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Interest on palmprint biometrics has experimented a strong growth in the last decades due to its useful characteristics as uniqueness, permanence, reliability, user-friendliness, acceptability, non-intrusiveness, and low cost of the acquisition devices, which make it attractive for civil and commercial applications. Accordingly, a wide research has been developed in this field. Nevertheless, there...
Wearable and distributed sensors are attractive for monitoring of gases for environmental surveillance, industrial safety, and other applications. For these sensors to be of high value, they need suitable detection accuracy in complex backgrounds. Here, we show accurate detection of volatiles using low-cost sensors based on a radio-frequency identification (RFID) platform. Our design and excitation...
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...
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)...
Urban expansion monitoring and organization can be performed through space-based observation thanks to the revisit time and level of details guaranteed by satellite remote sensing. In particular, the Landsat mission products are the most used thanks to the long time coverage and open access policy. This paper proposed a hybrid method — combination of pixel- and object-based analysis — in order to...
Remote sensing data have been commonly used for agricultural crop monitoring. This paper was assessed the quality of using SAR and optical data fusion for maize classification. Two different SAR data sets from different sensors including dual polarization (HH and VV) X-band COSMO-SkyMed (CSK) and quad polarization (HH, HV, VH and VV) C-band RADARSAT-2 images were fused with THAICHOTE (namely, THEOS,...
The gesture recognition using motion capture data and depth sensors has recently drawn more attention in vision recognition. Currently most systems only classify dataset with a couple of dozens different actions. Moreover, feature extraction from the data is often computational complex. In this paper, we propose a novel system to recognize the actions from skeleton data with simple, but effective,...
Principal component analysis (PCA) is a commonly used method for feature extraction and dimensionality reduction. This paper proposes PCA based on similarity/correlation criteria instead of covariance to gain low-dimensional features with high performance in text classification. Experimental results have demonstrated the advantages and usefulness of the proposed method in text classification in high-dimensional...
In real environment, the protocol distribution of Network traffic is imbalance, and the generalization ability of supervised learning algorithm such as algorithm to C4.5 is poor. In order to improve the classification accuracy and stability of network traffic, a network traffic classification method based on Rotation Forest was proposed. In the method, PCA was used for feature reduction and C4.5 algorithm...
This paper develops an inverse scattering method to determine the characteristics of resonance region targets by using ultrawideband scattering signals. The method mainly uses multiple time-domain signals for each target, and they are processed to give feature vectors in frequency domain. These vectors contain target-specific information related with characteristics (dimensions of substructures, material...
In recent power grid systems, data-driven approach has been taken to grid condition evaluation and classification after successful adoption of big data techniques in internet applications. However, the raw training data from single monitoring system, e.g. dissolved gas analysis (DGA), are rarely sufficient for training in the form of valid instances and the data quality can rarely meet the requirement...
This paper presents hardware constraints analysis of Gabor filtering operation for its hardware implementation in a real time Facial Expression Recognition System (FERS). Gabor filter is the most common feature extractor employed for the realization of such system. Feature extraction using Gabor filter is efficient and has better discrimination capability. In this work, we have employed software-based...
Pittsburgh compound B Positron Emission Tomography (PiB PET) imaging is a new technique to detect amyloid-beta (Aβ). Aβ is a pathological bio-data which appears distinctly in most neuro-degeneration diseases, such as Alzheimer's disease (AD). Although PiB PET imaging is relative mature, the accurate diagnosis of AD based on PiB PET images still remains a challenge for radiologists. To solve above...
For the purpose of discovering White Dwarf + Main Sequence (WDMS) from massive spectra, in this paper, an unsupervised learning algorithm for Nonlinear Dimensionality Reduction (NLDR) named Isometric Feature Mapping (Isomap) is discussed. The applicability of Isomap to Sloan Digital Sky Survey Data Release 10 (SDSS-DR10) is confirmed. Furthermore, Particle Swarm Optimization (PSO) is implemented to...
A face recognition algorithm based on a newly developed Transform Domain Mutual Principal Component Analysis (TD-2D-MuPCA) approach is proposed. In this approach, the spatial facial two-dimensional images (2D) and their division into horizontal, vertical and diagonal sub-images halves are generated. The sub-image halves are processed using non-overlapping and overlapping windows. Each face and its...
In data mining, a well known problem of “Curse of Dimensionality” occurs due to presence of large number of dimensions in a dataset. This problem leads to reduced accuracy of machine learning classifiers because of presence of many insignificant and irrelevant dimensions or features in the dataset. Data mining applications such as bioinformatics, risk management, forensics etc., generally involves...
Laser induced breakdown spectroscopy (LIBS) is an atomic emission based spectroscopy that uses a laser pulse as the source of excitation. The laser is focused to form hot plasma, which atomizes and excites the sample. In the LIBS spectrum each “feature” is the amplitude or intensity detected at different wavelengths in the range of 200–1000 nm. Pattern recognition techniques were applied on samples...
We propose to address the handwritten digits recognition (HWDR) problem by using a two-dimensional (2-D) discrete cosine transform (DCT) based sparse principal component analysis (PCA) algorithm for fast classification. The gain of processing speed is achieved by utilizing the ability of 2-D DCT for energy compaction and signal decorrelation. The proposed algorithm was applied to the mixed national...
Histograms of Oriented Gradients (HOG) feature has been successfully used in pedestrian detection and achieves high accuracy. This paper introduces a content retrieval algorithm based on improved HOG. The method has two steps which are adjusting the HOG structure by scanning the image with a sliding HOG window and reducing feature dimension by principle component analysis (PCA) technique. The experimental...
Hyperspectral image classification based on low-rank representation is considered. It is often assumed that major signals occupy a low-rank subspace, and the remaining component is sparse. Due to the mixed nature of hyperspectral data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose to use low-rank subspace representation...
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