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检索条件"任意字段=Conference on Image Algebra and Morphological Image Processing V"

共 655 条记录，以下是1-10 订阅

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Machine Learning Based Leaf Disease Diagnosis System

Machine Learning Based Leaf Disease Diagnosis System

引用

2025 International conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Shalini, v. Baby Kumar, Bittu varma, P. varun Reddy, P. vinay Kumar Kumar, T. Tarun Kalasalingam Academy of Research and Education Department of Information Technology Tamil Nadu Krishnankoil India

ISBN: (纸本)9798331509828

This research work suggests developing a diagnostic tool by using the techniques of machine learning and computer vision for the identification of plant diseases based on leaf images. It incorporates various features such as spots, lesions, abnormal shapes, and signs of insect damage to identify potential health problems in the plants. The tool can use image-processing methods like contour analysis, colour space transformation, and morphological operations to detect and classify the diseases based on risk factors like shape and area of spots, along with the extent of damage on the leaf. The tool is designed to work with uploaded leaf images from Google Drive and then provide an extensive report on plant health indicators such as spots, shape, and insect damage. It's early enough to detect and consequently reduce crop losses. This detection system equally ensures more targeted application of pesticides. © 2025 IEEE.

关键词： Diagnosis

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Combining image processing and Machine Learning for Object Detection in Complex Environments

Combining Image Processing and Machine Learning for Object D...

引用

2025 International conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rajababu, Durgam Krishna Adusumilli, Sri Bhargav Rama Mohan, Chinnem Sristi, Jayamangala Arun Srinivas, T. Gobinath, v.M. Sr University School of Engineering Department of Eee Telangana Warangal India Mindquest Technology Solutions Llc PhoenixAZ85027 United States Narayana Engineering College Department of Computer Science and Engineering Andhra Pradesh Nellore524004 India Santhiram Engineering College A. P Nandyal India J. P. College of Engineering Department of Electrical and Electronics Engineering Tenkasi627852 India Rajalakshmi Institute of Technology Mechanical Engineering Department Tamilnadu Chennai India

ISBN: (纸本)9798331508685

The goal of object detection research is to address safety issues at industrial locations;yet, optimizing object detection efficiency while maintaining real-time capabilities is still difficult in complicated industrial settings. An unconditionally protected detector is proposed in this research to solve this issue. A bottleneck-structured residual block is used by the detector's backbone network to improve real-time performance and minimize computation. Additionally, a selective attentiveness network enhances object detection by recognizing important characteristics based on the neck structure's feature map's morphological attributes. The updated YOLOv4 model, Faster RCNN, and SSD on the COCO and KITTI datasets for object identification, is proposed in this research to address the aforementioned issues. Particularly important tasks requiring strong mathematical underpinnings are picture segmentation, object detection, and classification. Although there have been improvements, there are still issues, like the need to define terms like deep learning, machine learning, and artificial intelligence. A strong research foundation requires precise definitions and interpretations. The development of machine vision is a reflection of a bold attempt to mimic human eyesight. The ability to simulate human behaviour and perception has advanced remarkably thanks to interdisciplinary cooperation and using machine learning techniques. © 2025 IEEE.

关键词： Adversarial machine learning

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image algebra and morphological image processing v

Image Algebra and Morphological Image Processing V

引用

image algebra and morphological image processing v 1994

The proceedings contain 34 papers. The topics discussed include: optimization of soft-morphological filters by genetic algorithms;adaptive resonance theory and self-organizing morphological kernels;optimal openings for overlapping signal and noise grains;stacking-matrix-based fast procedure for optimal stack filter design;learning the identity with basic morphological operators (potato peeler algorithm);image compression using multiresolution morphological decomposition;and radiographic inspection, defects segmentation, and multiscale analysis.

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Machine Learning Based Leaf Disease Diagnosis System

Machine Learning Based Leaf Disease Diagnosis System

引用

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

作者： v. Baby Shalini Bittu Kumar P varun varma P vinay Kumar Reddy T Tarun Kumar Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

关键词： Plant diseases Shape Insects Crops Machine learning Pesticides Internet Medical diagnosis Lesions morphological operations

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Soft Computing Based Agricultural Crops Monitoring and Disease Detection

Soft Computing Based Agricultural Crops Monitoring and Disea...

引用

2025 International conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Abhilash, Pideka Kundil Rex vijay, v. Kavitha, v. Alzubaidi, Laith H. Muthubalaji, S. Kaliappan, S. Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Sethu Institute of Technology Department of Electrical Communication Engineering Pulloor Tamil Nadu Kariapatti 626115 India College of Technical Engineering The Islamic University Department of computers Techniques engineering Najaf Iraq Cmr College of Engineering & Technology Department of Eee Telangana Hydrabad India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Phagwara 144411 India

ISBN: (纸本)9798331527495

In light of the growing need for intelligent farming, it is crucial to ensure effective development & enhanced output of plants. Tracking a plant from its development to its picking is essential in order to improve output & profitability. The paper presents the development of a system that utilizes image processing techniques to identify and monitor diseases in fruits during the entire process through planting to picking. This objective is accomplished by utilizing the idea of artificial neural network. The investigation focuses on four illnesses that affect tomato crops. The approach under consideration utilizes two picture databases. The initial dataset is utilized to train contaminated photos, while the subsequent dataset is employed for implementing additional inquiry photographs. The training dataset undergoes weight modification through the process of back propagation. The results of the study demonstrate the categorization & matching of pictures to their corresponding groups. The photographs are classified into colour, texture, & morphological categories. The morphological characteristic yields a 94%accuracy rate, surpassing the performance of the remaining two characteristics. The system's design exhibits a high level of efficacy in identifying the propagation of diseases. The technique has been implemented practically using MATLAB. © 2025 IEEE.

关键词： Back Propagation Colour Neuronal Networks Photo processing Segmented Texture

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Soft Computing Based Agricultural Crops Monitoring and Disease Detection

Soft Computing Based Agricultural Crops Monitoring and Disea...

引用

Intelligent Control, Computing and Communications (IC3), International conference on

作者： Pideka Kundil Abhilash Rex vijay v v. Kavitha Laith H. Alzubaidi S. Muthubalaji S. Kaliappan Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electrical Communication Engineering Sethu Institute of Technology Kariapatti Tamil Nadu India Department of computers Techniques engineering College of Technical Engineering The Islamic University Najaf Iraq Department of EEE CMR College of Engineering & Technology Hydrabad Telangana India Division of Research and Development Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

In light of the growing need for intelligent farming, it is crucial to ensure effective development & enhanced output of plants. Tracking a plant from its development to its picking is essential in order to improve output & profitability. The paper presents the development of a system that utilizes image processing techniques to identify and monitor diseases in fruits during the entire process through planting to picking. This objective is accomplished by utilizing the idea of artificial neural network. The investigation focuses on four illnesses that affect tomato crops. The approach under consideration utilizes two picture databases. The initial dataset is utilized to train contaminated photos, while the subsequent dataset is employed for implementing additional inquiry photographs. The training dataset undergoes weight modification through the process of back propagation. The results of the study demonstrate the categorization & matching of pictures to their corresponding groups. The photographs are classified into colour, texture, & morphological categories. The morphological characteristic yields a 94 % accuracy rate, surpassing the performance of the remaining two characteristics. The system's design exhibits a high level of efficacy in identifying the propagation of diseases. The technique has been implemented practically using MATLAB.

关键词： Training Backpropagation Crops Mathematical models Topology Monitoring Biological neural networks System analysis and design Diseases Testing

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Combining image processing and Machine Learning for Object Detection in Complex Environments

Combining Image Processing and Machine Learning for Object D...

引用

Pervasive Computational Technologies (ICPCT), International conference on

作者： Durgam Rajababu Sri Bhargav Krishna Adusumilli Chinnem Rama Mohan Jayamangala Sristi T. Arun Srinivas v. M. Gobinath Department of EEE School of Engineering SR University Warangal Telangana India Mindquest Technology Solutions llc Phoenix Arizona USA Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Santhiram Engineering College Nandyal A. P Department of Electrical and Electronics Engineering J. P. College of Engineering Tenkasi Mechanical Engineering Department Rajalakshmi Institute of Technology Chennai Tamilnadu

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The goal of object detection research is to address safety issues at industrial locations; yet, optimizing object detection efficiency while maintaining real-time capabilities is still difficult in complicated industrial settings. An unconditionally protected detector is proposed in this research to solve this issue. A bottleneck-structured residual block is used by the detector's backbone network to improve real-time performance and minimize computation. Additionally, a selective attentiveness network enhances object detection by recognizing important characteristics based on the neck structure's feature map's morphological attributes. The updated YOLOv4 model, Faster RCNN, and SSD on the COCO and KITTI datasets for object identification, is proposed in this research to address the aforementioned issues. Particularly important tasks requiring strong mathematical underpinnings are picture segmentation, object detection, and classification. Although there have been improvements, there are still issues, like the need to define terms like deep learning, machine learning, and artificial intelligence. A strong research foundation requires precise definitions and interpretations. The development of machine vision is a reflection of a bold attempt to mimic human eyesight. The ability to simulate human behaviour and perception has advanced remarkably thanks to interdisciplinary cooperation and using machine learning techniques.

关键词： Computational modeling Object detection Detectors Object segmentation video surveillance Real-time systems Reflection Safety Neck Object recognition

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morphological ANALYSIS BASED ON DISTANCE TRANSFORMS 5

MORPHOLOGICAL ANALYSIS BASED ON DISTANCE TRANSFORMS

引用

conference on image algebra and morphological image processing v

作者： STAROvOITOv, v BELARUSSIAN ACAD SCI INST ENGN CYBERNET MINSK 220012 BELARUS

ISBN: (纸本)081941624X

The digital Mathematical Morphology and the Distance Transform (DT) have many points of intersection. The DT combines numerical features and objects shapes. The properties of digital distance functions (metrics, asymmetries and quasi-metrics) and DT based on these functions are studied. Some extensions of the transform and the interpretation of the grey scale DT by application of the binary DT in the n-dimensional digital space are given. The morphological erosion and dilation may be performed by the DT for binary and grey scale images.

关键词： Transform theory Binary data Aluminum morphological analysis Mathematical morphology Radon Distance measurement image analysis image processing Selenium

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Compact optical morphological image processor with single operation mode

Compact optical morphological image processor with single op...

引用

Hybrid image and Signal processing v conference

作者： Wu, MX Yuan, SF Yan, YB Jin, GF Cheng, G TSING HUA UNIV DEPT PRECIS INSTRUMENTSBEIJING 100084PEOPLES R CHINA

ISBN: (纸本)0819421324

We present a single operation mode binary image algebra (SOMBIA) based on a basic operation that is a dilation- complement operation followed by an intersection operation. We also propose the parallel architecture of a compact optical morphological image processor (COMIP) with only one operation mode to efficiently realize the parallel algorithm of SOMBIA. The optical implementation of the basic operation of SOMBIA is finally discussed.

关键词： image algebra morphological image processing optical information processing

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GENERALIZED morphological CENTER - SELF-DUALITY 5

GENERALIZED MORPHOLOGICAL CENTER - SELF-DUALITY

引用

conference on image algebra and morphological image processing v

作者： CHARIFCHEFCHAOUNI, M SCHONFELD, D UNIV ILLINOIS DEPT ELECT ENGN & COMP SCI MC 154SIGNAL & IMAGE RES LABCHICAGOIL 60607

ISBN: (纸本)081941624X

In this paper, we present an investigation of the generalized morphological center. The generalized morphological center is first introduced. Conditions are subsequently derived for the self-duality of the generalized morphological center. Several important examples of self- dual generalized morphological centers are finally presented.

关键词： NONLINEAR FILTERING morphological FILTERING SIGNAL AND image RESTORATION

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