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Classification and Change Detection Using Multi-periodic Harmonic analysis 3

Classification and Change Detection Using Multi-periodic Har...

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3rd International conference on Vision, image and Signal Processing (ICVISP)

作者： Jun, Myunghee Lee, Sang-Hoon Software Engn Anyang Univ Kyunggi Do South Korea Gachon Univ Ind & Business Engn Kyunggi Do South Korea

ISBN: (纸本)9781450376259

Time-series of satellite images have been used to identify and monitor land cover change. Long-term datasets are very useful to examine an area over a period and see what changes have occurred. It is not an easy task to develop satisfactory change detection algorithms due to the processing complexity and extraction of meaningful change pattern of interest. In an effort to find an appropriate approach for this challenge, this paper presents a harmonic model-based change detection method using time- series of satellite images. The proposed algorithm is based on the temporal profile over time for the long-term change rather than a temporary change. A harmonic model can characterize the temporal variability of land covers whose signatures exhibit seasonal trends since components of the harmonic function inherently contain temporal information about seasonal changes. Several experiments were conducted on a multi-temporal dataset of Moderate Resolution Imaging Spectroradiometer (MODIS) over the Korean peninsula, in the time interval of 2012-2016. The results indicate that the proposed algorithm has a great potential for monitoring land cover condition and annual long-term land-cover change over large regions.

关键词： Land Cover Classification Change Detection Harmonic Model MODIS

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Radiology reports automated annotation performance: rule-based machine learning vs deep learning

Radiology reports automated annotation performance: rule-bas...

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3rd Smart Cities Symposium (SCS 2020)

作者： A. Sahl S. Hasan King Khalid University Computer Science Dept. Guraiger Abha 62529 Saudi Arabia Universiti Teknologi Malaysia Sultan Ibrahim Chancellery Building Jalan Iman 81310 Skudai Johor Malaysia

Currently the availability of medical images data itself is not a challenge, but the acquisition of relevant annotations/labelling for these images is. An unsolved challenge in medical image analysis and its related radiology reports is to turn the radiology reports into an accurate annotations or structured labels in an automated manner using Natural Language Processing (NLP). Traditional rule-based Machine Learning (ML) and Deep Learning (DL) algorithms are two approaches which were used in the extraction and annotation of radiology text reports. This paper provides a comparison of the performance of traditional rule-based machine learning to that of using deep learning algorithms in the extraction and annotation of radiology text reports using natural language processing (NLP). It presents an accurate analysis of the two discussed approaches and supports such with critical evidence related to the effects both approaches have on the issue of radiological reporting. According to the evaluation of the obtained data, the research concludes that Deep Learning (DL) models perform with higher efficiency than both the TML and validated NLP classifiers in terms of accuracy in extracting the required text information from the free-text radiology reports.

关键词： medical information systems text analysis radiology natural language processing deep learning (artificial intelligence) pattern classification

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Spatio-temporal land use analysis and urban growth dynamic prediction in the kingdom of Bahrain

Spatio-temporal land use analysis and urban growth dynamic p...

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3rd Smart Cities Symposium (SCS 2020)

作者： E.A. Alburshaid M.A. Mangoud National Space Science Agency Bahrain College of Engineering University of Bahrain Bahrain

This paper aims to classify and predict land use/cover (LULC) for coarse spatial resolution satellite imageries of the Northern Governorate in the Kingdom of Bahrain during the period from 2016 to 2050. Satellite images from the European Space Agency portal have been used to observe, quantify, and classify them into four classes which are water, urban area, barren land, and agricultural area. Three classifications methods namely, Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) have been conducted to classify these imageries and the results are compared based on the accuracy assessment matrices. Land change modeller (LCM) model has been used to predict the land use of the Northern Governorate based on historical transition trend and the pattern changes. This prediction model is constructed based on the Multilayer Perceptron Neural Network (MLPNN) and Markov Chain (MC) to predict the spatial dynamics of urban growth in 2030 and 2050. The prediction results show how the urban area is going to expand significantly from during (2016-2050). Also, the paper presents the future status of the agricultural area, water and barren land.

关键词： multilayer perceptrons image classification remote sensing agriculture land use support vector machines terrain mapping neural nets land cover land use planning geophysical image processing Markov processes

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Natural image Character recognition Using One Class-Principal Component analysis Classifier 3

Natural Image Character Recognition Using One Class-Principa...

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3rd International conference on pattern analysis and Intelligent Systems (PAIS)

作者： Zouaoui, Fatima Chibani, Youcef USTHB Fac Elect & Comp Sci Lab Ingn Syst Intelligents & Commun 32 El Alia Algiers 16111 Algeria

ISBN: (纸本)9781538642382

The character recognition from natural scene image is used in many applications as for instance in intelligent transportation systems. Indeed, many recent methods have been focused to design a better feature representation of the character. However, the performances of such system is still far from perfect since it depends also of the appropriate choice of the classifier. Hence, the present paper addresses the problem of natural character recognition and tries to investigate the performance of the One Class-Principal Component analysis Classifier (OC-PCA). The OC-PCA classifier has the main advantage to absorb the high dimension of the feature vector with few samples. For evaluating the performance of the proposed classifier, experimental results are conducted on challenging Char74k dataset highlighting its robustness against the state-of-art.

关键词： Character recognition Natural scene images Chars74k dataset oBIF OC-PCA

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A Comprehensive Analytic Scheme for Classification of Novel Models

A Comprehensive Analytic Scheme for Classification of Novel ...

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International conference on Intelligent Sustainable Systems (ICISS)

作者： D. Dakshayani Himabindu S. Praveen Kumar VNR VJIET Hyderabad T.S India GIT GITAM Visakhapatnam Andhra pradesh

ISBN: (数字)9781728170893

ISBN: (纸本)9781728170909

Classification is one of the major tasks in supervised learning. This had a great impact on deep leaning in developing various State-of-the-art architectures every year. These models can capture invariances by extracting the finest features by variant design models developed using Convolution Neural Networks. These deep networks have proven their resilience in the large scale image recognition by overhauling the existing drawbacks in *** this research, a complete analytical scheme is developed to extract features from various models and to depict its performance on Fruits-360 dataset. During the analysis, the data is processed from feature extraction block to classification block. After the classification, the individual models' performance is evaluated and assessed with standard classification metrics. Out of these, an optimal model is selected in all prospects. It is observed that VGG models tend to outperform the existing models by capturing invariances by providing transferability on variant tasks. The networks VGG-16, VGG-19 obtained the highest precision of 97.2±0.005, 96.6±0.66 respectively. A pair-plot is obtained to analyse the behaviour pattern of predicted labels to that of true labels. This research is a first attempt to analyse the behaviour of pre-trained models on Fruits-360 dataset.

关键词： Feature extraction Task analysis Computer architecture Training Convolution Analytical models Error analysis

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Automatic pattern Discovery of Neonatal Brain Tumor Segmentation and Abnormalities in MRI Sequence 1

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3rd International conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)

作者： Prashantha, S. J. Poornima, K. M. AIT Chikmagalur Dept CS&E Chikmagalur Karnataka India JNNCE Shivamogga Dept CS&E Shimoga Karnataka India

ISBN: (数字)9789811358029

ISBN: (纸本)9789811358029;9789811358012

Nowadays, image segmentation performs a large-scale contribution in the medical images. Magnetic resonance (MR) image is a good substantial strategy during the fetal and neonatal periods that make earlier diagnosis of individual tumor analysis. It is used as topmost image investigation model for pattern discovery of brain tumor. The aim of our study is to discriminate the unique images related to every one of the patterns of the brain tumor on neonatal MRI. Our proposed system consists of four steps. The first step is to explore the acquisition of the neonatal MRI sequence. The second step performs segmentation of the locality of abnormality. The third step is the segregation of tumor from edema and its enhanced sequences. The fourth step is the estimation of the drawn area of tumor and calculation of the mean. Our proposed method successfully detects the tumor area in the given neonatal MRI images.

关键词： Brain tumor Magnetic resonance image Segmentation Neonatal

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Adaptive Weighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB images

Adaptive Weighted Attention Network with Camera Spectral Sen...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Jiaojiao Li Chaoxiong Wu Rui Song Yunsong Li Fei Liu The State Key Laboratory of Integrated Service Networks Xidian University Xian China School of Physics and Optoelectronic Engineering Xidian University Xian China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (C- NNs). Nevertheless, most CNN-based SR algorithms neglect to explore the camera spectral sensitivity (CSS) prior and interdependencies among intermediate features, thus limiting the representation ability of the network and performance of SR. To conquer these issues, we propose a novel adaptive weighted attention network (AWAN) for SR, whose backbone is stacked with multiple dual residual attention blocks (DRAB) decorating with long and short skip connections to form the dual residual learning. Concretely, we investigate an adaptive weighted channel attention (AWCA) module to reallocate channel-wise feature responses via integrating correlations between channels. Furthermore, a patch-level second-order non-local (PSNL) module is developed to capture long-range spatial contextual information by second-order non-local operations for more powerful feature representations. Based on the fact that the recovered RGB images can be projected by the reconstructed hyperspectral image (HSI) and the given CSS function, we incorporate the discrepancies of the RGB images and HSIs as a finer constraint for more accurate reconstruction. Experimental results demonstrate the effectiveness of our proposed AWAN network in terms of quantitative comparison and perceptual quality over other state-of-the-art SR methods. In the NTIRE 2020 Spectral Reconstruction Challenge, our entries obtain the 1st ranking on the "Clean" track and the 3rd place on the "`Real World" track. Codes are available at https://***/Deep-imagelab/AWAN.

关键词： image reconstruction Adaptive systems Adaptation models Correlation Hyperspectral imaging Cascading style sheets Task analysis

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3rd International conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

3rd International Conference on Information and Communicatio...

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3rd International conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

ISBN: (纸本)9789811317460

The proceedings contain 147 papers. The special focus in this conference is on Information and Communication Technology for Intelligent Systems. The topics include: Review of EEG signal classification;How to reverse engineer ICS protocols using pair-HMM;classification of plants using invariant features and a neural network;CSTeller: Client or server is a question;geographical zone-based cluster head for routing in urban vehicular network;Congestion management by Static Var Compensator (SVC) using power world simulator;combining user-based and item-based collaborative filtering using machine learning;Predictive monitoring system using K-NN, QDC classifiers of physiological data;Recommender system based on OSN data analytics;automation of process evaluation of saccharification of wheat starch followed by fermentation of glucose to prepare bioethanol using digital image processing;Early detection of ransomware by indicator analysis and WinAPI call sequence pattern;a novel integrated framework based on modular optimization for efficient analytics on twitter big data;study of controller techniques for precision positioning;an effective content-based strategy analysis for large-scale deduplication using a multi-level pattern-matching algorithm;tamil sign language translator—An assistive system for hearing- and speech-impaired people;aspect-based sentiment analysis of students’ feedback to improve teaching–learning process;fair mechanisms for combinatorial reverse auction-based cloud market;timbre-vibrato model for singer identification;a range-based adaptive and collaborative localization for wireless sensor networks;image edge detection techniques using sobel, T1FLS, and IT2FLS;An optimal cryptographic approach for addressing security breaches to build resilient WSN;detection of mimicry attacks on speaker verification system for cartoon characters’ dataset;smart helmet for safety driving.

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3rd International conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

3rd International Conference on Information and Communicatio...

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3rd International conference on Information and Communication Technology for Intelligent Systems, ICTIS 2018

ISBN: (纸本)9789811317415

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Pose Invariant Object recognition Using a Bag of Words Approach

Pose Invariant Object Recognition Using a Bag of Words Appro...

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3rd iberian Robotics conference (Robot)

作者： Costa, Carlos M. Sousa, Armando Veiga, Germano Univ Porto INESC TEC Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783319708362;9783319708355

Pose invariant object detection and classification plays a critical role in robust image recognition systems and can be applied in a multitude of applications, ranging from simple monitoring to advanced tracking. This paper analyzes the usage of the Bag of Words model for recognizing objects in different scales, orientations and perspective views within cluttered environments. The recognition system relies on image analysis techniques, such as feature detection, description and clustering along with machine learning classifiers. For pinpointing the location of the target object, it is proposed a multiscale sliding window approach followed by a dynamic thresholding segmentation. The recognition system was tested with several configurations of feature detectors, descriptors and classifiers and achieved an accuracy of 87% when recognizing cars from an annotated dataset with 177 training images and 177 testing images.

关键词： Object recognition image feature analysis Clustering Machine learning

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