Automatic Identification and Extraction of Clouds from Astronomical Images Based on Support Vector Machinetwo

2019 ◽  
Vol 43 (1) ◽  
pp. 128-142 ◽  
Author(s):  
Wang Li-wen ◽  
Jia Peng ◽  
Cai Dong-mei ◽  
Liu Hui-gen
Keyword(s):  
Support Vector ◽  
Automatic Identification ◽  
Astronomical Images

scholarly journals Robust and Fast Scene Recognition in Robotics Through the Automatic Identification of Meaningful Images

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4024 ◽  
Author(s):  
David Santos ◽  
Eric Lopez-Lopez ◽  
Xosé M. Pardo ◽  
Roberto Iglesias ◽  
Senén Barro ◽  
...  
Keyword(s):  
Experimental Validation ◽  
Minimum Spanning Tree ◽  
Scene Recognition ◽  
Public Image ◽  
Support Vector ◽  
Automatic Identification ◽  
Correct Identification ◽  
Generalization Capability ◽  
Key Points ◽  
Visual Descriptors

Scene recognition is still a very important topic in many fields, and that is definitely the case in robotics. Nevertheless, this task is view-dependent, which implies the existence of preferable directions when recognizing a particular scene. Both in human and computer vision-based classification, this actually often turns out to be biased. In our case, instead of trying to improve the generalization capability for different view directions, we have opted for the development of a system capable of filtering out noisy or meaningless images while, on the contrary, retaining those views from which is likely feasible that the correct identification of the scene can be made. Our proposal works with a heuristic metric based on the detection of key points in 3D meshes (Harris 3D). This metric is later used to build a model that combines a Minimum Spanning Tree and a Support Vector Machine (SVM). We have performed an extensive number of experiments through which we have addressed (a) the search for efficient visual descriptors, (b) the analysis of the extent to which our heuristic metric resembles the human criteria for relevance and, finally, (c) the experimental validation of our complete proposal. In the experiments, we have used both a public image database and images collected at our research center.

scholarly journals Automatic identification of bird targets with radar via patterns produced by wing flapping

2008 ◽  
Vol 5 (26) ◽  
pp. 1041-1053 ◽  
Author(s):  
Serge Zaugg ◽  
Gilbert Saporta ◽  
Emiel van Loon ◽  
Heiko Schmaljohann ◽  
Felix Liechti
Keyword(s):  
Bird Migration ◽  
Wind Farms ◽  
Classification Performance ◽  
Future Research ◽  
Support Vector ◽  
Automatic Identification ◽  
Radar Meteorology ◽  
Ground Clutter ◽  
Environmental Impact Assessments ◽  
Radar Signals

Bird identification with radar is important for bird migration research, environmental impact assessments (e.g. wind farms), aircraft security and radar meteorology. In a study on bird migration, radar signals from birds, insects and ground clutter were recorded. Signals from birds show a typical pattern due to wing flapping. The data were labelled by experts into the four classes BIRD, INSECT, CLUTTER and UFO (unidentifiable signals). We present a classification algorithm aimed at automatic recognition of bird targets. Variables related to signal intensity and wing flapping pattern were extracted (via continuous wavelet transform). We used support vector classifiers to build predictive models. We estimated classification performance via cross validation on four datasets. When data from the same dataset were used for training and testing the classifier, the classification performance was extremely to moderately high. When data from one dataset were used for training and the three remaining datasets were used as test sets, the performance was lower but still extremely to moderately high. This shows that the method generalizes well across different locations or times. Our method provides a substantial gain of time when birds must be identified in large collections of radar signals and it represents the first substantial step in developing a real time bird identification radar system. We provide some guidelines and ideas for future research.

scholarly journals Research on automatic identification technique of CT image in lung

2018 ◽  
Vol 15 (3) ◽  
pp. 501-515
Author(s):  
Zhijie Zhao ◽  
Cong Ren ◽  
Huadong Sun ◽  
Zhipeng Fan ◽  
Ze Gao
Keyword(s):  
Human Cancer ◽  
Pulmonary Nodules ◽  
Lung Parenchyma ◽  
Support Vector ◽  
Automatic Identification ◽  
Ct Image ◽  
Cancer Disease ◽  
Identification Technique ◽  
Pulmonary Parenchyma ◽  
Occurrence Matrix

Lung cancer has become the world's human cancer disease in the "first killer." In this paper, three aspects of lung CT images were treated. Firstly, based on the CT image preprocessing, the lung parenchyma was segmented by random walk algorithm and the ROI was extracted from the pulmonary parenchyma; Secondly, the 10-dimensional feature vectors of pulmonary nodule ROI were extracted by the gray level co-occurrence matrix algorithm; Finally, support vector machine as a classifier is to identify the pulmonary nodules and the accuracy rate is more than 94%. The experimental results show that the study of automatic CT image recognition can provide some data reference for doctors and play a supporting role in the course of treatment.

scholarly journals Classification of Historical Documents Based on LBP and LPQ Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 1534-1539
Keyword(s):  
Support Vector ◽  
Historical Documents ◽  
Automatic Identification ◽  
Document Images ◽  
K Nearest Neighbors ◽  
New Approach ◽  
Modern Language ◽  
Handwritten Document ◽  
Feature Values

Historical documents are important source for knowing culture, language, social activities, educational system, etc. The historical documents are in different languages and evolved over centuries and transformed to present modern language, classification of documents into various eras, recognition of words etc. In this paper, we have proposed a new approach to automatic identification of the age of the historical handwritten document images based on LBP (Local Binary Pattern) and LPQ (Local Phase Quantization) algorithm. The standard historical handwritten document images named as MPS (Medieval Paleographic Scale) dataset which is publicly available is used to experiment. LBP and LPQ descriptors are used to extract the features of the historical document images. Further, documents are classified based on the discriminating feature values using classifiers namely K-NN (K-Nearest Neighbors) and SVM (Support Vector Machine) classifier. The accuracy of historical handwritten document images by K-NN and SVM are 90.7% and 92.8% respectively.

scholarly journals AUTOMATIC IDENTIFICATION OF EPILEPTIC AND BACKGROUND EEG SIGNALS USING FREQUENCY DOMAIN PARAMETERS

2010 ◽  
Vol 20 (02) ◽  
pp. 159-176 ◽  
Author(s):  
OLIVER FAUST ◽  
U. RAJENDRA ACHARYA ◽  
LIM CHOO MIN ◽  
BERNHARD H. C. SPUTH
Keyword(s):  
Support Vector Machine ◽  
Moving Average ◽  
Gaussian Mixture ◽  
Power Density Spectrum ◽  
Estimation Methods ◽  
Support Vector ◽  
Automatic Identification ◽  
Spectrum Estimation ◽  
Classification Rate ◽  
Density Spectrum

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yule-Walker and Burg's methods, to extract the power density spectrum from representative signal samples. Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. The classification results are documented with confusion matrices and compared with receiver operating characteristic curves. We found that Burg's method for spectrum estimation together with a support vector machine classifier yields the best classification results. This combination reaches a classification rate of 93.33%, the sensitivity is 98.33% and the specificy is 96.67%.

RECOGNISING DISCOURSE CAUSALITY TRIGGERS IN THE BIOMEDICAL DOMAIN

2013 ◽  
Vol 11 (06) ◽  
pp. 1343008 ◽  
Author(s):  
CLAUDIU MIHĂILĂ ◽  
SOPHIA ANANIADOU
Keyword(s):  
Gold Standard ◽  
Conditional Random Fields ◽  
Support Vector ◽  
Automatic Identification ◽  
Specific Information ◽  
Biomedical Domain ◽  
Semantic Features ◽  
Standard Data ◽  
Domain Specific ◽  
The Impact

Current domain-specific information extraction systems represent an important resource for biomedical researchers, who need to process vast amounts of knowledge in a short time. Automatic discourse causality recognition can further reduce their workload by suggesting possible causal connections and aiding in the curation of pathway models. We describe here an approach to the automatic identification of discourse causality triggers in the biomedical domain using machine learning. We create several baselines and experiment with and compare various parameter settings for three algorithms, i.e. Conditional Random Fields (CRF), Support Vector Machines (SVM) and Random Forests (RF). We also evaluate the impact of lexical, syntactic, and semantic features on each of the algorithms, showing that semantics improves the performance in all cases. We test our comprehensive feature set on two corpora containing gold standard annotations of causal relations, and demonstrate the need for more gold standard data. The best performance of 79.35% F-score is achieved by CRFs when using all three feature types.

scholarly journals Automatic Identification and Dynamic Monitoring of Open-Pit Mines Based on Improved Mask R-CNN and Transfer Learning

2020 ◽  
Vol 12 (21) ◽  
pp. 3474
Author(s):  
Chunsheng Wang ◽  
Lili Chang ◽  
Lingran Zhao ◽  
Ruiqing Niu
Keyword(s):  
Transfer Learning ◽  
Google Earth ◽  
Open Pit Mine ◽  
Dynamic Monitoring ◽  
Open Pit ◽  
Practical Significance ◽  
Support Vector ◽  
Automatic Identification ◽  
Open Pit Mines ◽  
Evaluation Indexes

As the ecological problems caused by mine development become increasingly prominent, the conflict between mining activity and environmental protection is gradually intensifying. There is an urgent problem regarding how to effectively monitor mineral exploitation activities. In order to automatic identify and dynamically monitor open-pit mines of Hubei Province, an open-pit mine extraction model based on Improved Mask R-CNN (Region Convolutional Neural Network) and Transfer learning (IMRT) is proposed, a set of multi-source open-pit mine sample databases consisting of Gaofen-1, Gaofen-2 and Google Earth satellite images with a resolution of two meters is constructed, and an automatic batch production process of open-pit mine targets is designed. In this paper, pixel-based evaluation indexes and object-based evaluation indexes are used to compare the recognition effect of IMRT, faster R-CNN, Maximum Likelihood (MLE) and Support Vector Machine (SVM). The IMRT model has the best performance in Pixel Accuracy (PA), Kappa and MissingAlarm, with values of 0.9718, 0.8251 and 0.0862, respectively, which shows that the IMRT model has a better effect on open-pit mine automatic identification, and the results are also used as evaluation units of the environmental damages of the mines. The evaluation results show that level Ⅰ (serious) land occupation and destruction of key mining areas account for 34.62%, and 36.2% of topographical landscape damage approached level I. This study has great practical significance in terms of realizing the coordinated development of mines and ecological environments.

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