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

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Application of Lossless Compression Method in image Compression

Application of Lossless Compression Method in Image Compress...

引用

International conference on Information systems and Computer Aided Education (ICISCAE)

作者： Weifeng Ni Tianqiu Pu Yuxi Wang College of Information Science and Engineering Hohai University Changzhou China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

This paper is based on the application of lossless compression algorithms in image compression, aiming to solve problems such as insufficient storage space, low transmission efficiency, and heavy data processing burden. In order to optimize the image lossless compression algorithm, a combination of Huffman encoding and LZW compression algorithm is proposed. The implementation process and advantages and disadvantages of the two are compared, and the compression ratio and encoding efficiency of image lossless compression technology are discussed. We need to optimize decoding speed, image quality, and other aspects to improve overall image lossless compression capability. By applying Huffman encoding and LZW compression algorithms to images separately, the results show that Huffman encoding can dynamically construct the optimal encoding based on the frequency of characters appearing in image data, while LZW technology is good at utilizing repeated patterns and contextual information in image data to further improve compression efficiency by establishing dictionaries. The complementary advantages of the two can further improve the lossless compression ability of images.

关键词： image quality Deep learning image coding Dictionaries Propagation losses Decoding image storage Huffman coding Compression algorithms Information systems

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Ayurvedic Medicinal Plant Identification System Using Embedded image processing Techniques 1

引用

4th International conference on Communication, Computing and Electronics systems, ICCCES 2022

作者： Das, Arnab Siva Sai Kumar, B. Shiva Shankar Reddy, S. Naveen Reddy, S. Peeyush, K.P. Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9789811977534

ISBN: (纸本)9789811977527

Plants can be classified based on various classification methods such as cell, genetic and serum etc. It's difficult for an individual to explore the various classification methods and it's practically not feasible as it demands good knowledge in plant taxonomy and long-term time investment. Due to the shortage of experienced and qualified taxonomists in identification and classification of medicinal plants, with the help of different image processing algorithms and computer vision, the above difference can be bridged. The main objective is to develop a Deep Learning and Machine Learning based model to identify and classify plants based on various features, which is done with the help of Gabor filter and Gray Level Co-occurrence Matrices (GLCM) and using classifiers such as Random Forest (RF), and Light Gradient Boosting Machine (LGBM), and made a comparative analysis which resulted an accuracy of 95.5% with LGBM and GLCM filter and used to develop a standalone device that clicks a picture and identifies the medicinal plant. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Gabor filters

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RecOpt: A Recommender-Based Optimization Framework for Enhanced Multi-Objective Decision-Making in Autonomous Driving

RecOpt: A Recommender-Based Optimization Framework for Enhan...

引用

image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Sihan Chen Zheng Lan Xiupeng Shi School of Microelectronics Shanghai University China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Autonomous driving systems operate in highly complex environments, necessitating real-time, balanced decisionmaking across multiple objectives such as safety, efficiency, and passenger comfort. This study introduces RecOpt, a novel framework that combines recommender algorithms with advanced optimization techniques to enhance multi-objective optimization (MOO) in decision-making. RecOpt tackles the challenges of large-scale mixed-integer MOO by identifying and restructuring key decision variables, thereby reducing the optimization scale and increasing efficiency. By integrating recall and finesorting technologies inherent in recommender systems, RecOpt efficiently screens and selects optimal solutions, surpassing the speed and scalability limitations of traditional MOO methods. Experiments utilizing the NGSIM dataset validate RecOpt’s efficacy in balancing safety, comfort, and efficiency, minimizing decision-making latency, and adapting to dynamic environments. Furthermore, RecOpt offers data-driven insights into explainable decision-making, providing a robust and transparent model for autonomous vehicle decision processes.

关键词： Machine learning algorithms Scalability image processing Decision making Real-time systems Safety Vehicle dynamics Optimization Autonomous vehicles Recommender systems

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AI-Enable Machine Learning for Lung Cancer Prediction and Classification 1

AI-Enable Machine Learning for Lung Cancer Prediction and Cl...

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1st International conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Singh, Kamred Udham Kumar, Ankit Sinha, Anurag Kumar, Gaurav Singh, Teekam Pandey, Saroj Kumar School of Computing Graphic Era Hill University Dehradun India Gla University Department of Computer Engineering & Apllications UP Mathura India Amity University Department of Computer Science Ranchi India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330915

In comparison to other types of cancer, lung cancer has the highest fatality rate, making it one of the most dangerous illnesses in the world. In India, there are over 70,000 new cases recorded each year, which demonstrates a startlingly rapid growth in the frequency of the disease. Lung cancer's sneaky nature makes early detection difficult. Early lung cancer identification improves survival rates, making it a top priority. Technology is crucial for early detection since it is more reliable. Many scholars have proposed innovative concepts after extensive study. Computer-Aided Diagnosis (CAD) systems, which use computers to diagnose, are a major advancement in this field. These cutting-edge methods employ machine learning, deep learning, and image processing to predict malignant lung cancer risk. This article evaluates and compares technologies for early lung cancer detection and diagnosis. This category includes image segmentation, feature extraction, and classification algorithms. This essay examines these current technological options to help fight this life-threatening sickness. The article's ultimate goal is to improve the outlook for those who are at risk of acquiring lung cancer due to the sickness. © 2023 IEEE.

关键词： CAD Classification CT Scan Lung Cancer MRI PET Pre-processing ReLU

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Real Time image processing for Autonomous Vehicles

Real Time Image Processing for Autonomous Vehicles

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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...

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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

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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...

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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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