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

共 15616 条记录，以下是4471-4480 订阅

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A comparitive study of image Filters and Machine Learning for use in Machined Part Recognition

A comparitive study of Image Filters and Machine Learning fo...

引用

International conference on Sensing Technology, ICST

作者： Andrew O'Riordan Gerard Dooly Daniel Toal Thomas Newe Confirm Research Centre Centre of Robotics and Intelligent Systems University of Limerick Limerick Ireland

ISBN: (数字)9781728148076

ISBN: (纸本)9781728148083

The use of filters for image processing has long existed and is well established within industrial practice and in academia. The wide-spread adoption of machine learning into industrial spaces, however, has presented opportunities for the use of machine learning and artificial intelligence applications to be further developed and researched in relation to image processing. This paper aims to identify three of the main types of filters used in the machine learning process and to test their ability for target recognition of a basic/standard industry part.

关键词： Filtering algorithms image edge detection image color analysis Information filters Target recognition

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Skin Disease Detection using Improved Bag of Features Algorithm

Skin Disease Detection using Improved Bag of Features Algori...

引用

International conference of Signal processing and Intelligent systems (ICSPIS)

作者： Ma. Christina R. Navarro Davood Pour Yousefian Barfeh Adamson Universitya Manila Philippines College of Computer Studies First City Providential College San Jose del Monte Philippines

ISBN: (数字)9781728153506

ISBN: (纸本)9781728153513

This study uses digital image processing to develop a model to detect common skin diseases in the Philippines; acne and BOIL. The researchers used different methods and technique such as; improved bag of features algorithm, speeded up robust features algorithm, interest point detection, Gaussian filtering and k-means clustering. The overall accuracy rate of the system is 96% while overall loss is (0.03), and the total average confidence rate of the tests done with different test data in terms of detection and classification is 98.48%. Moreover, the average precision/recall rate of combined images for the two categories is 99% In the confusion matrix, Acne got the highest number of correct predicted skin disease. On the other hand, BOIL got the lowest number of correctly classified. Besides, Acne got the highest precision result of 98%, while BOIL got a high precision result of 97%. In recall results, both models have the same percentage of 99%.

关键词： Skin Feature extraction Diseases visualization Signal processing algorithms Clustering algorithms Classification algorithms

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Monitoring and Detection of Combustible Gas Leakage by Using Infrared Imaging

Monitoring and Detection of Combustible Gas Leakage by Using...

引用

IEEE International conference on Imaging systems and Techniques (IST)

作者： Liu, Binglu Ma, Haocheng Zheng, Xiaoping Peng, Lihui Xiao, Anshan Tsinghua Univ Dept Automat Beijing Peoples R China Res Inst Safety Engn State Key Lab Safety & Control Chem Qingdao Peoples R China

ISBN: (纸本)9781538666289

In this paper, an infrared thermal imaging system is developed for combustible gas leakage monitoring and detection. The related imaging processing and gas leakage detection algorithms are presented. The gas leakage monitoring and detection experiments regarding three different scenes, including two indoor scenes and one outdoor scene, are carried out. The preliminary results show that the proposed imaging system and the algorithms are of detecting the leakage of 1% isobutane at 0.2L/min.

关键词： Gas leakage detection Infrared imaging image processing Frames subtraction Region of interest

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An Overview of Recent Convolutional Neural Network algorithms for image Recognition 61

An Overview of Recent Convolutional Neural Network Algorithm...

引用

61st IEEE International Midwest Symposium on Circuits and systems (MWSCAS)

作者： Sapijaszko, Genevieve Mikhael, Wasfy B. Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

ISBN: (纸本)9781538673928

An important aspect of a guidance system in an autonomous vehicle is the detection of objects and extraction of reliable features that can identify the object from images. Object detection methods have evolved in the last 20 years, with convolutional neural network algorithms in particular showing promise. This paper compares and contrasts recent convolutional neural network algorithms using the BelgiumTS Dataset. The different convolutional neural networks used and compared are AlexNet, VGGNet, GoogleNet, and ResNet. The object detection methods will be used to find the best processing speed and recognition rate among the convolutional neural networks. A survey of published state-of-the-art, convolutional neural networks are evaluated against published results.

关键词： Convolutional neural networks Feature extraction Training image recognition Convolutional codes Object recognition Kernel

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algorithms of Automated Formulation of the Operability Condition of Complex Technical systems 14

Algorithms of Automated Formulation of the Operability Condi...

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14th International conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Bobalo, Yuriy Seniv, Maksym Symets, Ivan Lviv Polytech Natl Univ Lvov Ukraine

The paper deals with the development of algorithms, which enable an automation of the reliability block diagram (RBD) processing and give an opportunity to build the reliability block diagram image of the technologica... 详细信息

ISBN: (纸本)9781538658819

关键词： reliability reliability block diagram software

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Research of image Sharpness Assessment Algorithm for Autofocus

Research of Image Sharpness Assessment Algorithm for Autofoc...

引用

International conference on image, vision and Computing (ICIVC)

作者： Lilin Her Xiaojun Yang Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi’an China

ISBN: (纸本)9781728123264

With the wide application of imaging system in security monitoring, aerospace, medical image and other fields, how to capture the clear image of the target in real time and automatically is particularly important. image sharpness evaluation function is the key to evaluate the imaging quality of various imaging systems. The spatial gradient evaluation algorithm is based on the direct processing of image pixels, and the calculation is simple and intuitive. In this paper, we compare the performance of image sharpness evaluation functions in four spatial domains through two sets of atlases with different background richness. Experiments show that Benner algorithm has high scene adaptability and strong anti-jamming ability; Laplace algorithm has high sensitivity and can get results quickly in different size images; Tenengrad algorithm can reduce the occurrence of local extremum after selecting a certain threshold; Robert algorithm has poor unimodality and accuracy in two sets of atlas test.

关键词： Sharpness evaluation function Autofocus Gradient operator Brenner algorithm auto-focus atlas images Sharpness image imagery (Psychotherapy) Imaging systems algorithms direct processing topographic maps

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Soft Prototyping Camera Designs for Car Detection Based on a Convolutional Neural Network

Soft Prototyping Camera Designs for Car Detection Based on a...

引用

International conference on Computer vision Workshops (ICCV Workshops)

作者： Zhenyi Liu Trisha Lian Joyce Farrell Brian Wandell Stanford University USA Jilin University China

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Imaging systems are increasingly used as input to convolutional neural networks (CNN) for object detection; we would like to design cameras that are optimized for this purpose. It is impractical to build different cameras and then acquire and label the necessary data for every potential camera design; creating software simulations of the camera in context (soft prototyping) is the only realistic approach. We implemented soft-prototyping tools that can quantitatively simulate image radiance and camera designs to create realistic images that are input to a convolutional neural network for car detection. We used these methods to quantify the effect that critical hardware components (pixel size), sensor control (exposure algorithms) and image processing (gamma and demosaicing algorithms) have upon average precision of car detection. We quantify (a) the relationship between pixel size and the ability to detect cars at different distances, (b) the penalty for choosing a poor exposure duration, and (c) the ability of the CNN to perform car detection for a variety of post-acquisition processing algorithms. These results show that the optimal choices for car detection are not constrained by the same metrics used for image quality in consumer photography. It is better to evaluate camera designs for CNN applications using soft prototyping with task-specific metrics rather than consumer photography metrics.

关键词： Cameras Automobiles Solid modeling Measurement Lenses image processing

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Household Appliance Identification Based on a Novel Load Signature processing Framework

Household Appliance Identification Based on a Novel Load Sig...

引用

IEEE conference on Energy Internet and Energy System Integration (EI2)

作者： Zhiren Ren Bo Tang Longfeng Wang Hui Liu Shuqin Dong Haiping Wu Wasion Group Co. Ltd. Changsha China School of Traffic & Transportation Engineering Central South University Changsha China

ISBN: (数字)9781728131375

ISBN: (纸本)9781728131382

Non-intrusive load identification is a promising candidate in providing energy consumption information of individual appliances, which can improve energy-using habits and save energy. The load signature has a huge impact on the performance of non-intrusive load identification systems. In this paper, a new household appliance load identification model based on a novel load signature processing framework is proposed. Based on the proposed framework, the original vi trajectory of the household appliance is mapped to the binary image. The LeNet trained on the MNIST dataset is utilized to extract the deep features from the binary vi image. After that, the ReliefF algorithm is adopted to select the most important information from the deep features. The SVM is utilized to identify household appliances based on the obtained load signature. Finally, the experimental results based on the measured dataset and the PLAID dataset indicate that the proposed load signature processing framework is effective in improving the household appliance identification accuracy.

关键词： Home appliances Feature extraction Trajectory Support vector machines Energy consumption Character recognition Load modeling

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Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction 1

引用

1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Dzyubachyk, Oleh Koolstra, Kirsten Pezzotti, Nicola Lelieveldt, Boudewijn P. F. Webb, Andrew Börnert, Peter Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands C.J. Gorter Center for High Field MRI Department of Radiology Leiden University Medical Center Leiden Netherlands Computer Graphics and Visualization Group Delft University of Technology Delft Netherlands Philips Research Eindhoven Eindhoven Netherlands Intelligent Systems Department Delft University of Technology Delft Netherlands Philips Research Hamburg Hamburg Germany

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

Quality assessment of different Magnetic Resonance Fingerprinting (MRF) sequences and their corresponding dictionaries remains an unsolved problem. In this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. First, we demonstrated stability of calculated embeddings that allows neglecting the stochastic nature of t-SNE. Next, we proposed and analyzed two algorithms for comparing the embeddings. Finally, we performed two simulations in which we reduced the MRF sequence/dictionary in length or size and analyzed the influence of this reduction on the resulting embedding. We believe that this research can pave the way to development of a software tool for analysis, including better understanding, optimization and comparison, of different MRF sequences. © 2019, Springer Nature Switzerland AG.

关键词： Magnetic resonance

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Color Quantization using Coreset Sampling

Color Quantization using Coreset Sampling

引用

IEEE International conference on systems, Man, and Cybernetics (SMC)

作者： Valenzuela, German Celebi, M. Emre Schaefer, Gerald Univ Cent Arkansas Dept Comp Sci Conway AR 72035 USA Loughborough Univ Dept Comp Sci Loughborough Leics England

ISBN: (纸本)9781538666500

Color quantization is an important operation with many applications in computer graphics and image processing and analysis. Clustering algorithms have been extensively applied to this problem. However, despite its popularity as a general purpose clustering algorithm, k-means has not received much attention in the colour quantization literature because of its high computational requirements and sensitivity to initialization. In this paper, we propose a novel color quantization method based on the k-means algorithm. The proposed method utilizes adaptive initialization, deterministic sub-sampling and efficient coreset construction to attain high speed and high quality quantization. Experiments on a set of benchmark images demonstrate the proposed method to be significantly faster than k-means while delivering nearly identical results.

关键词： image color analysis Clustering algorithms Quantization (signal) Sensitivity Computer science Clustering methods Euclidean distance

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