Search results for "DIMENSION"
showing 10 items of 2766 documents
Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods
2006
We describe some applications of linear and nonlinear pro- jection methods in order to reduce the number of spectral bands in Land- sat multispectral images. The nonlinear method is curvilinear component analysis CCA, and we propose an adapted optimization of it for image processing, based on the use of principal-component analysis PCA, a linear method. The principle of CCA consists in reproducing the topol- ogy of the original space projection points in a reduced subspace, keep- ing the maximum of information. Our conclusions are: CCA is an im- provement for dimension reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimization through PCA called CCAinitP…
Automatic Image Annotation Using Random Projection in a Conceptual Space Induced from Data
2018
The main drawback of a detailed representation of visual content, whatever is its origin, is that significant features are very high dimensional. To keep the problem tractable while preserving the semantic content, a dimen- sionality reduction of the data is needed. We propose the Random Projection techniques to reduce the dimensionality. Even though this technique is sub-optimal with respect to Singular Value Decomposition its much lower computational cost make it more suitable for this problem and in par- ticular when computational resources are limited such as in mobile terminals. In this paper we present the use of a "conceptual" space, automatically induced from data, to perform automa…
Manifold Learning with High Dimensional Model Representations
2020
Manifold learning methods are very efficient methods for hyperspectral image (HSI) analysis but, unless specifically designed, they cannot provide an explicit embedding map readily applicable to out-of-sample data. A common assumption to deal with the problem is that the transformation between the high input dimensional space and the (typically low) latent space is linear. This is a particularly strong assumption, especially when dealing with hyperspectral images due to the well-known nonlinear nature of the data. To address this problem, a manifold learning method based on High Dimensional Model Representation (HDMR) is proposed, which enables to present a nonlinear embedding function to p…
Intelligent system for material quality control using impact-echo testing
2008
This paper introduces an intelligent system to discern the quality of materials inspected by the impact-echo technique. The system includes a hardware setup to inspect parallelepiped-shape materials and a procedure to classify the material depending on its quality condition. Four levels of classification with different grades of knowledge about the material defects are approached: material condition, kind of defect, defect orientation, and defect dimension. The number of classes (material qualities) in the lowest classification level is 12. The procedure is applied on signals coming from 3D finite element simulations and lab experiments with aluminium specimens. The classification procedure…
Adding Synthetic Detail to Natural Terrain Using a Wavelet Approach
2002
Terrain representation is a basic topic in the field of interactive graphics. The amount of data required for good quality terrain representation offers an important challenge to developers of such systems. For users of these applications the accuracy of geographical data is less important than their natural visual appearance. This makes it possible to mantain a limited geographical data base for the system and to extend it generating synthetic data.In this paper we combine fractal and wavelet theories to provide extra data which keeps the natural essence of actual information available. The new levels of detail(LOD) for the terrain are obtained applying an inverse Wavelet Transform (WT) to…
Dimension Estimation in Two-Dimensional PCA
2021
We propose an automated way of determining the optimal number of low-rank components in dimension reduction of image data. The method is based on the combination of two-dimensional principal component analysis and an augmentation estimator proposed recently in the literature. Intuitively, the main idea is to combine a scree plot with information extracted from the eigenvectors of a variation matrix. Simulation studies show that the method provides accurate estimates and a demonstration with a finger data set showcases its performance in practice. peerReviewed
Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition
2019
Real-world data exhibiting high order/dimensionality and various couplings are linked to each other since they share some common characteristics. Coupled tensor decomposition has become a popular technique for group analysis in recent years, especially for simultaneous analysis of multi-block tensor data with common information. To address the multiblock tensor data, we propose a fast double-coupled nonnegative Canonical Polyadic Decomposition (FDC-NCPD) algorithm in this study, based on the linked CP tensor decomposition (LCPTD) model and fast Hierarchical Alternating Least Squares (Fast-HALS) algorithm. The proposed FDCNCPD algorithm enables simultaneous extraction of common components, i…
Evaluation of the registration of temporal series of contrast-enhanced perfusion magnetic resonance 3D images of the liver.
2011
The registration of 2D and 3D images is one of the key tasks in medical image processing and analysis. Accurate registration is a crucial preprocessing step for many tasks; consequently, the evaluation of its accuracy becomes necessary. Unfortunately, this is a difficult task, especially when no golden pattern (true result) is available and when the signal values may have changed between successive images to be registered. This is the case this paper deals with: we have a series of 3D images, magnetic resonance images (MRI) of the liver and adjacent areas that have to be registered. They have been taken while a contrast is diffused through the liver tissue, so intensity of each observed poi…
Morse Description and Geometric Encoding of Digital Elevation Maps
2004
Two complementary geometric structures for the topographic representation of an image are developed in this work. The first one computes a description of the Morse-topological structure of the image, while the second one computes a simplified version of its drainage structure. The topographic significance of the Morse and drainage structures of digital elevation maps (DEMs) suggests that they can been used as the basis of an efficient encoding scheme. As an application, we combine this geometric representation with an interpolation algorithm and lossless data compression schemes to develop a compression scheme for DEMs. This algorithm achieves high compression while controlling the maximum …
Thermal properties in low dimensional structures below 1 K
2009
In this thesis thermal properties of low dimensional structures were experimentally studied at low temperatures with the help of tunnel junction thermometry and the Joule heating technique. The main objects of study were electron-phonon coupling in disordered thin metal films and phonon transport in suspended silicon nitride membranes. Our aim has been to clarify the effect of the phonon dimensionality, i.e. the effect of boundaries to the phonon modes and the transition from 3D to 2D phonons. The dimensionality cross over had not been observed before this work even though it is fabricationally a standard procedure to create the low dimensional environments for nanoscale applications and de…