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

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Calculating Leaf Area Index Using Neural Network and WorldView 3 Multispectral imagery 59

Calculating Leaf Area Index Using Neural Network and WorldVi...

引用

59th International Scientific conference on Information, Communication and Energy systems and Technologies (ICEST)

作者： Polimenov, Ventsislav Ivanova, Krassimira Tsvetkova, Mihaela Anastasova, Elena Dimitrova, Katya Bulgarian Acad Sci Inst Math & Informat Sofia Bulgaria Bulgarian Acad Sci Risk Space Transfer Technol Transfer Off RST TTO Sofia Bulgaria

ISBN: (纸本)9798350386202;9798350386196

Leaf Area Index (LAI) holds significant importance as a specific characteristic of Leaf Areas in the field of smart agriculture. This study explores the estimation of LAI using a multi-spectral image from WorldView 3 satellite. The image combines 8 VNIR bands and has a spatial resolution of 1.24m. To overcome the limited amount of available data, the image was split into smaller subsets called paxels, resulting in 500 paxels for training and testing. For enhancing machine learning models. performance, the standardisation of a dataset is made, after that, a Multilayer Perceptron with a specific architecture aimed to predict LAI from the multiple bands is trained. The achieved results showed promising performance in LAI prediction. Overall, the study demonstrates the potential of using satellite imagery and machine learning algorithms to improve our understanding of crop health and productivity.

关键词： remote sensing machine learning image processing smart agriculture

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From manual to automated optical recognition of ancient coins

引用

13th International conference on Virtual systems and Multimedia, VSMM 2007

作者： Zaharieva, Maia Kampel, Martin Vondrovec, Klaus Vienna University of Technology Pattern Recognition and Image Processing Favoritenstr 9/1832 Vienna A-1040 Austria Museum of Fine Arts Department of Coin and Medals Burgring 5 Vienna A-1010 Austria

ISBN: (纸本)3540785655

Illegal trade and theft of coins appears to be a major part of the illegal antiques market. image based recognition of coins could substantially contribute to fight against it. Central component in the permanent identification and traceability of coins is the underlying classification and identification technology. However, currently available algorithms focus basically on the recognition of modern coins. To date, no optical recognition system for ancient coins has been researched successfully. In this paper, we give an overview over the challenges faced by optical recognition algorithms. Furthermore, we show that image based recognition can assist the manual process of coin classification and identification by restricting the range of possible coins of interest. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： image recognition

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Video Smoke Detection Algorithm Based on a Spatial-Temporal Neural Network Model 9

Video Smoke Detection Algorithm Based on a Spatial-Temporal ...

引用

9th International conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Cao, Zhen Zhang, xi Shenyang Fire Science and Technology Research Institute Ministry of Emergency Management Shengyang China

ISBN: (纸本)9798350376548

a kind of spatial-temporal neural network video smoke detection algorithm is proposed in order to solve the problems associated with the incorrect classification of the static approximate smoke background in the face of the detection of smoke in video detection networks, and the problem of false alarms and of the original test model algorithms being different in different detection environments. Based on the original YOLO v4 neural network algorithm, this paper introduces a k-means + + algorithm and genetic algorithm, while using the algorithm's clustering function to classify the sample points of the real boxes of the image data set, which make it a more suitable anchor. At the same time, the genetic algorithm is used to adjust its anchor in order to allow the generated anchor to adapt to the needs related to smoke detection. In the original neural network model, the dual-stream network model algorithm is used to extract information from the first step of the YOLO algorithm in order to further filter the smoke's characteristics as well as filter out error information, all to improve the detection capabilities of the overall neural network for video smoke fog images. Compared with traditional YOLOv4 networks, the algorithm obtained by the model algorithm has been improved by 8.51°/0. In actual tests, the alarm time requirements of the smoke alarm test program for early fire monitoring and the alarm systems for visual images were improved, and the detection accuracy of the network was also improved based on the assurance of the detection speed, while the performance of the model algorithm was also improved for different scenes. © 2024 IEEE.

关键词： dual-stream Network K-means safety science smoke detection YOLOv4

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Exploring Domain Generalization in Semantic Segmentation for Digital Histopathology: A Comparative Evaluation of Deep Learning Models 24

Exploring Domain Generalization in Semantic Segmentation for...

引用

9th International conference on Biomedical Signal and image processing, ICBIP 2024

作者： Liu, Ruochen xiao, Hongyan Wang, Yuyao Zhang, Minghao Meng, Biwen Long, xi Liu, Jingxin School of Advanced Technology Xi'An Jiaotong-Liverpool University China School of AI and Advanced Computing Xi'An Jiaotong-Liverpool University China

ISBN: (纸本)9798400717970

With the rise in clinicopathologic prognosis and the introduction of whole slide imaging scanners, artificial intelligence systems are being suggested to assist pathologists in their decision-making processes. Despite advancements, the variability in digital pathology images, due to different organs, tissue preparation methods, and image acquisition processes, poses significant challenges. This variability, known as domain shift, affects the performance of machine learning models trained on specific datasets. Addressing these challenges, this paper explores domain generalization (DG) in semantic segmentation for digital histopathology images. We systematically evaluate the DG capabilities of six prominent deep learning models across two novel adenocarcinoma segmentation datasets. Our comparative analysis provides insights into the models' effectiveness in mitigating domain shift, contributing to the advancement of DG in computational pathology. © 2024 Copyright held by the owner/author(s).

关键词： Semantic Segmentation

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Fast algorithms for video target detection and tracking

Fast algorithms for video target detection and tracking

引用

conference on Signal processing, Sensor Fusion, and Target Recognition XVII

作者： Li, Changchun Si, Jennie Abousleman, Glen P. Arizona State Univ Tempe AZ 85287 USA Gen Dynam C4 Syst Scottsdale AZ 85257 USA

ISBN: (纸本)9780819471598

This paper addresses two problems commonly associated with video target tracking system. First, video target detection and tracking usually require extensive searching in a large space to find the best matches for preregistered templates. Existing fast search methods cannot guarantee a global optimal match, which results in substandard performance. To obtain a true global match, a full search at the pixel or sub-pixel level is required. Obviously., this introduces significant computational overhead, which limits the implementation of these algorithms in real-time applications. In this paper, we propose a fast method to compute two-dimensional normalized cross-correlations to efficiently find the global optimal match result from a large image area. Comparisons and complexity analysis are provided to show the efficiency of the proposed algorithm. Second, another challenge commonly faced by detection and tracking systems is the accurate detection of target orientation in a two-dimensional image. This problem is motivated by applications where the walk-in and walk-out people need to be detected and a fast image registration method is needed to compensate the change in rotation, translation and size, which is natural since the target's distance from the camera is changing dramatically. To address this issue, we propose a novel and efficient eigenvector-based method to detect target orientation and apply it into automatic human recognition system. Experimental and real-world test results verify that the proposed fast algorithm achieves similar accuracy as the recursive registration method which is computationally expensive.

关键词： infrared video target tracking image registration human recognition cross correlation

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FPGA Based Ultrasound Backend System with image Enhancement Technique

FPGA Based Ultrasound Backend System with Image Enhancement ...

引用

5th ISSNIP-IEEE Biosignals and Biorobotics conference - Biosignals and Robotics for Better and Safer Living (BRC)

作者： Akkala, Vivek Rajalakshmi, P. Kumar, Punit Desai, Uday B. ITT Hyderabad Dept Elect Engn Hyderabad 502205 Andhra Pradesh India

ISBN: (纸本)9781479956906

Ultrasound imaging uses high-frequency sound waves in medical imaging like obstetric diagnosis, stones in kidney etc. As ultrasound images are captured in real-time, they can show movement of the body's internal organs as well as blood flowing through blood vessels. In this paper medical B-mode architecture of the backend system is implemented in Kintex-7 FPGA platform. The backend processing consists of envelope detection which uses fixed filter coefficients for Hilbert transformation, log compression technique to achieve the desired dynamic range for display and image enhancement technique to increase the contrast. In-phase and quadrature phase components are computed using envelope detection block, whose absolute value is compressed to fit the dynamic range of display and interpolated to avoid artifacts while displaying. Further full scale contrast stretch enhancement technique is used to improve the image clarity. The implementation of the backend algorithms along with image enhancement technique on FPGA, show that the resolution of display is improved and also the hardware resource utilization is minimized leading to compact design for portable ultrasound systems.

关键词： Field programmable gate arrays Programmable logic arrays ultrasonic transducers image quality image enhancement

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Analysing Wear in Carpets By Detecting Varying Local Binary Patterns

Analysing Wear in Carpets By Detecting Varying Local Binary ...

引用

conference on image processing: algorithms and systems IX

作者： Orjuela, S. A. Vansteenkiste, E. Rooms, F. De Meulemeester, S. de Keyser, R. Philips, W. Univ Ghent IBBT IPI Dept Telecommun & Informat Proc TELIN Sint Pietersnieuwstr 41 B-9000 Ghent Belgium Univ Ghent Dept Text Ghent Belgium Univ Ghent Dept Elect Energy Syst & Automat Ghent Belgium

ISBN: (纸本)9780819484079

Currently, carpet companies assess the quality of their products based on their appearance retention capabilities. For this, carpet samples with different degrees of wear after a traffic exposure simulation process are rated with wear labels by human experts. Experts compare changes in appearance in the worn samples to samples with original appearance. This process is subjective and humans can make mistakes up to 10% in rating. In search of an objective assessment, research using texture analysis has been conducted to automate the process. Particularly, Local Binary Pattern (LBP) technique combined with a Symmetric adaptation of the Kullback-Leibler divergence (SKL) are successful for extracting texture features related to the wear labels either from intensity and range images. We present in this paper a novel extension of the LBP techniques that improves the representation of the distinct wear labels. The technique consists in detecting those patters that monotonically change with the wear labels while grouping the others. Computing the SKL from these patters considerably increases the discrimination between the consecutive groups even for carpet types where other LBP variations fail. We present results for carpet types representing 72% of the existing references for the EN1471:1996 European standard.

关键词： LBP techniques image Analysis Wear Analysis Texture Analysis

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Greedy algorithms of feature selection for multiclass image classification 4

Greedy algorithms of feature selection for multiclass image ...

引用

4th International conference on Information Technology and Nanotechnology - Session: image processing and Earth Remote Sensing, IPERS-ITNT 2018

作者： Goncharova, E.F. Gaidel, A.V. Samara National Research University Moskovskoe Shosse 34 Samara443086 Russia Image Processing Systems Institute Br. of the Fed. Sci. Research Centre 'Crystallography and Photonics' of Russian Academy of Sciences Molodogvardeyskaya str. 151 Samara443001 Russia

To improve the performance of remote sensing images multiclass classification we propose two greedy algorithms of feature selection. The discriminant analysis criterion and regression coefficients are used as the measure of feature subset effectiveness in the first and second methods respectively. The main benefit of the built algorithms is that they estimate not the individual criterion for each feature, but the general effectiveness of the feature subset. As there is a big limitation on the number of real remote sensing images, available for the analysis, we apply the Markov random model to enlarge the image dataset. As the pattern for image modelling, a random image belonging to one of the 7 classes from the UC Merced Land-Use dataset has been used. Features have been extracted with help of MaZda software. As the result, the largest fraction of correctly classified images accounts for 95%. Dimension of the initial feature space consisting of 218 features has been reduced to 15 features, using the greedy strategy of removing a feature, based on the linear regression model. © 2018 CEUR-WS. All rights reserved.

关键词： Discriminant analysis

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Generalized Minkowski Distance-Based Local Mean k-Nearest Neighbor Classifier

Generalized Minkowski Distance-Based Local Mean k-Nearest Ne...

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2023 IEEE International conference on image processing and Computer Applications, ICIPCA 2023

作者： Sun, Guohao Sun, Yana Luo, Fenfen School of Information Science and Engineering University of Jinan Jinan China School of Electronic Engineering Xi'an Shiyou University Xi'an China College of New Engineering and Industry Putian University Putian China

ISBN: (纸本)9798350314670

This research focuses on comparing the classification performance of Local Mean-based K Nearest Neighbors (LMKNN) using different distance metrics. Specifically, we examine different value of close neighbors and 10 distance metrics within the Lp Norm ranging from 1 to 10, such as Euclidean distance, Minkowski distance and Manhattan distance. The KNN algorithm is a powerful tool for classification and regression tasks. Unlike many other algorithms, KNN is non-parametric, meaning it can adapt to complex models without making any assumptions about the data distribution. This makes it a popular choice for researchers who prefer simple classification model rather than complex one. However, calculating the distance between each test data point and all training data points can be computationally expensive, particularly when working with a large data set. To address this issue, we have consulted an improved KNN algorithm based on the local mean, which significantly reduces the calculation complexity without sacrificing accuracy. We have applied our algorithm to several commonly used data sets on UCI and observed promising classification performance. Furthermore, we have extended the Euclidean distance measurement in the original algorithm to the level of vector norm. This expands the classification index of KNN beyond just Euclidean distance and provides more accurate and robust results. © 2023 IEEE.

关键词： Classification (of information)

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Optimized restoration of wavefront coded images 15

Optimized restoration of wavefront coded images

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conference on Current Developments in Lens Design and Optical Engineering XV

作者： Toxqui-Quitl, C. Acosta, Eva Arines, Justo Padilla-Vivanco, A. Univ Politecn Tulancingo Ingn 100 Tulancingo 43629 Hidalgo Mexico Univ Santiago de Compostela Dept Fis Aplicada Santiago De Compostela 15782 Spain

ISBN: (纸本)9781628412192

Wavefront coding (WFC) uses a special aspheric optics and digital image processing to extend the depth of focus in imaging systems. An intermediate blurred image is generated by the spatial convolution of the image with the point spread function (PSF) of a cubic phase mask. Restoration tests are performed on an optically encoded image. The best option to restore an image is not always given by a deconvolution method using the PSF of the imaging system. Due to it is limited by the perfect PSF of the system. In this study, the restored image is given by a deconvolution method using the numerical PSF. As the quality of the image is depended of the pupil diameter and strength of the wfc phase plate, an optimization was achieved by means of measuring the decoded image contrast. Results shows a high resolution decoded image.

关键词： image restoration wavefront coding genetic algorithms focus invariant

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