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

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GLCM Based Feature Extraction and Medical X-ray image Classification Using Machine Learning Techniques 1st

GLCM Based Feature Extraction and Medical X-ray Image Classi...

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1st EAI International conference on Intelligent systems and Machine Learning, ICISML 2022

作者： Rout, Jyotiranjan Das, Swagat Kumar Mohalik, Priyabrata Mohanty, Subhashree Mohanty, Chandan Kumar Behera, Susil Kumar Department of Computer Science and Engineering Balasore College of Engineering and Technology Balasore India

ISBN: (纸本)9783031350771

The automated system is now created with excellent accuracy to detect abnormalities in X-ray images. To enhance the appearance of medical photographs, image pre-processing methods are applied, so that high accuracy can be achieved with constrained means. images are often classified based on their textural properties, which are measured using the Gray Level Co-occurrence Matrix (GLCM). The grey level correlation matrix provides statistical information of the second order on the grey levels of neighboring pixels in a picture (GLCM). In this proposed paper, medical X-ray images are classified and their features are extracted using an ensemble learning model. By extracting image features using the GLCM feature extraction method, this proposed model is able to distinguish between healthy and sick images (Gray level co-occurrence matrix).to improve the efficiency of the Ensemble learning classification method, it is compared against various algorithms using performance indicators, including Logistic regression, Gaussian Naive Bayes, as well as Random Forest. When this approach is compared to existing methods, the proposed ensemble model has an accuracy rate of 97% in classifying normal and diseased images. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： image enhancement

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Template for Preparation of Papers for IEEE Sponsored conferences & Symposia 25

Template for Preparation of Papers for IEEE Sponsored Confer...

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IEEE 25th International conference on Intelligent Transportation systems (ITSC)

作者： Jarofka, Maximilian Sieberg, Philipp M. Hurten, Christian Benedens, Tim Peters, Ricarda Kracht, Frederic E. Schramm, Dieter NIST Boulder CO 80305 USA Rice Univ Houston TX 77005 USA Colorado State Univ Dept Phys Ft Collins CO 80523 USA Univ Colorado Elect Engn Dept Boulder CO 80309 USA

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

This paper describes the integration of publicly available geoinformation into a simulation environment. Within the research project `AutoBin', the autonomous control of an inland vessel is to be tested in a simulated environment and the developed algorithms are to be validated. This requires a photorealistic simulation within a modern game engine. In this paper, the different sources of the used geodata are presented. Furthermore, the necessary adaptations of the data for the desired integration into the simulation are shown. At the end of the integration of the different data sources, an environment model of the entire test field in the project `AutoBin` - a part of the Dortmund-Ems-Canal from the lock Waltrop to the port of Dortmund - is created. The last section of this paper describes the necessary adaptations to integrate the environment model into the simulation infrastructure of the research and control center for autonomous inland vessels `VeLABi'.

关键词： Adaptation models Solid modeling Three-dimensional displays Soft sensors Stereo image processing Games Seaports

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Metrological characterization of different methods for recovering the optically sectioned image by means of structured light 12

Metrological characterization of different methods for recov...

引用

conference on Optical Measurement systems for Industrial Inspection xii

作者： Martinez, P. Bermudez, C. Carles, G. Cadevall, C. Matilla, A. Marine, J. Artigas, R. Sensofar Tech SL Barcelona Spain

ISBN: (数字)9781510643994

ISBN: (纸本)9781510643994

Imaging confocal microscopy (ICM) and focus variation (FV) are two of the most used technologies for 3D surface metrology. Both methods rely on the depth of focus of the microscope objective, which depends on its numerical aperture and wavelength of the light source to compute an optical section. In this paper we study how several methods of structured illumination microscopy affect the metrological characteristics of an areal optical profiler. We study the effect of the projection of different structured patterns, the sectioning algorithms, and the use of high and low frequency components onto the optically sectioned image. We characterized their performance in terms of system noise, instrument transfer function and metrological characteristics such as roughness parameters and step height values.

关键词： Focus Variation Confocal Optical sectioning Structured light

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A Survey on Privacy Preserving Content Based image Retrieval and Information Sharing in Cloud Environment 4

A Survey on Privacy Preserving Content Based Image Retrieval...

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4th International conference on Recent Trends in Computer Science and Technology, ICRTCST 2021

作者： Chouragade, Pushpanjali M. Ambhore, Premchand B. Government College Of Engineering Amravati Computer Science & Engineering MS Amravati India

ISBN: (纸本)9781665466332

As the quantity of images has expanded significantly, commonly utilized Content Based image Retrieval algorithms are commonly utilized in our ordinary routine. When it comes to processing and storing information, image retrieval systems are generally highly expensive. As a result, image owners should consider outsourcing these functions to a cloud server. However, since the cloud server can only be trusted to a certain amount, image owners' confidentiality might be jeopardized. This situation gives rise to the need of an algorithm/method which makes the images retrieval secure. For addressing this issue, the effort will be done to propose the Secure Content Based image Retrieval method with less complexity, high speed of operation, high accuracy and high retrieval efficiency. For this, various works have been studied to find best approach for achieving the efficient retrieval scheme. © 2022 IEEE.

关键词： Search engines

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Detection of Leaf Diseases Based on Ensemble Convolutional Neural Networks 4

Detection of Leaf Diseases Based on Ensemble Convolutional N...

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4th IEEE International conference on Computing, Communication, and Intelligent systems, ICCCIS 2023

作者： Chithra, P.L. Pushparani, S. Janes University of Madras Department of Computer Science Tamil Nadu Chennai India Ethiraj College for Women Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350306118

The appearance of infections in plantations is the most common cause of crop losses in a variety of crops. Due to lower product quality and a narrower offering of items, these usually result in reduced income for farmers and higher pricing for consumers. Infection symptoms are mostly evident in plant foliage, hence they are widely used to detect and identify diseases. Combined with computational intelligence or soft computing approaches, image-processing tools can help farmers diagnose diseases more effectively. The disease in plants can be identified using traits gleaned from its symptoms. A strategy using an ensemble approach for providing more precise predictions by merging results through different machine learning algorithms. A group of models cooperating to address a shared issue. Ensemble learning uses the strength of a variety of techniques to overcome the particular weaknesses of each model, as opposed to depending just on one model to provide the optimal answer. The set ought to be less error-prone than a single model. Combining many iterations of a projected model is the process of bagging. Before being used, each model is independently trained using an average model. Bagging's objective is to reduce variance below that of any model. In this work, images of leaves comprising healthy class and diseases obtained from the Plant Village dataset are fed into three Convolutional Neural Network architectures namely LeNet, Inception, and VGG16 net achieve an accuracy of 95%, 92%, and 95% respectively and the ensemble model obtains an improvement of 3.5% matched to the standard method. © 2023 IEEE.

关键词： image enhancement

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A novel approximation scheme for floating-point square root and inverse square root for FPGAs 11

A novel approximation scheme for floating-point square root ...

引用

11th International conference on Modern Circuits and systems Technologies (MOCAST)

作者： Pennestri, Pietro Huang, Yanqiu Alachiotis, Nikolaos Univ Twente Fac EEMCS NL-7522 NB Enschede Netherlands

ISBN: (纸本)9781665467179

Jointly computing the square root (SQRT) and the inverse square root (ISQRT) of floating-point numbers is common in many algorithms, e.g., in image or time series data processing when computing norms or vector normalization. Existing designs suffer from high latency and inefficient resource utilization due to the separate architectures that carry out these two operations. In this paper, we first propose a non-iterative approximation method for computing SQRT and ISQRT based on the Chebyshev min-max criterion to reduce the latency while meeting the accuracy requirements of various applications;thereafter a shared architecture of these two operations is designed and implemented in FPGA with less logic units. In contrast with other approximation solutions, our method does not need to perform any iterations and the accuracy can be mathematically estimated. A comparison with vendor-provided IP cores for FPGAs revealed that our proposed SQRT/ISQRT floating-point IP core utilizes less resources while reducing the clock-cycle latency by nearly four times.

关键词： Time-frequency analysis Time series analysis Computer architecture Chebyshev approximation Data processing Cyclones IP networks

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Comparative Analysis of Deep Learning algorithms for image Classification

Comparative Analysis of Deep Learning Algorithms for Image C...

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2nd International conference on Signal processing, Communication, Power and Embedded systems, SCOPES 2024

作者： Chowdary, Ginjupalli Pranay Rajakumar, K. Yalla, Sri Satya Narendra Yadav, Varthala Charith Kasetty, Sai Bhargav V.I.T University Scope Tamilnadu Vellore India

ISBN: (纸本)9798331506452

image classification is one of the highly explored fields in artificial intelligence and computer vision, due to its widespread applications such as medical image analysis, autonomous cars, geographical classification of satellite photos, and facial recognition. Generally, conventional image classification models depend upon manual feature extraction, but due to the exponential rise in data volume, manual adjustment is no longer feasible or efficient. This difficulty has led to the adoption of deep learning algorithms, which automate feature extraction and provide benefits such as high processing capacity, efficient feature filtering, and improved classification speed and accuracy. This research compares multiple deeplearning techniques for image classification. The study examines the results, offering a fundamental analysis and discussion of the findings where CNN proved to perform better when compared to other algorithms like GAN, RNN, and DBN with the highest accuracy of 92.8%. © 2024 IEEE.

关键词： Deep neural networks

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Implementation of Intelligent Robotic systems for Pick and Place based on image processing

Implementation of Intelligent Robotic Systems for Pick and P...

引用

Asian Control conference

作者： Sang-Won Kang Tae-Wook Um Nam-Jin Park Hyo-Sung Ahn School of Mechanical Engineering Gwangju Institute of Science and Technology Gwangju Korea

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper introduces an intelligent delta robot system enhanced with image processing to optimize Pick & Place operations in Agricultural Produce Centers (APCs). Facing a critical demand for mechanization in agriculture due to a shrinking rural workforce, our system utilizes camera-based segmentation and depth estimation to efficiently automate the packaging of fruits and vegetables. It concentrates on essential tasks such as precise gripping and ungripping, supported by advanced camera-based visual sensors. Integrating these vision technologies with delta robot kinematics and specialized image processing algorithms allows the robot to execute highly accurate movements. Our implementation showcases significant enhancements in the efficiency and reliability of APC operations, advancing the field of agricultural robotic automation and establishing a new standard for future developments in automated food production.

关键词： image segmentation Visualization Automation Estimation Packaging Streaming media Robot sensing systems

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Deep Learning Approach to Automated Tomato Plant Leaf Disease Diagnosis 2

Deep Learning Approach to Automated Tomato Plant Leaf Diseas...

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2nd International conference on Electronics and Renewable systems, ICEARS 2023

作者： Karthika, I. Megha, M. Roshni, M. M. Kumarasamy College of Engineering Dept. of Computer Science Engineering Tamilnadu Karur India

ISBN: (纸本)9798350346640

India's agriculture is primary occupation and it experiences a yearly loss of 35% in crop productivity due to plant-related issues. Advances in technology, such as precision agriculture and the use of Deep learning algorithms, are helping to make this possible. A number of practical and technological challenges, including dataset collecting and labelling, variability in plant leaf images, inter-class similarity, imbalanced datasets, generalization, and real-time detection, are prevalent in the challenging task of detecting plant leaf disease. The main goal of utilizing a convolutional neural network (CNN) for plant leaf disease detection is to precisely categorize images of plant leaves into categories of healthy or diseased. Depending on the particular application scenario, there could be additional goals that researchers try to accomplish with their CNN models in addition to this main one. Tomato is a widely cultivated vegetable and is an important food crop worldwide. Employing machine learning and image processing methods for detecting diseases in tomato plant leaves can be highly efficient. These techniques can be used to analyze images of tomato leaves taken in the field, and an accurate algorithm can be used to identify the presence of disease symptoms. One such approach is using Convolutional Neural Networks (CNNs) algorithm which have been trained on a dataset of images of tomato leaves with and without disease symptoms. This can be done by gathering the images of the healthy tomato leaves and the infected ones and then training a CNN model to detect the difference. The trained model can categorize new images of tomato leaves into healthy or infected, offering a quick and precise method to identify diseases in tomato plants' leaves. © 2023 IEEE.

关键词： image segmentation

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Design and Implementation of VGA image Transmission Based on FPGA

Design and Implementation of VGA Image Transmission Based on...

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Automation, Robotics and Computer Engineering (ICARCE), International conference on

作者： Weijun Hao School of Cultural Tourism Chongqing Vocational College of Culture and Arts Chongqing China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

With the increase of image data scale, a faster and more efficient image acquisition and processing system is needed. FPGA has good parallel performance, which is very suitable for algorithms dealing with basic parallel features, and can greatly improve the efficiency of image acquisition and processing system. Aiming at the problem of FPGA image acquisition, processing and display, this paper carries out the design of real-time image acquisition system, which has the function of real-time display after processing the acquired image. In this paper, the chip of EP4CE6F17C8 of Cyclone IV series is used, the CMOS camera is OV5640 and the monitor is VAG. After full experimental verification, the system is extensible, the later expansion goal can not be achieved without the cooperation of Bluetooth and other facilities, with the role of recognizing and positioning license plates, obviously this series of application prospects are considerable; With flexibility, can customize the algorithm for optimization; Low cost a series of advantages.

关键词： Bluetooth Robot vision systems Streaming media Cameras Real-time systems Field programmable gate arrays License plate recognition System analysis and design Robots Optimization

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