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检索条件"任意字段=Image Processing: Algorithms and Systems V"

共 2735 条记录，以下是481-490 订阅

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Connected Component Labeling algorithm in streaming image processing with FPGAs

Connected Component Labeling algorithm in streaming image pr...

引用

SPIE Future Sensing Technologies Conference

作者： Bakumenko, A. Bakhchevnikov, v Derkachev, v Kovalev, A. Lobach, v Potipak, M. Southern Fed Univ Engn Ctr Instrument Making Radio & Microelect 81 Petrovskaya Str Taganrog Rostov Region Russia Southern Fed Univ Dept Radio Engn & Telecommun Syst 44 Nekrasovsky Lane Taganrog Rostov Region Russia

ISBN: (数字)9781510646926

ISBN: (纸本)9781510646926;9781510646919

The task of image fragments extraction with objects of interest is not new. The choice of appropriate segmentation algorithm and its implementation in real time mode become a problem in systems operating with a high-speed video data stream. Connected components labeling is an important step in training samples images preparation for usage in neural networks. To ensure high speed and feasibility of image labeling algorithm for the FPGA, it is necessary to justify the choice of segmentation algorithms, considering the hardware capabilities of the platform. In this paper, we propose a modified one-pass image labeling algorithm for FPGAs, as well as its implementation using the Xilinx System Generator for DSP and the Matlab/Simulink package. As a hardware platform the FMC-200-A mezzanine is used to provide high-speed video data stream from the line camera TELEDYNE DALSA LA-CC-04K05B-00-R to the ZYNQ Ultrascale + MPSoC ZCU106 evaluation kit board. The procedure of using hardware implemented SPI interface on the MPSoC ZCU106 board, which is to configure and control the FMC200-A module is described. Implementation of SPI interface is made by using vivado and vitis IDE. The labeling results of proposed algorithm on test images, as well as images obtained experimentally are presented.

关键词： Connected Components image Segmentation FPGA Streamed processing

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A Modified Echo State Network for Time Independent image Classification

A Modified Echo State Network for Time Independent Image Cla...

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IEEE International Midwest Symposium on Circuits and systems (MWSCAS)

作者： Gardner, Steven D. Haider, Mohammad R. Moradi, Lee vantsevich, vladimir Univ Alabama Birmingham Sch Engn Birmingham AL 35294 USA

ISBN: (纸本)9781665424615

image classification is typically performed with highly trained feed-forward machine learning algorithms like deep neural networks and support vector machines. The image can be treated as a time-series input when applied to the network multiple times, opening the way for recurrent neural networks to perform tasks like image classification, semantic segmentation and auto-encoding. With this approach, ultra-fast training, network optimization, and short-term memory effects allows for dynamic, low-volume datasets to be quickly learned without heavy image pre-processing or feature extraction;the main limitation being that input images need labeled output images for training, as is also true of most standard approaches. In this work, the MNIST handwritten digit dataset is used as a benchmark to evaluate metrics of a modified Echo State Network for static image classification. The image array is passed through a noise filter multiple times as the Echo State Network converges to a classification. This highly dynamic approach easily adapts to sequential image (video) tasks like object tracking and is effective with small datasets. Classification rates reach 95.3% with sample size of 10000 handwritten digits and training time of approximately 5 minutes. Progression of this research enables discrete image and time-series classification under a single algorithm, with low computing power and memory requirements.

关键词： reservoir computing Echo State Network image classification MATLAB

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Smart and Quick Parking Spot Detection

Smart and Quick Parking Spot Detection

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Santhosh Kumar veeramalla Rama Lakshmi Gali M Ranjeeth Saikumar Tara v Hindumathi L Bhargava Kumar Department of ECE BVRIT HYDERABAD College of Engineering for Women Hyderabad India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The rapid expansion of urban areas has intensified the challenge of finding parking spaces for drivers. Intelligent parking systems emerge as a crucial solution by providing real-time detection of available spaces. While various methods exist, from basic magnetic sensors to advanced computer vision algorithms, their effectiveness can be hindered by environmental factors. Recognizing the importance of automated systems, this paper focuses on implementing an image processing approach for parking space detection. By strategically placing cameras in parking lots, the system identifies vacant spaces and transforms images to determine availability. Integrating these techniques into mobile applications could further aid drivers in locating parking spots, improving their overall experience. Ultimately, accurate and efficient parking space identification is vital for reducing congestion, enhancing air quality, and improving urban environments.

关键词： Wireless sensor networks Transmitters Urban areas Transforms Streaming media Cameras User experience Real-time systems Automobiles vehicles

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Detection of Traffic Congestion from Surveillance videos using Machine Learning Techniques 6

Detection of Traffic Congestion from Surveillance Videos usi...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Govinda Rao, S. Rambabu, R. Anil Kumar, B.S. Srinivas, v. varaprasada Rao, P. Griet Department of Cse Hyderabad India Riet Department of Cse Rajahmundry India Cse Griet Hyderabad India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Smart Cities applications, automated traffic control and management is the most trending research area. With the improving needs of developed towns and cities traffic congestion, now a days this the traffic congestion control and its applications has large needed facing problem in the increased population cities. Peeled eye camera photos and videos can be watched efficiently to detect traffic congestions in most of the areas in the grown populated cities. The earlier researchers had observed more on traffic signal controls through photos executed by using different algorithms of machine learning. There is existing research available on traffic signal controls through image processing and various machine learning methods. The image features are extracted and interpreted for the same. Deep learning algorithm, convolutional neural network (CNN) is proposed for effective detection of traffic congestion. There were existing works available in traffic detection using machine learning and deep learning approaches. Machine learning, Nowadays, traffic surveillance systems collect a lot of videos or images and store them for the live monitoring purposes. Deep learning techniques are used sparingly in traffic surveillance and control systems. various images with various weather conditions are collected and are used as training dataset. Advantages of deep learning have been exploited in many applications, which use computer vision and image analysis. One of such applications is traffic monitoring, in which large amounts of video or images are processed for effective learning. The traffic surveillance can only monitor, which cannot detect the traffic on particular time. © 2022 IEEE.

关键词： Traffic congestion

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Development of an Expert System for Advanced Podiatric Treatments and Ergonomics

Development of an Expert System for Advanced Podiatric Treat...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： P vigneshwaran v Buvanesh Pandian M Radha B Mohan R Santhana Krishnan J Relin Francis Raj Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

An expert system tailored for foot pressure analysis and imprint detection, with a focus on enhancing podiatric treatments and ergonomics. The system comprises three key components: a foot pressure analyser, an image processing-based foot imprint analyser, and a machine learning-driven data analyser. The foot pressure analyser employs an array of FSR sensors with piezo resistive material velostat, accommodating various foot types. The collected data is processed using thermal image algorithms to create foot pressure images. The image processing unit of the system utilizes edge detection techniques to extract relevant features from the foot pressure images, forming a dataset for machine learning. A neural network is trained using this dataset to identify lower limb abnormalities accurately. The expert system encompasses an extensive knowledge base, storing foot anatomy, pressure point norms, common foot conditions, footwear information, and also analyses lower limb extremities like arthritis and synovial accumulations etc., especially helps in geriatric care. The system generates comprehensive reports summarizing the analysis results, facilitating treatment planning for specific applications, age groups, genders, and foot shapes and giving real-time feedback and recommendations based on the Expert’s knowledge to aid in treatment planning, ensuring accessibility and data security. Future developments include 3D printing integration, research support, and machine learning enhancements. The system promises to advance foot health diagnostics and treatments significantly, benefiting healthcare professionals and patients.

关键词： Limbs Shape Medical services Machine learning Footwear Three-dimensional printing Planning Expert systems Foot Sensor arrays

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Localization of Dengue virus Likelihood Using image processing Technique

Localization of Dengue Virus Likelihood Using Image Processi...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Md. Mahbub Hosen Md. Rahatul Islam Faria Tabassum Syeda Thamina Tonni Malik Mohammad Nafiur Noor Shahid Rahman Dept. of Computer Science & Engineering Northern University of Bangladesh Dhaka Bangladesh Dept. of Human Intelligence Systems Kyushu Institute of Technology Wakamatsu Kitakyushu Japan Dept. of Computer Science & Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Human Intelligence Lab Bangladesh Advance Robotics Research Center Dhaka Bangladesh Dept. of Electrical & Electronic Engineering Canadian University of Bangladesh Dhaka Bangladesh

The Aedes mosquito, found in tropical areas, transmits, and causes dengue fever. The spread of the dengue fever virus from an infected Aedes mosquito bite typically takes between three and fifteen days to manifest itself clinically as dengue fever symptoms. Early and automated detection of this illness will reduce the occurrence of outbreaks and facilitate the digitization of the system. Our primary objective is to identify regions at high risk for dengue fever using limited image data through the utilization of sophisticated machine learning and image processing methods. This work showcases the application of transfer learning algorithms for the timely identification of regions at risk for dengue mosquito infestation, particularly in situations where obtaining early data on dengue outbreaks is challenging. The experiments included images of 2150 dengue-positive data sets and 1440 dengue-negative data sets. We used AI and image classifiers Yolo v4 Tin, Tensor Flow, Google Colab, etc. for the training data set, and the system makes predictions with a 99.63% accuracy rate. Our system has some unique attributes; it not only detects the percentage of dengue probability but also the specific dengue-prone location on the submitted image.

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Design and Implementation of a Tilapia (Oreochromis niloticus) Fingerling Counter Using Otsu Thresholding and Morphological Transformation Techniques

Design and Implementation of a Tilapia (Oreochromis niloticu...

引用

International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

作者： Tom Jairus v. Portea Maricris I. De La Fuente Mae Z. Medrano Farah Faye P. Felices Ian Jake A. Naldo Rizza T. Loquias Keith Marlon R. Tabal College of Engineering & Architecture Camarines Sur Polytechnic Colleges Camarines Sur Philippines

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Many different image processing techniques have been developed and implemented in counting systems for livestock and fisheries applications. These existing systems often require sophisticated hardware and computational resources, leading to an increase in the financial costs associated with their production. In this paper, an innovative Tilapia (Oreochromis niloticus) fingerling counting system is described. The system is comprised of low-cost computer vision-based hardware and software that integrates Otsu thresholding and morphological transformation techniques. These techniques derive contours from segmented regions generated by the formulated algorithms. It uses a Raspberry Pi 4B single-board computer platform in conjunction with a Raspberry Pi Camera Module version 1.3 to capture images. The performance of the manual process and the proposed method were evaluated by determining its mean squared error using a ground truth as a reference. Based on the tests performed, it was revealed that the proposed automatic counting system resulted in a better performance than the manual counting method.

关键词： Performance evaluation Thresholding (Imaging) Accuracy Software algorithms Prototypes Manuals Production Hardware Software Python

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Transform-Based Quality Assessment for Enhanced image 44

Transform-Based Quality Assessment for Enhanced Image

引用

Conference Applications of Digital image processing XLIv

作者： voronin, v. Semenishchev, E. Zelensky, A. Tokareva, O. Cenc, Y. Agaian, S. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Lab Math Methods Image Proc & Intelligent Comp Vi Rostov Na Donu Russia Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China CUNY Dept Comp Sci New York NY 10021 USA

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

images captured in bad weather suffer from low contrast and faint color. Recently, plenty of enhanced algorithms have been proposed to improve visibility and restore color. The goal of image quality assessment is to predict the perceptual quality for improving imaging systems' performance. We proposed a no-reference image quality enhancement measure using hypercomplex Fourier transform for color images. The main idea is that enhancing the contrast of an image would create more high-frequency content in the enhanced image than the original image. An increase in the magnitude of higher frequency coefficients indicates an enhancement in contrast to the image's luminance content. To test the performance of the proposed algorithm, the public database TID2013 is used. The Pearson rank-ordered correlation coefficient is utilized to measure and compare the proposed quality measure's performance with state-of-the-art approaches.

关键词： quality measure frequency-domain transform no-reference (NR) image quality enhanced image

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MedSeg: A Medical image Analyzer

MedSeg: A Medical Image Analyzer

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Control Instrumentation System Conference (CISCON)

作者： Shlok Mehta Keyur Pancholi Sakshi Pandey Sudhir Dhage Computer Engineering Department Sardar Patel Institute of Technology Mumbai India

ISBN: (数字)9798350375480

ISBN: (纸本)9798350375497

This research paper aims to address the critical need for efficient and accurate identification of chest diseases using chest X-rays through a combination of advanced image processing techniques and machine learning algorithms. With the growing prevalence of respiratory and cardiovascular conditions worldwide, timely and precise diagnosis is paramount for effective patient care. The study begins with a comprehensive review of existing methodologies and technologies employed in the identification of chest diseases from X-ray images. It critically evaluates the strengths and limitations of current approaches, highlighting the challenges faced in achieving high accuracy, speed, and scalability. To address these issues, the project aims to develop an AI-powered system for medical image analysis. In response to these challenges, our research proposes a novel approach that integrates Inception v3 model and imagenet. We leverage a large dataset of annotated chest X-rays to train a deep neural network capable of recognizing subtle patterns indicative of various diseases, including pneumonia, pneumothorax, lung and cardiac abnormalities. The model is optimized to provide not only accurate diagnoses but also to minimize false positives and negatives. In conclusion, this research contributes to the ongoing efforts in utilizing chest X-ray images for disease identification, presenting a robust and efficient methodology that could revolutionize the current diagnostic landscape. The findings hold promise for the development of automated systems capable of assisting healthcare professionals in the accurate and timely detection of chest diseases, ultimately contributing to enhanced patient care and management.

关键词： Accuracy Pneumonia Machine learning algorithms Reviews Scalability Medical services Pattern recognition X-ray imaging Medical diagnostic imaging Diseases

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Electronic Aid Design of Fruits image Classification for visually Impaired People 4th

Electronic Aid Design of Fruits Image Classification for Vis...

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4th International Conference on Information and Communication Technology for Intelligent systems, ICTIS 2020

作者： Srividhya, v. Sujatha, K. Aruna, M. Sangeetha, D. EEE Department Dr. MGR Educational and Research Institute Chennai India EEE Department Meenakshi College of Engineering Chennai India

ISBN: (纸本)9789811570612

Deep learning has attained immense achievement in many fields, such as computer vision and usual speech processing in recent years. It has a burly erudition capability and can create enhanced use of datasets for characteristic extraction when compared to traditional machine learning systems. The researchers start doing research work in this deep learning due to its popularity. In this manuscript, we chiefly initiate several superior neural networks of deep learning and their functions. Deep learning technologies are becoming the major approaches for natural signal and information processing, like image classification, speech recognition. Deep learning is a technology inspired by the functioning of human brain. Convolutional neural networks (CNN) become very popular for image classification in deep learning. CNN’s perform better than human subjects on many of the image classification datasets. In this project, a deep learning convolutional network based on Tensorflow is deployed using python for binary image classification. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning algorithms

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