Principal component analysis (PCA) has been generalized to complex principal component analysis (CPCA), which has been widely applied to complex-valued data, two-dimensional vector fields, and complexified real data through the Hilbert transform. Nonlinear PCA (NLPCA) can also be performed using auto-associative feed-forward neural network (NN) models, which allows the extraction of nonlinear f...

This paper presents a comparative approach for Content Based Image Retrieval (CBIR) using Scale Invariant Feature Transform (SIFT) algorithm and Principal Component Analysis (PCA) for color images. The motivation to use SIFT algorithm for CBIR is due to the fact that SIFT is invariant to scale, rotation and translation as well as partially invariant to affine distortion and illumination changes...

In this paper several results concerning static hand gesture recognition using an algorithm based on left-right Hidden Markov Models (HMM) are presented. The features used as observables in the training as well as in the recognition phases are based either on the 2D Discrete Cosine Transform (DCT) or on the Principal Component Analysis (PCA). The left-right topology of the HMM together with the...

In computer vision, detection and tracking of targets is very complex problem and demands sophisticated solutions. Unmanned Aerial Vehicles (UAVs) are increasingly being used for reconnaissance and Surveillance. This framework mainly consists of image matching for reconnaissance and Surveillance. This framework mainly consists of image matching for Digital Scene Matching Area Correlation (DSMAC...

Remote sensing image fusion is an effective way to use a large volume of data from multisensor images. Most earth satellites such as SPOT, Landsat 7, IKONOS and QuickBird provide both panchromatic (Pan) images at a higher spatial resolution and multispectral (MS) images at a lower spatial resolution and many remote sensing applications require both high spatial and high spectral resolutions, es...

a r t i c l e i n f o Keywords: Non-negative principal component analysis Local feature representation k + 1 rule NMR-based metabolomics Multivariate data analysis Proton nuclear magnetic resonance (1 H-NMR) spectroscopy is one of the major analytical platforms used in metabolomics. The data acquired from NMR experiments are frequently processed using multivariate statistical methods such as pr...

In face recognition, Principal Component Analysis (PCA) is often used to extract a low dimensional face representation based on the eigenvector of the face image autocorrelation matrix. Kernel Principal Component Analysis (Kernel PCA) has recently been proposed as a non-linear extension of PCA. While PCA is able to discover and represent linearly embedded manifolds, Kernel PCA can extract low d...

Principal component analysis (PCA) is a ubiquitous statistical technique for data analysis. PCA is however limited by its linearity and may sometimes be too simple for dealing with real-world data especially when the relations among variables are nonlinear. Recent years have witnessed the emergence of nonlinear generalizations of PCA, as for instance nonlinear principal component analysis (NLPC...

In this paper a novel face recognition approach based on Adaptive Principal Component Analysis (APCA) and de-noised database is produced. The aim of our approach is to overcome PCA disadvantages especially the two limitations of discriminatory power poverty and the computational load complexity, by producing a new adaptive PCA based on single level 2-D discrete wavelet transform using Daubachie...