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

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

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

引用

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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Design and Implementation of VGA image Transmission Based on FPGA

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

引用

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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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 ...

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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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SuperTA-Net: A Supervised Transmission-Augmented Network for image Compressive Sensing Reconstruction

SuperTA-Net: A Supervised Transmission-Augmented Network for...

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Data-driven Optimization of Complex systems (DOCS), International conference on

作者： Zhijie Zhang Huang Bai Ljubiša Stanković Junmei Sun Xiumei Li School of Information Science and Technology Hangzhou Normal University Hangzhou China Faculty of Electrical Engineering University of Montenegro Podgorica Montenegro

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Compressive sensing (CS) has seen extensive use in signal processing, particularly in tasks related to image reconstruction. CS simplifies the sampling and compression pro-cedures, but leaves the difficulty to the nolinear reconstruction. Traditional CS reconstruction algorithms are usually iterative, having a complete theoretical foundation. However, these iter-ative algorithms are constrained by significant computational complexity. While modern deep network-based methods can achieve high-precision reconstruction in compressed sensing (CS) with satisfactory speed, they often lack theoretical analysis and interpretability. To leverage the strengths of both types of CS methods, the deep unfolding networks (DUNs) have been developed. In this paper, a novel DUN named supervised transmission- augmented network (SuperTA-Net) is proposed. Based on the framework of our previous work PIPO-Net, the multi-channel transmission strategy is put forward to reduce the influence of critical information loss between modules and improve the reliability of data. Besides, in order to avoid the issues such as high information redundancy and high computational burden when too many channels are set, the attention based supervision scheme is presented to dynamically adjust the weight of each channel and remove the redundant information. Through exper-iments focused on reconstructing CS images, the proposed neural network architectures are shown to be highly effective.

关键词： Redundancy Signal processing algorithms Signal processing Reconstruction algorithms Propagation losses Sensors Reliability image reconstruction Compressed sensing Optimization

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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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Liquid Filling Quality Control System for Beverage Industry Using image processing (CNN)

Liquid Filling Quality Control System for Beverage Industry ...

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International conference on Control, Automation and systems ( ICCAS)

作者： B. Chatchapol W. Autanan W. Anan Department of Electrical Engineering Faculty of Electric Engineering Rajamangala University of Technology Lanna Chiang Mai Thailand Robotics Engineering and AI Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Electronic Engineering and Automation Control System Faculty of Engineering Rajamangala University of Technology Lanna Chiang Mai Thailand

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

The beverage industry has been leveraging automation to enhance production efficiency. An essential advancement involves the use of AI and image processing to ensure the high quality of products. image processing systems employ sophisticated algorithms to meticulously inspect specific aspects of the production process (liquid level, lid sealing, text print). The CNN algorithm is primarily utilized to scrutinize water bottle images to verify their quality. Maintaining consistent production speed poses a challenge, underscoring the importance of examining the image processing system from various perspectives. The research proposed implementing a system that oversees liquid filling quality through an image processing algorithm, a camera module, and conveyor belt speed control to address. This system monitors liquid levels and bottle capping while the production line is operational. On average, the system achieves a 98.89 percent accuracy rate in detecting liquid levels and a 100 percent accuracy rate in sealing the lids. Furthermore, the conveyor speed control can adjust automatically to detect variations in product movement speed.

关键词： Liquids image processing Velocity control Production Quality control Inspection Belts Filling Beverage industry Monitoring

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Review on Advanced Vehicle Recognition System: OCR and REST API Integration for Efficient Results 5

Review on Advanced Vehicle Recognition System: OCR and REST ...

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5th IEEE International conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Singh, Rohit Kumar Saxena, Aumreesh Kumar Jhapate, Arun Shrivastava, Ritu Srivastava, Rajiv Sirt Bhopal India Csit Sirt Bhopal India Cse Sirt Bhopal India

ISBN: (纸本)9798350383522

Vehicle number plate recognition ranks first in traffic management, especially in densely populated areas like India. This paper presents a conceptual approach that integrates state-of-the-art technologies to accurately identify vehicles using video license plate scanning.[1] Powered by a Raspberry Pi and a set of advanced algorithms, the system is ideal for securing confined spaces including educational institutions and high-security roads Using the power of OpenCV, the system catches on and runs video programs to extract sensitive data. This study digs deep into the difficult areas of acceptable behavior and photo appearance, with license plate recognition being a major focus. A notable feature is the integration of Optical Character Recognition (OCR) technology via a REST API, which provides the ability to quickly and accurately extract colors from license plates REST API continuous capture of license plates creates a plate image, which provides key information from these plates.[3]. Experimental findings confirm the capabilities of the system, showing recognition rates in standard with neural network-based strategies for number plate recognition The proposed model opens up possibilities for various applications, from regulatory requirements to checking forged vehicle registrations, extending to activity-monitoring, and encouraging electronic tax collection In together with OCR and REST API integration, this review shows a remarkable walk away progressive system detection vehicle. The convergence assures increased accuracy and skill in number plate recognition, and helps the system for safer, smarter and safer transportation systems. © 2024 IEEE.

关键词： Advanced image processing Automated Number Plate Recognition Efficient Vehicle Identification License Plate Recognition OCR Technology Optical Text Recognition REST API Integration Vehicle Detection System

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Real Time Cheating Detection Pipeline for Online Exams 30

Real Time Cheating Detection Pipeline for Online Exams

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30th IEEE Signal processing and Communications Applications conference (SIU)

作者： Dadak, Ahmet Uyan, Ufuk Ozturk, Mahiye Uluyagmur Huawei Turkey R&D Ctr Istanbul Turkey

ISBN: (纸本)9781665450928

Remote exams and job interviews have gained popularity due to pandemics and the advantage of remote working conditions. Most companies and institutions use online platforms for recruitment and examination. However, one of the critical problems of remote exam systems is that the exams cannot be held in a reliable environment. In this work, we present a cheating detection system for online exams. This system provides real-time detection of suspicious events such as another person, electronic device usage, candidate absent status by accessing the microphone and camera stream of the candidate's device through the browser. The cheating detection system can detect other people than candidates, electronic device usage, voice activity, and candidate absence status using state-of-the-art deep learning algorithms. When a suspicious event is detected, its moments are recorded as a video. The advantages of the real time cheating detection pipeline are as follows: 1) The system provides real time suspicious activity detection and produce instant alarm for proctors, 2) the system does not require an external program installation, 3) the number of user the system will serve at the same time has become scalable since it is run through the browser.

关键词： Cheating detection pipeline Face recognition Object detection image processing Voice processing

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