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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Simulation Research and Development of Agricultural Products processing Process Based on machine Learning Algorithm

Simulation Research and Development of Agricultural Products...

引用

2022 International conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2022

作者： Peng, Luo Shihezi University School of Food Science and Technology Xinjiang Shihezi832003 China

ISBN: (数字)9781665454575

ISBN: (纸本)9781665454575

In recent years, with the development of science and technology and its application in agricultural production, China's agricultural science and technology have made great progress, the concept of "agricultural processing"has been mentioned, and the research on agricultural processing has also achieved fruitful results. The combination of intelligent and automated machine learning algorithms with traditional industries can promote productivity improvement on the one hand, and realize industrial upgrading and transformation on the other hand. However, in practical production applications, machine learning algorithms are restricted by factors such as high cost, and the research and application of machine learning algorithms are greatly limited. With the development of virtual simulation technology in the field of machine learning algorithm research, it provides a new way for machine learning algorithm technology to be applied to agricultural product processing. Therefore, the research on machine learning algorithms has become a trend. The development of machine learning algorithms will drive the development of modern agriculture. It is very necessary for the research of machine learning algorithms to learn algorithms. The image is converted into a data matrix, and a computer used to replace the human brain is used to analyze the image, while completing a vision related task. China's agricultural development is facing severe challenges such as rising costs, continuous deterioration of the ecological environment and high tension of resource conditions. With the deepening of machine learning algorithm research and the rapid development of machine learning algorithm technology, machine learning algorithm simulation technology, as a safe and economic experimental tool in the application of machine learning algorithm technology, plays a more and more important role. In order to make full use of the latest research results abroad and narrow the gap with the advanced level ab

关键词： Deterioration

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Deep Learning and machine Learning Based Efficient Framework for image Based Plant Disease Classification and Detection

Deep Learning and Machine Learning Based Efficient Framework...

引用

2022 International conference on Advanced Computing Technologies and applications, ICACTA 2022

作者： Nancy, P. Pallathadka, Harikumar Naved, Mohd Kaliyaperumal, Karthikeyan Arumugam, K. Garchar, vipul Saveetha Institute of Medical and Technical Sciences Chennai India Manipur International University Manipur India Amity University Noida India AMBO University IT HH Campus India Karpagam Academy of Higher Education Coimbatore India Junagadh Agricultural University Junagadh India

ISBN: (纸本)9781665495158

Without agriculture, human existence would be inconceivable. A large percentage of the world's population relies on agriculture for their daily needs. In addition, it creates a big number of jobs in the area. Using traditional agricultural practices results in lower yields, which is the fault of farmers. Agriculture and allied sectors will continue to be critical to the economy's long-term growth and prosperity. Farming has a slew of challenges, including disease detection and control and crop monitoring and tracking. Farming with intelligence is a realistic option in many situations. Smart agriculture is now possible because to the internet of things and machine learning approaches. Computer vision, image processing, and machine learning techniques are used in the automated leaf disease diagnostic system to analyze photographs of diseased leaves. A farmer can make an educated choice regarding a plant illness thanks to automated disease detection equipment that speeds up the diagnostic process. A farmer had to first send the contaminated leaf to a pathology lab for confirmation of the illness, which was a tedious process. It is the purpose of this paper to propose a framework for the real-time classification of agricultural images. Crop disease pictures categorization and illness prediction are made easier using this system. © 2022 IEEE.

关键词： image segmentation

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Bionic model of information processing in the retina

Bionic model of information processing in the retina

引用

Artificial Intelligence, Computer, Data Sciences and applications (ACDSA), International conference on

作者： Sabine Bohlmann Institute of Computer Science Clausthal University of Technology Clausthal-Zellerfeld Germany

Today, bionic models for vision applications base on the general information pathways, structure and characteristics of the visual system implemented in intelligent algorithms, mostly based on AI, to improve the resolution, colour and contrast of images. These approaches often use machine learning techniques and neural networks, or simply mathematical pre-processing filters. The major drawback of all these methods is that they require a lot of computing power. But nature can't afford that kind of processing power just to improve contrast/resolution, detect contours or the true colours of an image and it doesn't use learning strategies or high-order mathematical equations to detect contours. Nature works very simply and very efficiently, it can only work with potentials and their differences. There is therefore a huge gap between AI-based approaches and image processing in the human visual system. This opens the way for a new bionic model of the general pathways of the retina - using only the simple principles of nature, potentials and their differences.

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Research on Real-Time 3D image Acquisition System Based on Stripe Projection

Research on Real-Time 3D Image Acquisition System Based on S...

引用

image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Duo Zhou Shibin Yin Hongbo Xie School of Precision Instrument and Opto-Electronics Engineering Tianjin University Tianjin China Isvision (Hangzhou) Technology Company Limited Hangzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In response to the demand for rapid acquisition of three-dimensional profiles in industrial online inspection, a real-time 3D image acquisition system has been designed and researched. This system is based on the principles of optical projection and binocular vision fusion, utilizing a DLP projector and industrial cameras to construct a hardware platform, along with the development of an efficient 3D reconstruction algorithm. A three-frequency phase-shifting coding scheme is employed in the stripe pattern design, and a phase unwrapping algorithm guided by a quality map is proposed to enhance reconstruction reliability. The system achieves parallel processing through a CUDA heterogeneous computing platform and establishes a four-stage pipeline structure to optimize real-time performance. Experimental results show that the system meets the expected goals in key metrics such as spatial resolution, measurement accuracy, and real-time performance. In practical applications on the mobile phone casing inspection production line, the system demonstrates superior detection efficiency and reliability compared to traditional methods. The research results provide a complete solution for rapid 3D inspection, holding significant engineering application value in fields like industrial quality inspection and reverse engineering.

关键词： Three-dimensional displays Production Inspection Parallel processing Optical imaging Real-time systems Time measurement Heterogeneous networks Spatial resolution image reconstruction

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Infrared Computer vision for Utility-Scale Photovoltaic Array Inspection

Infrared Computer Vision for Utility-Scale Photovoltaic Arra...

引用

International conference on Information, Intelligence, Systems and applications (IISA)

作者： David F. Ramirez Deep Pujara Cihan Tepedelenlioglu Devarajan Srinivasan Andreas Spanias SenSIP Center School of ECEE Arizona State University Tempe USA Poundra LLC Tempe AZ USA

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but often symptomatically exhibit temperature differences. Included is a mini survey to review these common faults and PV array fault detection approaches. Among these, infrared thermography cameras are a powerful tool for improving solar panel inspection in the field. These can be combined with other technologies, including image processing and machine learning. This position paper examines several computer vision algorithms that automate thermal anomaly detection in infrared imagery. We demonstrate our infrared thermography data collection approach, the PV thermal imagery benchmark dataset, and the measured performance of image processing transformations, including the Hough Transform for PV segmentation. The results of this implementation are presented with a discussion of future work.

关键词： Surveys Computer vision machine learning algorithms Reviews Infrared imaging Transforms Inspection Land surface temperature Solar panels Meteorology

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Research on Target Defect Detection Based on machine vision 4

Research on Target Defect Detection Based on Machine Vision

引用

2021 4th International conference on Modeling, Simulation and Optimization Technologies and applications, MSOTA 2021

作者： Lu, Chuqiao Li, Zhiyong Shi, Shaoyue Peng, Shaolong School of International Education Wuhan University of Technology Wuhan China School of Automation Wuhan University of Technology Wuhan China School of Navigation Wuhan University of Technology Wuhan China

The daily detection of highspeed electric multiple units (EMU) body is very important for China railway maintenance system. This paper proposes a new method based on machine vision to detect bolts and switches on EMU side skirt, which aims to replace manpower. Yolov3 network is used to identify and locate bolts and switches. After positioning, the status of bolts is detected through Alexnet network. Experiments show that the processing method can achieve the detection work efficiently and accurately. It is suitable for many cases such as insufficient illumination and non-vertical shooting of image samples, which has high robustness. © Published under licence by IOP Publishing Ltd.

关键词： Bolts

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Quality control automation of metallic surface using machine vision

Quality control automation of metallic surface using machine...

引用

conference on Photonics applications in Astronomy, Communications, Industry, and High Energy Physics Experiments

作者： Lenty, Bartosz Kwiek, Pawel J. AGH Univ Sci & Technol Al Mickiewicza 30 Krakow Poland

ISBN: (数字)9781510649569

ISBN: (纸本)9781510649569;9781510649552

The article presents the usage of machine vision to automate quality control (QC) of metallic surfaces. QC include detection of selected defects of metallic surface, i.e. scratches, cracks. Imaging using the scatter method has been proposed, resulting in greater contrast. The article provides a detailed description of the measurement stand, image acquisition method and image analysis algorithm. The project's principal aim is to construct an automatic system that controls the state of the surface with a frequency of 6 Hz.

关键词： machine vision vision system 2D image image processing quality control scattering scratches detection

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A New hybrid deep learning coder for Alzheimer classification

A New hybrid deep learning coder for Alzheimer classificatio...

引用

International conference on image and Signal processing and their applications (ISPA)

作者： Zoulikha Bebboukha Athmane Zitouni Department of Electrical Engineering Faculty of Science and Technology University of Mohammed Khider Biskra Algeria Vision and Communication Systems Laboratory (VCS) Biskra

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

Deep learning algorithms offer distinct advantages over other machine learning methods, enabling the exploitation of advanced techniques to analyze brain MRI scans. In a recent research study, a combination of ResNet-50 and DenseNet-201 convolutional neural network models was utilized to extract crucial features from brain MRI images, which were then fed into a compact classification model. The proposed model demonstrated remarkable results in accurately classifying the stages of Alzheimer’s disease. It outperformed all other approaches using the same dataset and achieved an outstanding accuracy of 99.9 % in categorizing the four cases, affirming the effectiveness and promising potential of this proposed model in the precise diagnosis and classification of Alzheimer’s disease.

关键词： Deep learning machine learning algorithms Databases Magnetic resonance imaging Signal processing algorithms Signal processing Feature extraction

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A Review on Quantum machine Learning in Different Computer vision Fields

A Review on Quantum Machine Learning in Different Computer V...

引用

IEEE International conference on Performance, Computing and Communications (IPCCC)

作者： Md Majedul Islam Jing Selena He Department of Computer Science Kennesaw State University Marietta USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

Quantum machine Learning (QML) promises the transformative potential in computer vision by utilizing quantum computing to facilitate faster high-dimensional data processing. In this paper, we will go through some of the recent works that employ QML for computer vision problems such as image Segmentation, Classification, and Generation. Demonstrations aimed at showing where QML methods beat the state of art techniques in particular applications like facial recognition, medical imaging, and satellite imagery. QML aspires to make pathbreaking changes in a field limited by current hardware capabilities. This poster abstract summarizes the important studies, methodologies and findings to inform further research in this developing field.

关键词： Computer vision image segmentation Quantum computing Reviews High dimensional data Face recognition Generative adversarial networks Hardware Satellite images Biomedical imaging

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Fast Correction and Stitching Algorithm for Fisheye images

Fast Correction and Stitching Algorithm for Fisheye Images

引用

image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Qian Li Shantao Song Ruixue Liu Eugene Levin Shandong Provincial Key Laboratory of Robot and Manufacturing Automation Technology Institute of Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China Department of Biomedical Data Science School of Applied Computational Sciences Meharry Medical College Nashville Tenn USA

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Fisheye cameras, with their ultra-wide-angle field of view, can capture a larger scene in a single shot compared to traditional lenses. This capability makes them highly valuable in areas such as security surveillance, panoramic photography, autonomous driving, and robot navigation. However, building an efficient and accurate visual system is crucial for realizing these applications. This paper proposes an improved algorithm based on fisheye image correction and stitching. First, a fast fisheye image distortion correction method is introduced to eliminate the severe distortion caused by the fisheye lens. Then, the o-FAST algorithm is used for feature point detection, ensuring rotational invariance of the feature points, and the KNN algorithm is employed for initial matching. On this basis, the PROSAC algorithm is applied to remove incorrect matches and compute the homography matrix between images. Finally, seamless image stitching is achieved using a gradual weighted fusion method. The research results demonstrate that this method significantly improves both image quality and computational efficiency, making it effective for generating and processing panoramic images. Experimental results further confirm that the method enhances stitching accuracy while also increasing processing speed, showcasing its high practical value.

关键词： Accuracy Surveillance Robot vision systems visual systems Distortion Feature extraction Cameras Security image stitching Lenses

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