A view-based statistical system for multi-view face detection and pose estimation

This paper proposes a new SAR image segmentation method based on graph and gray level reduction in Independent component analysis (ICA) space. Firstly, according to the grayscale information of SAR image, effective use of gray level reduction for initial segmentation can group the pixels with same or similar values to the same homogeneous region, which can address the problem of over-segmentation. Secondly, the features of regions are extracted in ICA space, and then the similarity degree can be calculated by Euclidean distance. The initial regions are merged in fully connected graph based on minimum spanning tree in ICA space. The process of region merging is divided into two phases; the first phrase is merging the different regions with the largest similarity degree, the second will focus on updating the fully connected graph for iteration. Finally, experimental and comparative results on synthetic and real SAR images verify the efficiency of the proposed algorithm.

In order to effectively separate and identify the noise source of excavator cab, this paper adopts the methods of ensemble empirical mode decomposition (EEMD), principal component analysis (PCA), independent component analysis (ICA) and S-transform (ST) to study. Firstly, EEMD is used to decompose the single channel noise signal collected at the driver’s position into multiple intrinsic mode functions (IMF); Secondly, PCA is used to reduce the dimension of IMFs to remove the redundant IMF; Next, ICA processing is performed on the remaining IMFs to obtain independent IC components; Finally, S-transform is used to analyze the time-frequency of IC component, and the source of IC component is identified by prior experience. The results show that the main noise sources are the vibration radiation noise of related parts in the cab, the combustion noise of diesel engine and the fan noise. After identifying the main noise sources, the next step is to study the contribution of these noise sources, which provides a new idea for noise control.

This paper address new face verification scheme based on Log-Gabor filter (texture based) and Gaussian Mixture Model. The proposed method consists of three parts. The first part is a Log-Gabor filtering on facial image. The second part is to model the Log-Gabor filter response using Gaussian Mixture Model to obtain more than one set of features. The third part is transforming the set of features using subspace methods such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Thus, in this paper two methods namely Log-Gabor Mixture Model (LGMM) based on PCA and Log-Gabor Mixture Model based (LGMM) on ICA is proposed. Proposed methods are evaluated for its performance by conducting series of experiments on three image databases: ORL, AR, YALE face database. The nature of type \& size of databases chosen on the performance of these algorithms are also studied. The experimental results indicate the efficacy of the proposed methods and varied nature of results based on these algorithms.

In this paper, we investigate some issues related to realtime processing for independent component analysis (ICA), based on gradient learning with simultaneous perturbation stochastic approximation (SPSA). Real-time ICA processing is especially necessary for an application in dynamic mixing environment, since a batch type of ICA processing can work well only in a static or stationary mixing environment. Although there are many choices for an ICA object function to which SPSA can be applied, in this paper, we choose a diagonality of the nonlinear correlation matrix as our object function. Theories and implementations of the algorithm are described. Results of computer simulation are also presented to demonstrate the effectiveness.

Event-related fast optical signal in a rapid object recognition task: Improving detection by the independent component analysis

Locally linear embedding (LLE) is an unsupervised nonlinear manifold learning algorithm. It performs well in visualizing data yet has a very poor recognition rate in facial expression recognition. In this paper, to improve the performance of LLE in facial expression recognition, we first employ the independent component analysis (ICA) technique to preprocess the face images such that they are represented by some independent components and some noise is filtered from them. We then propose a supervised LLE (SLLE) algorithm to learn the hidden manifold. SLLE constructs the neighborhood graphs for the data according to the Euclidean distances between them and the cluster information of them. Its embedding step is the same as that of LLE. Finally, we use a generalized regression neural network (GRNN) to learn the implicit nonlinear mapping from the ICA space to the embedded manifold. Experiments on the JAFFE database show promising results

Not alike to principal component analysis (PCA) based monitoring statistics (T2 and SPE), the control limits for independent component analysis (ICA) monitoring statistics (I2 , Ie 2 and SPE) cannot be determined directly from a particular approximation distribution due to latent variables do not follow Gaussian distribution. Lee et al. (2004) proposed to use kernel density estimation (KDE) to obtain the control limits. However, the KDE method is very sensitive to the choice of smoothing parameter. Therefore, this study utilizes the support vector machine (SVM) for process fault detection by taking information of ICA extracted statistics as inputs of SVM. The proposed method (named, ICA-SVM) will be implemented in the Tennessee Eastman Process. In which, several multivariate monitoring schemes such as PCA, ICA, modified ICA and ICA- PCA will be also compared to demonstrate the efficiency of proposed method.

The traditional procedure for increasing the signal-tonoise ratio of NMR echo trains is based on stacking and then averaging the data acquired at different depths up to a certain depth interval. Although this works efficiently in increasing the signal-to-noise ratio, it also reduces the vertical resolution of the acquired signal. Therefore a trade-off has to be made between cancelling the noise level without deteriorating the targeted signal but yet still achieving acceptable vertical and spectral resolutions. In NMR logging technology, vertical resolution, usually assessed by NMR-total porosity compared to neutrondensity porosity, is important. However, noise can also affect the spectral distribution of the inverted signal that generates the T1 or T 2 distribution and its components like: clay-bound water, irreducible water, and free fluid. In the area of machine learning applied to denoising, relatively new algorithm developments are using approaches like ICA (Independent Component Analysis), which are based on exploring the statistical independency of the signal components. ICA in fact, puts a stronger mathematical condition on the data-independency—rather than perpendicularity (which traditional Principal Component Analysis (PCA) is based on). However, since ICA is a kind of blind source separation algorithm, the subspace of the ICA solution is usually determined by the relevant numbers of principal components obtained after running PCA. There are several ICA approaches proposed in the literature. One of the most popular is the ‘FastIca’ algorithm. We choose the FastIca algorithm for our test due to its ease of implementation in the MATLAB computational environment. The results from simulated data show that ICA-denoising can be more effective than stacking, if the numbers of the blindly defined ICA components are properly selected. The tradeoff between preserving a significant amount of signal and filtering the right amount of noise is a sensitive aspect in implementing ICA for denoising when rock-fluid characteristics are being explored. Results from real data show that ICA can be readily adapted for post-processing in log analysis, but the selection of ICA-subspace poses a major challenge.

Principal components analysis (PCA) has been widely used in many applications, particularly, data compression. Independent component analysis (ICA) has been also developed for blind source separation along with many other applications such as channel equalization, speech processing. Recently, it has been shown that the ICA can be also used for hyperspectral data compression. This paper investigates these two transforms in hyperspectral data compression and further evaluates their strengths and weaknesses in applications of target detection, mixed pixel classification and abundance quantification. In order to take advantage of the strengths of both transform, a new transform, called mixed PCA/ICA transform is developed in this paper. The idea of the proposed mixed PCA/ICA transform is derived from the fact that it can integrate different levels of information captured by the PCA and ICA. In doing so, it combines m principal components (PCs) resulting from the PCA and n independent components (ICs) generated by the ICA to form a new set of (m+n) mixed components used for hyperspectral data compression. The resulting transform is referred to as mixed (m,n)-PCA/ICA transform. In order to determine the total number of components, p needed to be generated for the mixed (m,n)-PCA/ICA transform, a recently developed virtual dimensionality (VD) is introduced to estimate the p where p = m + n. If m = p and n = 0, then mixed (m,n)-PCA/ICA transform is reduced to PCA transform. On the other hand, if m = 0 and n = p, then mixed (m,n)-PCA/ICA transform is reduced to ICA. Since various combinations of m and n have different impacts on the performance of the mixed PCA/ICA spectral/spatial compression in applications, experiments based on subpixel detection and mixed pixel quantification are conducted for performance evaluation.

Data compression on the illumination adjustable images by PCA and ICA

This paper describes the application of principal component analysis (PCA) and independent component analysis (ICA) to identify the reference spectra of a pharmaceutical tablet's constituent compounds from Raman spectroscopic data. The analysis shows, first with a simulated data set and then with data collected from a pharmaceutical tablet, that both PCA and ICA are able to identify most of the features present in the reference spectra of the constituent compounds. However, the results suggest that the ICA method may be more appropriate when attempting to identify unknown reference spectra from a sample. The resulting PCA and ICA models are subsequently used to estimate the relative concentrations of the constituent compounds and to produce spatial distribution images of the analyzed tablet. These images provide a visual representation of the spatial distribution of the constituent compounds throughout the tablet. Images associated with the ICA scores are found to be more informative and not as affected by measurement noise as the PCA based score images. The paper concludes with a discussion of the future work that needs to be undertaken for ICA to gain wider acceptance in the applied spectroscopy community.

Online pose estimation and mapping in unknown environments is essential for most mobile robots. Especially autonomous unmanned aerial vehicles require good pose estimates at comparably high frequencies. In this paper, we propose an effective system for online pose and simultaneous map estimation designed for light-weight UAVs. Our system consists of two components: (1) real-time pose estimation combining RTK-GPS and IMU at 100 Hz and (2) an effective SLAM solution running at 10 Hz using image data from an omnidirectional multi-fisheye-camera system. The SLAM procedure combines spatial resection computed based on the map that is incrementally refined through bundle adjustment and combines the image data with raw GPS observations and IMU data on keyframes. The overall system yields a real-time, georeferenced pose at 100 Hz in GPS-friendly situations. Additionally, we obtain a precise pose and feature map at 10 Hz even in cases where the GPS is not observable or underconstrained. Our system has been implemented and thoroughly tested on a 5 kg copter and yields accurate and reliable pose estimation at high frequencies. We compare the point cloud obtained by our method with a model generated from georeferenced terrestrial laser scanner.

The literature on independent component analysis (ICA)-based face recognition generally evaluates its performance using standard principal component analysis (PCA) within two architectures, ICA Architecture I and ICA Architecture II. In this correspondence, we analyze these two ICA architectures and find that ICA Architecture I involves a vertically centered PCA process (PCA I), while ICA Architecture II involves a whitened horizontally centered PCA process (PCA II). Thus, it makes sense to use these two PCA versions as baselines to reevaluate the performance of ICA-based face-recognition systems. Experiments on the FERET, AR, and AT&T face-image databases showed no significant differences between ICA Architecture I (II) and PCA I (II), although ICA Architecture I (or II) may, in some cases, significantly outperform standard PCA. It can be concluded that the performance of ICA strongly depends on the PCA process that it involves. Pure ICA projection has only a trivial effect on performance in face recognition.

Statistical analysis of wind-induced pressure fields and PIV measurements on two buildings

The authors consider the roles of PCA (principal component analysis) and ICA (independent component analysis) in the fusion of several data sources in remote sensing. With high dimensional data from each source they may be tempted to use PCA or ICA to obtain a small subset of most significant components. Smaller feature sets not only can reduce the computation requirements but also can mitigate the Hughes phenomenon. However how they use the resulting feature sets from several data sources can make significant difference in the overall classification performance. Also fusion can lead to image enhancement. Several experiments have been performed to illustrate the conditions that PCA and ICA can be most useful in data fusion.