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Computers and Smartphone's becomes vital part of everyday life and hence use of internet becomes more and more. Due to internet, computers are becomes vulnerable of different kinds of security threats. Therefore it is required that we need to have efficient security method in order to avoid leakage of important data or misuse of data. This security method is called as Intrusion Detection System (IDS)...
Fabric defect inspection is the pivotal part in the production of textile products. Since manual inspection is tedious and erroneous, automated fabric inspection has been topic of research for past years. Automation of fabric inspection involves two major aspects: defect detection and defect classification. We focused on classifying defects based on geometric features of defects. The features are...
Conventionally small area topographic detail plan is produced using land survey techniques. Various method of modern topographical detail plan however required more cost to speed up the data acquisition process for large data and high accuracy. The recent development in commercial grade Unmanned Aerial Vehicle (UAV) has the potential to be used as a data capture equipment with low cost. In this study,...
Brain-computer Interfaces (BCIs) are control and communication systems based on acquisition and processing of brain signals to control a computer or an external device. Usually, BCI is focused in recognizing acquired events by different neuroimage methods, but the most used is the electroencephalography (EEG). Feature extraction over EEG signals for BCI systems is crucial to the classification performance...
Feature learning algorithms aim to provide a compact and discriminative representation of complex datasets in order to increase the speed and accuracy of clustering or classification. In this paper, we propose a novel interactive feature learning approach which is mainly based on 3D interactive data visualization and Non-negative Matrix Factorization (NMF). Here, the data is visualized in a 3D interface...
For hyperspectral image classification, feature extraction is a crucial pre-process for avoiding the Hughes phenomena. Some feature extraction methods such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE), and their kernel versions, generalized discriminant analysis (GDA) and kernel nonparametric weighted feature extraction method (KNWFE) have been shown that...
We propose a superpixel-based composite kernel framework for hyperspectral image (HSI) classification. Composite kernel methods can utilize both the spectral and the spatial information for the HSI classification. However, setting the optimal spatial neighborhood for different spatial structures is a non-trivial issue. In order to adaptively exploit the spatial contextual information, we utilize superpixel...
Blind hyperspectral unmixing jointly estimates both the endmembers and the abundances of hyperspectral images. The endmembers represent the spectral signatures of material found in the image and the abundances specify the amount of each material seen in each pixel in the image. In this paper, a blind hyperspectral unmixing method for feature extraction and classification using total variation (TV)...
A general approach or framework is proposed for multi-sensor data fusion using template based matching (TBM). The main advantage of TBM is that it allows defining features/templates using a priori information from the scene/image. The approach works as follows: first quite complex features e.g. roundabouts/junctions are extracted in the optical image, then these features are simulated in SAR and finally...
Recently, a fast density peak-based clustering algorithm, namely FDPC, has demonstrated its power on nonspherical clustering problems. In this paper, we propose an enhanced fast density peak-based clustering, namely E-FDPC, for hy-perspectral band selection. The main contributions of the proposed E-FDPC, in comparison with the original FDPC are two folds. First, we introduce a parameter to control...
In hyperspectral image analysis, the classification task has generally been discussed with dimensionality reduction due to high correlation and noise between the spectral features, which might cause significantly low classification performance. In supervised classification, limited training samples in proportion to the number of spectral features have also negative impacts on the classification accuracy,...
Image registration technology is getting more and more important in nowadays. The accuracy of the registration resolution plays an important. In this passage, we present an image registration algorithm based on point feature of sub-pixel, which can improve the accuracy. Firstly we use Harris corner point algorithm to get point features, then we use our method to refine the Harris points, after that...
Rough set theory is a paradigm to deal with uncertainty, vagueness, and incompleteness of data. Although it has been applied successfully to feature selection in different application domains, it is seldom used for the analysis of hyperspectral images. In this paper, a rough set based supervised method is proposed to select informative bands in hyperspectral images. The proposed technique exploits...
This paper presents a novel classification method for high-spatial-resolution satellite scene classification introducing multiset aggregated canonical correlation analysis (MACCA)-based feature fusion to fuse and combine multiple features. Firstly, a superpixel representation of the scene is constructed by employing a high-efficiency linear iterative clustering algorithm. After that, three diverse...
In this paper, an ontology-based framework of China Geographic National Conditions Monitoring (CGNCM) land cover extraction is presented. Using statistical analysis method, spectral, texture, spatial features of each land cover class are extracted from the referenced ZY-3 image acquired in summer, 2012. Stored in the form of OWL, the features of one class can be considered as the prototype of this...
We present a new approach for remote sensing image classification. The methodology combines many related tasks namely non linear source separation, feature extraction, feature fusion and learning classification. Nonlinear source separation is a pre-processing stage that aims to compensate the nonlinear mixing natural phenomenon. Latent signals, called sources are transformed to the feature presentation...
Flood is the most frequent disaster in the world, which can do harm to agriculture and threat to food security. Using kernel based supervised classifier to execute change detection for multi-temporal remote sensing data is a common method for flood disaster monitoring and assessment, and kernel Fisher's discrimination analysis (KFDA) is one of them. Choosing training sample by visual interpretation...
Feature extraction is at the core of satellite scene classification task. In this paper, we propose a fast binary coding (FBC) method to effectively generate the global discriminative feature representation of image scenes. Equipped with unsupervised feature learning technique, we first learn a set of optimal “filters” from large quantities of randomly sampled image patches, and then we obtain feature...
Different types of classifiers were investigated in the context of classification of problem tickets in the Enterprise domain. There were still challenges in building an accurate classifier post data cleaning and other accuracy improving pre-processing techniques. Creating an ensemble of classifiers gave better accuracy than individual classifiers. The maximum accuracy was got by enhancing the ensemble...
The paper describes an experimental study on emotion recognition using a collection of emotional recordings from SRoL corpus. Its goal is to study and to obtain a simple tool that can be used in recordings validation in the process of building large voice corpora. The tools can help or even replace the human validation. In this study we used two classifiers, k-NN (k — Nearest Neighborhood) and SVM...
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