1180 订阅

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Classification of Red Watermelon Varieties Using Canny Edge Detection and CNN

Classification of Red Watermelon Varieties Using Canny Edge ...

引用

IEEE International conference on Automatic Control and Intelligent systems (I2CACIS)

作者： Milca Rejaiah B. Martinez Kian Matthew D. Dayrit Analyn N. Yumang School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9798350372106

ISBN: (纸本)9798350372113

Watermelon is a commonly cultivated fruit worldwide, especially in Southeast Asia. As one of the top exports in Asia, the commercialization of watermelon has its market. Its consumer appeal makes it one of the most sought-after fruits globally. With the watermelon fruit consisting of different varieties, consumers usually have difficulty in classifying watermelons due to their similar external appearances, especially when labeled under the same name. This study is conducted to implement a system that detects and classifies three red watermelon varieties, Red Export, Orchid Sweet, and Dixie Queen, with image processing algorithms. The researchers utilized Canny Edge Detection for the dataset’s preprocessing phase and Convolutional Neural Network (CNN) for its classification. The Raspberry Pi is also applied to this study. Moreover, the researchers created and collected their datasets for testing and validation data. The model used in this study has acquired 84.71% overall accuracy.

关键词： Accuracy image edge detection image processing Asia Process control Classification algorithms Convolutional neural networks

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Research on Unmarked Motion Action Recognition Technology Based on Computer Vision

Research on Unmarked Motion Action Recognition Technology Ba...

引用

image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Lun Zhao Shandong Vocational College of Science and Technology Weifang China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

The main purpose of this study is to explore the issues of real-time, accurate, and unmarked recognition of sports movements in recent years. By reviewing the relevant research on machine learning or deep learning for specific sports or target actions based on computer vision image data input, the aim is to provide references for the application of unmarked motion capture technology in the field of sports motion recognition. The research employed a literature review methodology, conducting searches in six databases, namely Web of Science, PubMed, Scopus, Google Scholar, IEEE Xplore, and China National Knowledge Infrastructure (CNKI), covering publications from January 2000 to June 2020. Through boolean logic operations on the retrieved literature, key information such as first author/publication year, types/targets of motion, participant information, camera parameters, image feature extraction techniques, action recognition algorithms, evaluation methods for action recognition quality, training and validation methods for image data, and performance metrics for action recognition were extracted. After screening, a total of 23 articles were included in the study. The findings revealed that $39 \%$ of the studies utilized machine learning algorithms based on support vector machines, while $35 \%$ employed deep learning algorithms based on convolutional neural networks. Commonly used evaluation metrics for action recognition quality included classification accuracy, confusion matrix, and displacement error. The development of computer vision motion capture, models, and algorithms demonstrated promising applications in areas such as action technique recognition and sports performance analysis. Traditional machine learning algorithms like support vector machines and principal component analysis remain dominant in action recognition technology; however, in certain scenarios, the performance of deep learning algorithms surpassed that of traditional machine learning methods.

关键词： Deep learning Support vector machines Computer vision Machine learning algorithms image recognition Cameras Feature extraction Motion capture Real-time systems Sports

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Hybrid Machine Learning Techniques for image Denoising Based on Wavelet Transform

Hybrid Machine Learning Techniques for Image Denoising Based...

引用

IEEE International conference on Power, Intelligent Computing and systems (ICPICS)

作者： Qikun Yuan University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

Wavelet theory has been widely applied in image processing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. This paper introduces a novel image denoising method. It leverages wavelet transform and machine learning algorithms, presenting a synergistic approach that combines discrete wavelet transform coefficients with Random Forests and CNNs. This method significantly enhanced image clarity without increasing computational demand. This article utilized wavelet transform to extract image features, and then trained the model with wavelet coefficients as training data through machine learning methods, integrating Random Forests and Convolutional Neural Networks to analyze the effectiveness of different wavelet coefficients, thereby optimizing the model's denoising performance. It has been proved effective in denoising performance after rigorous validation through quantitative comparisons of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the high-quality stereo image dataset Flickr1024.

关键词： Machine learning algorithms Computational modeling Noise reduction Transforms Feature extraction Discrete wavelet transforms Convolutional neural networks Random forests image denoising Wavelet coefficients

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An exploration of pre-processing approaches for iris spoof detectors 1

An exploration of pre-processing approaches for iris spoof d...

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1st International conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2022

作者： Verma, Palak Selwal, Arvind Sharma, Deepika Central University of Jammu Department of Computer Science and Information Technology Jammu and Kashmir181143 India

ISBN: (数字)9781665480048

ISBN: (纸本)9781665480048

Iris recognition has fascinated the attention of several real-life applications to offer secured and reliable human authentication. However, these systems are exposed to a diverse range of security breaches that include direct spoofing of the sensing device or using some indirect means to gain illegal access to the authentication system. To mitigate these spoof attacks on the sensor module, an anti-spoofing sub-module is integrated alongside the sensor that intelligently classifies a presented iris trait as real or fake. With the emergence of machine learning algorithms, the problem of spoof detection has become comparatively easier, where a model learns from training samples to detect the liveliness of a given image. Building a spoof detection model involves three important steps namely: pre-processing, feature extraction, and a classifier. In this study, we propound a comparative analysis of pre-processing techniques, which are widely deployed in data-driven iris spoof detectors (ISDs). The presented image pre-processing techniques are explored based on two categories of ISD mechanism namely;hand-crafted features or deep learning (DL)-based approaches. Our study reveals that pre-processing techniques significantly improves the quality of raw images that leads to better prediction accuracy. © 2022 IEEE.

关键词： Feature extraction

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Real-Time Obstacle Detection and Path Adjustment for Autonomous Robots Using OpenCV

Real-Time Obstacle Detection and Path Adjustment for Autonom...

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Telecommunications and Intelligent systems (ICTIS), International conference on

作者： Abrar Elaoua Mohamed Nadour Lakhmissi Cherroun Mohammed Taqiyeddine Mahdi Ahmed Hafaifa Laboratory of Applied Automatic and Industrial Diagnostics (LAADI) University of Djelfa Djelfa Algeria

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

The real-time obstacle detection and path adjustment system for autonomous robots presented in this paper was created using OpenCV. The combination of image processing techniques enables the robot to identify and navigate around any obstacle efficiently. The main functions include real-time image acquisition; where the robot constantly takes pictures of its surroundings, and obstacle detection; which uses edge detection and contour analysis for proper identification of hazards in the environment. The method entails that once an object is detected, the robot's route is reshaped with sophisticated algorithms that are aimed at optimizing navigation as well as minimizing collision probabilities. A wide range of challenging settings such as static or dynamic environments were subjected to rigorous testing on the proposed system. The results indicate that it is robust and effective in detecting obstacles reliably to enhance navigation based on its capacity to make adjustments during operation time (discovered employing experimentation that took place in these settings).

关键词： Visualization Navigation Service robots image processing image edge detection Real-time systems Telecommunications Reliability Autonomous robots Testing

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Analysis of Duplicating the Human Mind Suspicions in AI in Various Divisions

Analysis of Duplicating the Human Mind Suspicions in AI in V...

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

作者： S. P. Santhoshkumar R. Karthika N. Vijayakumar S. Gajalakshmi K. Jayanthi S. Hariharasudhan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Information Technology School of Computing Rathinam Technical Campus Coimbatore India Computer Science and Engineering Pollachi Institute of Engineering and Technology Pollachi Coimbatore India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness of its output or decision-making. Uncertainty can arise in AI systems due to a variety of factors, including the quality of input data, variations in data formats, variations in context or environment, and the complexity of the underlying algorithms. Different AI applications can have specific types of uncertainty associated with them, as we discussed in previous questions about uncertainty in AI image recognition, natural language processing, data extraction, expert systems, planning and optimization, and robotics. To mitigate uncertainty in AI systems, researchers and developers use various methods, such as incorporating more data and diverse inputs, improving the quality of input data, enhancing the algorithms, and using feedback mechanisms to learn from errors and improve the system over time. However, complete elimination of uncertainty is often challenging or impossible, and users should always be aware of the potential for errors or unexpected behaviour in AI systems. In this paper, analysis of artificial intelligence uncertainty in various fields.

关键词： Uncertainty Accuracy image recognition Predictive models Probabilistic logic Natural language processing Planning Artificial intelligence Robots Optimization

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Saliency Detection of Logistics Packages Based on Deep Learning 2

Saliency Detection of Logistics Packages Based on Deep Learn...

引用

2nd IEEE International conference on Computer systems, ICCS 2022

作者： Jiang, Guanhui Zhang, Weizhong Wang, Wenshan Sun, Xiaoqi School of Computer Science and Technology Qingdao University Qingdao China Qingdao University Weihai Innovation Research Institute School of Computer Science and Technology Qingdao University Qingdao China

ISBN: (数字)9781665460101

ISBN: (纸本)9781665460101

Aiming at the problem that parcels in the logistics industry cannot be accurately located on the conveyor belt to obtain parcel location information, this paper proposes a parcel saliency detection and location method based on deep learning. Firstly, RGBD (Red-Green-Blue-Depth) images are acquired by depth cameras, and the images are filtered and hole-filled to remove noise and irrelevant information;then they are input to the constructed neural network model for training and testing;finally, the location of parcels in the images is obtained. Test experiments on the parcels on the conveyor belt show that the accuracy of locating the parcel position reaches 96.92% with an mean absolute error of only 0.0141, which can guarantee the accuracy of locating the parcel position and thus facilitate the post-processing of the parcel information. This method has greater research significance and engineering application value for the logistics industry. © 2022 IEEE.

关键词： image processing

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Autonomous System Identification and Exploitation through Observation of Behavior 4

Autonomous System Identification and Exploitation through Ob...

引用

conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications iv

作者： Bookman, Lake Clymer, Dan Sierchio, Jennifer Gerken, Mark BAE Syst 600 Dist Ave Burlington MA 01803 USA

ISBN: (纸本)9781510651036;9781510651029

Robust defenses to the new threats require early determination of the adversary's plan of attack. At the same time, automated path-planning systems often behave predictably and produce paths with recognizable characteristic features. The increasing adoption of autonomous systems changes the defense landscape, thwarting traditional defenses with rapid decision speeds, but opening up new weaknesses. In this work, we investigate several possibilities to exploit artifacts of path planning algorithms that might be used as tells, giving a defending commander early warning and advantage in mounting a defense. With the application of an integrated air-defense system in mind, we examine a use case of several high-value targets which are obstructed by known defending threats. We use incoming time-series track data to predict the most probable targets and future trajectories of an enemy platform. One such approach is to attempt to directly learn the mapping from threat track to target. However, such an approach is likely brittle, requiring large volumes of data and substantial retraining for each target laydown. By contrast, we attempt to exploit predictable features from the path-planning algorithm itself, by first classifying the path-planning algorithm being used, and including that knowledge in our target prediction algorithm. We demonstrate that it is possible to differentiate classes of path planning algorithms with high accuracy based on track data alone. Utilizing the underlying model, we can then predict likely track updates and likely targets. We discuss strengths and limitations of this approach with respect to the aim of adding a robust tool to the air-defense use case.

关键词： autonomy counter autonomy navigation machine learning path planning image classification

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Designing a Basketball Action Recognition System Based on the Improved OpenPose Algorithm

Designing a Basketball Action Recognition System Based on th...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Zhenhai Su College of General Education Harbin Huade University Harbin China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

In the realm of computer-assisted learning, video tutorials have gained significant traction for teaching various sports, including basketball. However, these tutorials often suffer from issues such as lack of standardization, clarity, and effectiveness. To address these challenges, this study presents a novel approach: a basketball action recognition system built upon an enhanced OpenPose algorithm. This system efficiently processes videos or images captured by learners, identifies crucial action frames, reconstructs human skeletal structures, computes joint angles, compares them with standard actions, and offers real-time posture correction guidance. Experimental validation showcases the system’s potential as a valuable tool for learning and honing basketball techniques. Furthermore, its adaptability suggests broader applications across diverse sports education contexts, emphasizing its practical significance and potential impact. The integration of machine learning algorithms ensures continuous improvement and customization, catering to individual learning needs and enhancing overall learning experiences.

关键词： Machine learning algorithms image recognition Education Tutorials Streaming media Real-time systems Reliability Standards image reconstruction Sports

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Parallel image processing for Liquid Level Detection 7th

Parallel Image Processing for Liquid Level Detection

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7th International conference on Computing in Engineering and Technology, ICCET 2022

作者： Devare, Manoj Amity University Maharashtra Mumbai India

ISBN: (纸本)9789811927188

The liquid level detection in a transparent bottle is the requirement in the food, Beverage industry, Scene understanding, Chemical Laboratories, and Forensic sciences. The liquid level detection indicates the quantity of the liquid in the bottle to assure customer satisfaction. The liquid level in a transparent container is an edge, a sudden change in intensity. The problem of liquid level detection is an image processing application of the Canny Edge Detector. The image data set is prepared using different sizes, transparency, color, and orientation as straight, landscape, and reverse. The transparent bottle classification accuracy rate is 87.5%, and the overall accuracy rate is 69.56%. The Serial and parallel algorithms are developed to improve the speed of execution. The multiprocessing and concurrent process pool of the multiprocessing approach found 4.6 times better turnaround execution time over synchronous pool map, concurrent thread pool, asynchronous thread pool, and serial execution. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bottles

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