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检索条件"任意字段=1st International Conference on Image Processing Machine Learning and Pattern Recognition"

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A learning Approach for Informative-Frame Selection in US Rheumatology images 2nd

A Learning Approach for Informative-Frame Selection in US Rh...

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2nd international Workshop on Recent Advances in Digital Security: Biometrics and Forensics, BioFor 2019, 1st international Workshop on pattern recognition for Cultural Heritage, PatReCH 2019, 1st international Workshop eHealth in the Big Data and Deep learning Era, e-BADLE 2019, international Workshop on Deep Understanding Shopper Behaviors and Interactions in Intelligent Retail Environments, DEEPRETAIL 2019 and Industrial session held at the 20th international conference on image Analysis and processing, ICIAP 2019

作者： Fiorentino, Maria Chiara Moccia, Sara Cipolletta, Edoardo Filippucci, Emilio Frontoni, Emanuele Department of Information Engineering Università Politecnica delle Marche Ancona Italy Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy Department of Rheumatology Università Politecnica delle Marche Ancona Italy

ISBN: (纸本)9783030307530

Rheumatoid arthritis (RA) is an autoimmune disorder that causes pain, swelling and stiffness in joints. Nowadays, ultrasound (US) has undergone an increasing role in RA screening since it is a powerful tool to assess disease activity. However, obtaining a good quality US frame is a tricky operator dependent procedure. For this reason, the purpose of this paper is to present a strategy to the automatic selection of informative US rheumatology images by means of Convolutional Neural Networks (CNNs). The proposed method is based on VGG16 and Inception V3 CNNs, which are fine tuned to classify 214 balanced metacarpal head US images (75% used for training and 25% used for testing). A repeated 3 fold cross validation for each CNN was performed. The best results were achieved with VGG16 (area under the curve = 90%). These results support the possibility of applying this method in the actual clinical practice for supporting the diagnostic process and helping young residents’ training. © 2019, Springer Nature Switzerland AG.

关键词： Ultrasonics

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OCR for Greek polytonic (multi accent) historical printed documents: Development, optimization and quality control 2019

OCR for Greek polytonic (multi accent) historical printed do...

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3rd international conference on Digital Access to Cultural Textual Heritage, DATeCH 2019

作者： Sichani, Anna-Maria Kaddas, Panagiotis Mikros, Georgios K. Gatos, Basilis Sussex Humanities Lab. University of Sussex East SussexBN1 9RG United Kingdom Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Agia ParaskeviGR-153 10 Greece Department of Italian Language and Literature School of Philosophy National and Kapodistrian University of Athens AthensGR-157 84 Greece

ISBN: (纸本)9781450371940

This paper presents the development and implementation of a robust OCR tool and a related comprehensive workflow for the recognition of Greek printed polytonic scripts. This project is initiated and developed by an interdisciplinary team with expertise in the areas of document image processing, character segmentation and recognition, machine learning, corpus creation and digital humanities. Our paper aims to describe the design and development of the workflow around this project, including data gathering and structuring, OCR tool development, user interface development, experiments on the training procedure of the tool, evaluation, post-correction and quality control of the results. © 2019 Association for Computing machinery.

关键词： machine learning

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study on Temperature Measurement of Power Equipment Based on Retinex Theory and machine learning

Study on Temperature Measurement of Power Equipment Based on...

引用

Electrical Engineering (CIYCEE), IEEE China international Youth conference on

作者： Yang He Qizheng Ye Wenjiao Du Wenmao Li State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Jiangmen Power Supply Bureau Guangdong Power Grid Company Jiangmen China

ISBN: (数字)9781728196596

ISBN: (纸本)9781728196602

Electric power equipment temperature is closely related with its working condition. The current mainstream infrared thermal imagers are expensive, complicated to operate and weak in spatial positioning. Infrared and visible image fusion has a promising application prospect in power system fault location, anomaly monitoring and so on. Visible light radiation temperature measurement has many application cases in the high temperature field. This paper puts forward a method based on image processing and machine learning, and successfully applies visible light temperature measurement technology to the low temperature field such as the temperature detection of power equipment. We used copper plates as the research object, and established a library of visible images under different temperature and light conditions. Four kinds of machine learning algorithms were used to build the temperature prediction model by extracting the gray distribution features from images. We select two algorithms which have good performance in time complexity and prediction accuracy. The average absolute error of predicting temperature is only about 1.5°C. We also have performed Retinex processing on all images to eliminate the interference of different lighting intensity on the grayscale features. After training and calculation, it was found that the average absolute error is reduced to 1.309°C with the same algorithms, which has a better prediction accuracy.

关键词： Temperature measurement machine learning algorithms machine learning Interference Predictive models Gray-scale Prediction algorithms

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Comparison of Deep learning, Data Augmentation and Bag of-Visual-Words for Classification of Imbalanced image Datasets 2nd

Comparison of Deep Learning, Data Augmentation and Bag of-Vi...

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2nd international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2018

作者： Saini, Manisha Susan, Seba Delhi Technological University Delhi India

ISBN: (纸本)9789811391804

image classification is a supervised machine learning task to classify images into different categories. As most real-world datasets are imbalanced in nature, instances are not equally distributed in all classes which often results in biased classification. So considering this objective, we are dealing with an imbalanced dataset. We have created a small size of an imbalanced dataset consisting of Ant and Plane images from Caltech-101 dataset. This paper illustrates the comparison between Deep learning, Data Augmentation and the Bag-of-Visual Words (BOVW) for classification of imbalanced image datasets. According to the experimental results, it was found that deep learning results in higher accuracy in comparison to bag-of-visual-words (BOVW) and data augmentation. © 2019, Springer Nature Singapore Pte Ltd.

关键词： image classification

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1st international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2016

1st International Conference on Recent Trends in Image Proce...

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1st international conference on Recent Trends in image processing and pattern recognition, RTIP2R 2016

ISBN: (纸本)9789811048586

The proceedings contain 39 papers. The special focus in this conference is on Recent Trends in image processing and pattern recognition. The topics include: Word-level thirteen official Indic languages database for script identification in multi-script documents;skew detection and correction of devanagari script using interval halving method;Eigen value based features for offline handwritten signature verification using neural network approach;an approach for logo detection and retrieval in documents;spotting symbol over graphical documents via sparsity in visual vocabulary;word retrieval from Kannada document images using hog and morphological features;hand drawn symbol recognition in immersive virtual reality using deep extreme learning machines;comparative study of handwritten Marathi characters recognition based on KNN and SVM classifier;recognition of handwritten devanagari numerals by graph representation and lipschitz embedding;combination of feature selection methods for the effective classification of microarray gene expression data;pattern recognition based on hierarchical description of decision rules using choquet integral;classification of summarized sensor data using sampling and clustering: a performance analysis;feature reduced weighted fuzzy binarization for histogram comparison of promoter sequences;a fast k-nearest neighbor classifier using unsupervised clustering;DSS for prognostication of academic intervention by applying DM technique;cluster based symbolic representation for skewed text categorization;performance analysis of impulse noise attenuation techniques;industrial applications of colour texture classification based on anisotropic diffusion and a comprehensive method for assessing the blepharospasm cases severity.

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Research on Visual Speech recognition Based on Local Binary pattern and stacked Sparse Autoencoder 1st

Research on Visual Speech Recognition Based on Local Binary ...

引用

1st international conference on Human Systems Engineering and Design (IHSED) - Future Trends and Applications

作者： Lu, Yuanyao Gu, Ke He, Shan North China Univ Technol Sch Elect & Informat Engn Beijing 100144 Peoples R China

ISBN: (纸本)9783030020538;9783030020521

Lip feature extraction from human mouth image plays an essential role in visual speech recognition applications. This paper presents a lip feature extraction algorithm based on Local Binary patterns (LBP) and stacked Sparse Autoencoders (SSAE). First, LBP texture features are extracted from lip images. Then SSAE uses greedy unsupervised learning to extract high-level features. At last, we improve the performance of overall system by fine-tuning and input the extracted features into the Softmax classifier. Compared with traditional methods, the model proposed in this paper has higher classification accuracy and more applicability.

关键词： Visual speech recognition Local Binary pattern stacked Sparse Autoencoder

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Adaptive bilinear pooling for fine-grained representation learning 1

Adaptive bilinear pooling for fine-grained representation le...

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1st ACM international conference on Multimedia in Asia, MMAsia 2019

作者： Min, Shaobo Xie, Hongtao Tian, Youliang Yao, Hantao Zhang, Yongdong University of Science and Technology of China Guizhou Provincial Key Laboratory of Public Big Data China Guizhou Provincial Key Laboratory of Public Big Data GuiZhou University China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China University of Science and Technology of China China

ISBN: (纸本)9781450368414

Fine-grained representation learning targets to generate discriminative description for fine-grained visual objects. Recently, the bilinear feature interaction has been proved effective in generating powerful high-order representation with spatially invariant information. However, the existing methods apply a fixed feature interaction strategy to all samples, which ignore the image and region heterogeneity in a dataset. To this end, we propose a generalized feature interaction method, named Adaptive Bilinear Pooling (ABP), which can adaptively infer a suitable pooling strategy for a given sample based on image content. Specifically, ABP consists of two learning strategies: P-order learning (P-net) and spatial attention learning (S-net). The p-order learning predicts an optimal exponential coefficient rather than a fixed order number to extract moderate visual information from an image. The spatial attention learning aims to infer a weighted score that measures the importance of each local region, which can compact the image representations. To make ABP compatible with kernelized bilinear feature interaction, a crossed two-branch structure is utilized to combine the P-net and S-net. This structure can facilitate complementary information exchange between two different visual branches. The experiments on three widely used benchmarks, including fine-grained object classification and action recognition, demonstrate the effectiveness of the proposed method. © 2019 Association for Computing machinery.

关键词： image representation

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Detection and Classification of Faulty Weft Threads Using Both Feature-Based and Deep Convolutional machine learning Methods 7th

Detection and Classification of Faulty Weft Threads Using Bo...

引用

7th international conference on pattern recognition Applications and Methods, ICPRAM 2018

作者： Kopaczka, Marcin Saggiomo, Marco Güttler, Moritz Kielholz, Kevin Merhof, Dorit Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Institut für Textiltechnik RWTH Aachen University Aachen Germany

ISBN: (纸本)9783030054984

In our work, we analyze how faulty weft threads in air-jet weaving machines can be detected using image processing methods. To this end, we design and construct a multi-camera array for automated acquisition of images of relevant machine areas. These images are subsequently fed into a multi-stage image processing pipeline that allows defect detection using a set of different preprocessing and classification methods. Classification is performed using both image descriptors combined with feature-based machine learning algorithms and deep learning techniques implementing fully convolutional neural networks. To analyze the capabilities of our solution, system performance is thoroughly evaluated under realistic production settings. We show that both approaches show excellent detection rates and that by utilizing semantic segmentation acquired from a fully convolutional network we are not only able to detect defects reliably but also classify defects into different subtypes, allowing more refined strategies for defect removal. © 2019, Springer Nature Switzerland AG.

关键词： Defects

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Sequence-to-sequence Bangla sentence generation with LstM recurrent neural networks 1

Sequence-to-sequence Bangla sentence generation with LSTM re...

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1st international conference on Pervasive Computing Advances and Applications, PerCAA 2019

作者： Md. Sanzidul, Islam Sadia Sultana, Sharmin Mousumi Abujar, Sheikh Hossain, Syed Akhter Dept. of CSE Daffodil International University Dhaka1207 Bangladesh Dept. of CSE Daffodil International University Dhaka1207 Bangladesh Dept. of CSE Daffodil International University Dhaka1207 Bangladesh

Sequence to sequence text generation is the most efficient approach for automatically converting the script of a word from a source sequence to a target sequence. Text generation is the application of natural language generation which is useful in sequence modeling like the machine translation, speech recognition, image captioning, language identification, video captioning and much more. In this paper we have discussed about Bangla text generation, using deep learning approach, Long Short-term Memory (LstM), a special kind of RNN (Recurrent Neural Network). LstM networks are suitable for analyzing sequences of text data and predicting the next word. LstM could be a respectable solution if you want to predict the very next point of a given time sequence. In this article we proposed a artificial Bangla Text Generator with LstM, which is very early for this language and also this model is validated with satisfactory accuracy rate. © 2019 The Authors. Published by Elsevier Ltd.

关键词： Natural language processing systems

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RNN-LstM Based Indoor Scene Classification with HoG Features 2nd

RNN-LSTM Based Indoor Scene Classification with HoG Features

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2nd international conference on Advanced Informatics for Computing Research (ICAICR)

作者： Verma, Ambica Sharma, Shilpa Gupta, Priyanka Lovely Profess Univ Sch Comp Sci & Engn Jalandhar Punjab India

ISBN: (纸本)9789811331404;9789811331398

The machine learning and artificial intelligence models have evolved with unenvisaged swiftness over the past decade. The machine learning and artificial intelligence models work on the basis of the mathematical models, which are capable of processing the financial, image, video, audio, audio-visual and several other forms of data. In this paper, the work has been carried upon the bulk image data representing the variety of indoor scenes. The goal is to detect the type of indoor scene, which can be utilized by variety of artificial intelligent application for various purposes. The indoor scene recognition has been performed over the computer vision & pattern recognition (CVPR), 2009 dataset, which is consisted of 15620 images. The deep learning mechanism known as recurrent neural network (RNN) has been incorporated for the classification of the indoor scene data over the histogram of oriented gradient (HoG) based features. Specifically, the forget gates based recurrent mode called Long short-term memory (LstM) has been incorporated for the classification of the indoor scene data. The performance of the proposed model has been analyzed over the CVPR09 dataset using 50 and 100 randomly drawn test cases. The proposed model has been found 92% accurate in comparison with SVM (88%), KNN (80%) and Naive Bayes (86%). The F1-measure based performance assessment also proves the robustness of LstM based model with 96% accuracy over 92% of Naive Bayes, 93% of SVM and 88% of KNN.

关键词： Deep learning Recurrent neural network Long-short term memory LstM Indoor scene recognition CVPR'09

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