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

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Real Time image processing for Autonomous Vehicles

Real Time Image Processing for Autonomous Vehicles

引用

Intelligent Technologies (ACOIT), Asian conference on

作者： Mhamane Sanjeev Chandrashekhar Bhagyashri Amol Bachute Amruta K. Gadgoli Dinesh Dattatraya Rankhamb Shrinivas Madri Department of Electronics and Telecommunication Engineering Shree Siddheshwar Women’s College of Engineering Solapur Maharashtra India Department of Electronics & Telecommunication Engineering MIT College of Railway Engineering & Research Solapur Maharashtra India Department of Artificial Intelligence & Data Science VVP Institute of Technology Solapur Maharashtra India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Autonomous vehicles require real-time image processing to improve their capabilities by allowing them to understand and respond appropriately to their environment. This paper examines the present state of real-time image processing for self-driving vehicles, including the techniques employed, challenges, and advancements. This article investigates methods such as semantic segmentation, object recognition, categorization, and depth estimation, with an emphasis on enhancing vehicle perception, navigation, and decision-making. The article delves into the implementations of significant algorithms on embedded platforms, their computational efficiency, and their deployment in real-world scenarios. In conclusion, the report investigates prospective avenues for additional research to improve the reliability and efficiency of the real-time image processing systems of autonomous automobiles.

关键词： Embedded systems Navigation image processing Semantic segmentation Decision making Object detection Real-time systems Computational efficiency Reliability Object recognition

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Implementation of Farmguard with Automated Animal Detection and Monitoring System using IoT

Implementation of Farmguard with Automated Animal Detection ...

引用

International conference on Science Technology Engineering and Management (ICONSTEM)

作者： S Sujitha Harshika Vinoth Kumar K Hemavathi V Disha M Aamna Nafiza Department of EEE New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The agricultural landscape is evolving, demanding innovative solutions to enhance productivity while ensuring the welfare of livestock. Farmguard introduces an advanced Automated Animal Detection and Monitoring System designed to revolutionize traditional farm management practices. Leveraging cutting-edge sensor technology, computer vision, and machine learning algorithms, Farmguard offers real-time, non-invasive monitoring of animal behavior, health, and movement within farm premises. This system operates seamlessly, utilizing a network of strategically placed sensors and cameras to track and identify individual animals. Through sophisticated image processing and AI-powered algorithms. This technology aims to minimize conflicts by providing early warnings about animal intrusion, enabling timely intervention strategies. By leveraging Arduino's capabilities and image processing techniques.

关键词： Technological innovation Machine learning algorithms Tracking image processing Wildlife Real-time systems Agriculture

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image Stitching Evaluation by Object Detection and Geometric Transformation in Retail

Image Stitching Evaluation by Object Detection and Geometric...

引用

IEEE Signal processing and Communications Applications (SIU)

作者： Nermin Uzay Onur Aydemir Azmi C. Özgen Mehmet Kayaoğlu Server Calap Lütfü Çakıl REM People İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Serial shelf images are frequently used in product availability research in the retail industry. However, in order to accurately count the products and determine the shelf arrangement in these serial images, these images must be stitched with image processing algorithms. Appropriate metrics are also needed to measure the quantitative quality of the stitched images. In this study, we developed a new method to measure the performance of algorithms used to stitch multiple images that have intersections. In this method, object detection was performed separately on individual images and on the stitched image. image quality is calculated by looking at the overlap of the projection coordinates of the objects detected in the individual images and the stitched image. For this purpose, we prepared a dataset 1 consisting of retail shelf scenes and tested our proposed method by comparing state-of-the-art stitched algorithms on this dataset. As a result of the tests, we showed numerically that deep learning-based methods achieve higher performance than classical methods. 1 https://***/rempeople/remretail100

关键词： Measurement Learning systems Industries image quality Signal processing algorithms Object detection Signal processing

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Advanced Intelligent systems for Optimizing Breast Cancer Thermography

Advanced Intelligent Systems for Optimizing Breast Cancer Th...

引用

Silchar Subsection conference (SILCON), IEEE

作者： T. Deepa S. Maragathasundari M. Kameswari Josephine Selle Jeyannathan Department of Mathematics Saveetha school of Engineering Saveetha Institute of medical and technical sciences Chennai India Department of Mathematics Kalasalingam academy of research and education Krishnankoil Virudhunagar India Department of Electronics and communication Engineering Kalasalingam academy of research and education Krishnankoil Virudhunagar India

ISBN: (数字)9798331540821

ISBN: (纸本)9798331540838

The efficiency of diagnostic processes is paramount in healthcare, particularly for breast cancer detection and treatment. This research explores the queuing dynamics within the breast cancer imaging process, emphasizing the role of advanced image processing techniques alongside computing, communication, and intelligent systems to streamline patient flow and reduce wait times. By employing sophisticated image processing algorithms and intelligent queuing models, we scrutinize various strategies through data obtained from a premier medical facility. Our methodology integrates statistical analysis and simulation techniques to evaluate the effectiveness of different queuing approaches on operational efficiency and patient outcomes. Results indicate that intelligent image processing systems significantly enhance service efficiency, alleviate patient anxiety, and elevate overall healthcare delivery.

关键词： Statistical analysis image processing Infrared imaging Medical services Predictive models Streaming media Breast cancer Real-time systems Intelligent systems Queueing analysis

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Real-time Fire Detection using image and Video processing

Real-time Fire Detection using Image and Video Processing

引用

Electrical Electronics and Computing Technologies (ICEECT), International conference on

作者： Rhythm Shah Ruchi Gajjar Electronics and Communication Engineering Department Institute of Technology Nirma University Ahmedabad Gujarat India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

There has been a rise in the frequency of fire-related calamities all over the globe, which leads to the need for an efficient fire detection system to avoid high losses or fatalities. This paper focuses on real-time fire detection techniques through image and video processing. In particular, this paper is aimed at a color detection approach that uses HSV and YCbCr color models for detecting only fire pixels along with the implementation of fire movement detection approach by comparing consecutive frames from the live feed. Overall, the study contributes to advancing fire detection methodology, highlighting the potential of image processing methods in real-time fire detection systems.

关键词： image segmentation image color analysis Motion segmentation Disasters Streaming media Filtering algorithms Real-time systems Motion detection Delays Feeds

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Detection and Classification of COVID-19 Chest X-rays by the Deep Learning Technique 14

Detection and Classification of COVID-19 Chest X-rays by the...

引用

14th Biomedical Engineering International conference (BMEiCON)

作者： Sonarra, Wannika Vongmanee, Naphatsawan Wanluk, Nutthanan Pintavirooj, Chuchat Visitsattapongse, Sarinporn King Mongkuts Inst Technol Ladkrabang KMITL Sch Engn Dept Biomed Engn Bangkok 10520 Thailand

ISBN: (纸本)9781665489034

The Coronavirus disease (COVID-19) infection has become a pandemic, and this is the most critical problem that has occurred in Thailand and also expanded all over the world. As such, it is not astonishing to know that this virus has had a direct effect on hospitals with the delayed screening of patients because of the increasing number of daily cases and the shortage of medical personnel and restricted treatment space. Due to such restrictions, in this study, we used a clinical decision-making system with predictive algorithms. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. Moreover, image classification is one interesting aspect of image processing. Convolutional neural network (CNN) is a widely used algorithm for image classification by separating the images of the COVID-19 disease, images with a lung infection, and normal images. To evaluate the predictive performance of our models, precision, F1-score, recall, receiver operating characteristic (ROC) curve (area under the ROC curve), and accuracy scores were used. It was observed that the predictive models trained on the laboratory findings could be used to predict the COVID-19 infection as well and could be helpful for medical experts to appropriately prioritize the resources. This could be employed to assist medical experts in validating their initial laboratory findings and could also be used for clinical prediction studies.

关键词： Convolutional neural network (CNN) image of COVID-19 coronavirus deep learning

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Deep Learning-based Serialized Model and Feature Extraction Algorithm for Solid Oxide Fuel Cell State Prediction 43

Deep Learning-based Serialized Model and Feature Extraction ...

引用

43rd Chinese Control conference, CCC 2024

作者： Wu, Jiajian Zhu, Kanglin Wu, Han Xiang, Guohui Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen China

ISBN: (纸本)9789887581581

Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC) prediction. However, the irrationality of the prediction object and the lack of prediction accuracy have hindered the progress of the prediction methods for SOFC systems. Therefore, two algorithms are proposed in this study, a feature selection algorithm based on maximum information coefficient and Bayesian information criterion for identifying key variables that are sensitive to changes in the SOFC system, and a prediction model based on a genetic algorithm optimized for sequence to sequence architecture. The M/BIC algorithm reduces the number of features by 62.5% (from 40 to 15), which contributes to the acceleration of the algorithm training and the subsequent sensor layout optimization. The prediction model incorporates a two-dimensional convolutional neural network and a multi-head attention mechanism to capture more information in the input sequence, an encoder-decoder architecture and a Gated recurrent unit to better predict performance trends. Tests with real experimental data show significant changes in feature dimensionality reduction and prediction accuracy. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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Oscillatory Neural Network as Hetero-Associative Memory for image Edge Detection 2022

Oscillatory Neural Network as Hetero-Associative Memory for ...

引用

Annual Neuro-Inspired Computational Elements conference (NICE)

作者： Abernot, Madeleine Gil, Thierry Todri-Sanial, Aida Univ Montpellier CNRS LIRMM Montpellier France

ISBN: (纸本)9781450395595

The increasing amount of data to be processed on edge devices, such as cameras, has motivated Artificial Intelligence (AI) integration at the edge. Typical image processing methods performed at the edge, such as feature extraction or edge detection, use convolutional filters that are energy, computation, and memory hungry algorithms. But edge devices and cameras have scarce computational resources, bandwidth, and power and are limited due to privacy constraints to send data over to the cloud. Thus, there is a need to process image data at the edge. Over the years, this need has incited a lot of interest in implementing neuromorphic computing at the edge. Neuromorphic systems aim to emulate the biological neural functions to achieve energy-efficient computing. Recently, Oscillatory Neural Networks (ONNs) present a novel brain-inspired computing approach by emulating brain oscillations to perform auto-associative memory types of applications. To speed up image edge detection and reduce its power consumption, we perform an in-depth investigation with ONNs. We propose a novel image processing method by using ONNs as a Heterogeneous Associative Memory (HAM) for image edge detection. We simulate our ONN-HAM solution using first, a Matlab emulator, and then a fully digital ONN design. We show results on gray scale square evaluation maps, also on black and white and gray scale 28x28 MNIST images and finally on black and white 512x512 standard test images. We compare our solution with standard edge detection filters such as Sobel and Canny. Finally, using the fully digital design simulation results, we report on timing and resource characteristics, and evaluate its feasibility for real-time image processing applications. Our digital ONN-HAM solution can process images with up to 120x120 pixels (166 MHz system frequency) respecting real-time camera constraints. This work is the first to explore ONNs as hetero-associative memory for image processing applications.

关键词： Edge detection Hetero-Associative Memory Neuromorphic Computing Oscillatory Neural Networks

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Conditional Diffusion Process for Inverse Halftoning 36

Conditional Diffusion Process for Inverse Halftoning

引用

36th conference on Neural Information processing systems (NeurIPS)

作者： Jiang, Hao Mu, Yadong Peking Univ Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781713871088

Inverse halftoning is a technique used to recover realistic images from ancient prints (e.g., photographs, newspapers, books). The rise of deep learning has led to the gradual incorporation of neural network designs into inverse halftoning methods. Most of existing inverse halftoning approaches adopt the U-net architecture, which uses an encoder to encode halftone prints, followed by a decoder for image reconstruction. However, the mainstream supervised learning paradigm with element-wise regression commonly adopted in U-net based methods has poor generalization ability in practical applications. Specifically, when there is a large gap between the dithering patterns of the training and testing halftones, the reconstructed continuous-tone images have obvious artifacts. This is an important issue in practical applications, since the algorithms for generating halftones are ever-evolving. Even for the same algorithm, different parameter choices will result in different halftone dithering patterns. In this paper, we propose the first generative halftoning method in the literature, which regards the black pixels in halftones as physically moving particles, and makes the randomly distributed particles move under some certain guidance through reverse diffusion process, so as to obtain desired halftone patterns. In particular, we propose a Conditional Diffusion model for image Halftoning (CDH), which consists of a halftone dithering process and an inverse halftoning process. By changing the initial state of the diffusion model, our method can generate visually plausible halftones with different dithering patterns under the condition of image gray level and Laplacian prior. To avoid introducing redundant patterns and undesired artifacts, we propose a meta-halftone guided network to incorporate blue noise guidance in the diffusion process. In this way, halftone images subject to more diverse distributions are fed into the inverse halftoning model, which helps the model to lear

关键词： image reconstruction

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Pioneering Early Parkinsons Disease Detection with Deep Learning and image processing Innovations 10

Pioneering Early Parkinsons Disease Detection with Deep Lear...

引用

10th International conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Srinivasarao, Narisetty Khan, Mohammad Basith Anwar Haneesha, Pullipati Meghana, Palaparthi Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350384369

Parkinson's disease, a neurological disorder which affects the nervous system, manifests as unintentional and uncontrollable movements in the body. With over 6 million individuals globally affected, early detection of PD proves challenging, given its varied symptoms. The manifestations of the disease can differ among individuals, with symptoms typically initiating as a tremor in one hand and progressively impacting the entire body over time. Typically, symptoms begin with a hand tremor and progress throughout the body. Currently, conventional clinical approaches do not possess the capability to detect Parkinson's disease during its initial phases. Diagnosis relies on medical history, brain MRI images, and manual symptom observation, a process that is time-consuming and often insufficient for early detection. The potential of deep learning for early PD detection is explored using datasets comprising spiral drawings from both normal and PD-affected individuals. CNN and ResNet50 algorithms are applied to spiral drawings, enhancing the model's ability to capture intricate patterns. The training process utilizes 60% of the dataset, with the remaining 40% reserved for testing. In the case of spiral drawings, the ResNet50 algorithm proves instrumental. It transforms spiral drawings into pixels, facilitating classification. During testing, the pixels of current drawings are compared with previously trained CNN and ResNet50 models to identify the disease. The amalgamation of results from ResNet50-enhances spiral drawing datasets significantly and improves disease detection accuracy more than CNN. Individual's data can be given as input into the system for efficient disease detection. © 2024 IEEE.

关键词： Ability testing

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