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

共 16439 条记录，以下是1591-1600 订阅

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Crops Classification Using Machine Learning And Google Earth Engine 14

Crops Classification Using Machine Learning And Google Earth...

引用

14th International conference on Intelligent systems: Theories and Applications, SITA 2023

作者： Achahboun, Chakir Chikhaoui, Mohamed Naimi, Mustapha Bellafkih, Mostafa Iav Hassan 2 Ressources Naturelles et Environnement Rabat Morocco Institut National des Postes et Télécommunications Rabat Morocco

ISBN: (纸本)9798350308211

The primary objective of this study was to address the critical challenge of obtaining accurate information regarding the spatial distribution and classification of crops in agricultural areas. The aim was to enhance agricultural decision-making and management, especially in regions with limited water resources, where crop productivity and sustainability are crucial for achieving food security and sustainable development. To achieve this objective, this study utilized machine-learning classification algorithms in conjunction with Landsat and Sentinel satellite imagery. The classification of different crops was based on Normalized Difference Vegetation Index (NDVI) phenology. The classification process and post-processing were conducted using the Google Earth Engine (GEE) platform , as well as utilizing Python, and scikit-learn library. Ground-truth data provided by local experts, along with the EUROMAP 2018 dataset in South Spain, were used to label the classification results. The findings of this study demonstrated a classification accuracy of 72% for certain crop types, indicating significant implications for sustainable agricultural practices and land use planning. © 2023 IEEE.

关键词： image classification

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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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Joint Visual and Semantic Information image Retrieval

Joint Visual and Semantic Information Image Retrieval

引用

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

作者： Ziao Qin xinyue Hu Yijia Chen Qingyun Xu Zhenting Li Northwestern Polytechnical University Xi'an China Oxford Brookes University Brookes UK Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In image retrieval, there exists a "semantic gap" problem, which refers to the discrepancy between the image semantics understood by computers through extracted image features and the image semantics perceived by humans. This paper studies an image retrieval algorithm based on image visual information and semantic information. The algorithm utilizes image features combined with hash encoding to describe the visual characteristics of images, and employs a word vector generation model to convert semantic words of images into word vectors, thereby describing the semantic features of the images. To better combine the similarities of visual features and semantic features, the algorithm establishes a reranking retrieval matrix for image searching. The paper validates the effectiveness of the proposed algorithm through experiments, demonstrating that the retrieved images highlighted by this algorithm are consistent in terms of semantic information while ensuring the similarity of visual features.

关键词： Visualization image coding Machine learning algorithms Semantics image retrieval Machine learning Feature extraction Vectors Matrix converters Sorting

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Stereo Matching Based on Collective Adaptive Symbiotic Genetic algorithms

Stereo Matching Based on Collective Adaptive Symbiotic Genet...

引用

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

作者： Shiyu Song Pinfan Hou Wei Zhang Yujiang Zhong School of Aeronautics Northwestern Polytechnical University Xi’an People’s Republic of China

Stereo matching algorithms are used to recovery the three-dimensional map through the left and right pictures from the binocular camera. The accurate matching of corresponding points between stereo image pairs is a fundamental step in stereo vision, but it remains a challenging task due to factors such as occlusions, noise, and image ambiguities. This paper proposes an accurate stereo matching algorithm based on collective adaptive symbiotic genetic algorithms. The fundamental idea behind using GA for stereo matching is to formulate the problem as an optimization task. To address this, we decompose complete images into numerous smaller image patches, each represented as distinct species or populations with phenotypes corresponding to different polynomial functionals. For each patch, the cost function contains both self and symbiotic terms with adaptive weights. By minimizing the cost function, GA propagates local interactions to a global scale and finds the global optimal polynomial functionals for each patch. Finally, by fusing all the small patches, we obtain the accurate disparity map.

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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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Construction and Characteristic Analysis of Two-Layer Complex Network Model

Construction and Characteristic Analysis of Two-Layer Comple...

引用

International conference on image, Vision and Intelligent systems, ICIVIS 2023

作者： Tang, Chunyang Ye, Zhonglin Zhao, Haixing xiao, Yuzhi Bai, Libing College of Computer Qinghai Normal University Xining810008 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810008 China Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province Xining810008 China Key Laboratory of Tibetan Information Processing Ministry of Education Xining810008 China

ISBN: (纸本)9789819708543

To characterize the homogeneity and heterogeneity between nodes or connecting edges, we considered the multi-relationship attributes of nodes and constructed six dual-layer network models. Firstly, based on node homogeneity, we constructed three homogeneous two-layer network models (GEE,R), (GWW,R) and (GBB,R) using the same generation algorithm for intra-layer connections and random connections for inter-layer connections. Secondly, based on node heterogeneity, we constructed three heterogeneous two-layer network models (GEW,R), (GBE,R) and (GBW,R) using different generation algorithms for intra-layer connections and random connections for inter-layer connections. Then, we derived the average degree analytical expressions and node degree distribution analytical expressions for the six two-layer networks. Finally, according to the differences in inter-layer connection probabilities, we depict the experiments with node degree distribution. Experiments have shown that the node degree distribution of (GEW,R) networks exhibits a bimodal characteristic, while the node degree distribution of (GBE,R) networks presents both Power-law and unimodal mixed characteristics. Overall, this study provides insights into the characteristics of different two-level network models and their application in modeling complex systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Complex networks

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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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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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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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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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