Search results for "reduction"
showing 10 items of 2058 documents
Emulation of 2D Hydrodynamic Flood Simulations at Catchment Scale Using ANN and SVR
2021
Two-dimensional (2D) hydrodynamic models are one of the most widely used tools for flood modeling practices and risk estimation. The 2D models provide accurate results
Architecture and Language for Semantic Reduction of Domain-Specific Models in BPMS
2012
Nowadays each business process management system (BPMS) supports either an industry standard or its own specific modeling language. But no BPMS supports a specific language for each organization. We propose an architecture for building BPMS that allows creating a domain-specific modeling language for every client easily. The main problem is to bridge the gap between the domain-specific language and the executable language. We show that we can look at this problem as a classification of the domain-specific language constructs in the terms of the executable language. To solve this problem we present a novel model transformation language, with which this type of problem can be solved more natu…
A multi-process system for HEp-2 cells classification based on SVM
2016
An automatic system for pre-segmented IIF images analysis was developed.A non-standard pipeline for supervised image classification was adopted.The system uses a two-level pyramid to retain some spatial information.From each cell image 216 features are extracted.15 SVM classifiers one-against-one have been implemented. This study addresses the classification problem of the HEp-2 cells using indirect immunofluorescence (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Recently, studies have shown that it is possible to identify the cell patterns using IIF image analysis and machine learning techniques. In this paper we de…
Hand Detection and Tracking Using the Skeleton of the Blob for Medical Rehabilitation Applications
2012
This article presents an image processing application for hand detection and tracking using the 4-connected skeleton of the segmentation mask. The system has been designed to be used with techniques of virtual reality to develop an interactive application for phantom limb pain reduction in therapeutic treatments. One of the major contributions is the design of a fast and accurate skeleton extractor, that has proven to be faster than those available in the literature. The skeleton allows the system to precisely detect the position of all the interest points of the hand (namely the fingers and the hand center). The system, composed of both the hand detector and tracker, and the virtual realit…
Texture Classification with Generalized Fourier Descriptors in Dimensionality Reduction Context: An Overview Exploration
2008
In the context of texture classification, this article explores the capacity and the performance of some combinations of feature extraction, linear and nonlinear dimensionality reduction techniques and several kinds of classification methods. The performances are evaluated and compared in term of classification error. In order to test our texture classification protocol, the experiment carried out images from two different sources, the well known Brodatz database and our leaf texture images database.
Local Feature Selection with Dynamic Integration of Classifiers
2000
Multidimensional data is often feature space heterogeneous so that individual features have unequal importance in different sub areas of the feature space. This motivates to search for a technique that provides a strategic splitting of the instance space being able to identify the best subset of features for each instance to be classified. Our technique applies the wrapper approach where a classification algorithm is used as an evaluation function to differentiate between different feature subsets. In order to make the feature selection local, we apply the recent technique for dynamic integration of classifiers. This allows to determine which classifier and which feature subset should be us…
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…
Quality-preserving low-cost probabilistic 3D denoising with applications to Computed Tomography
2021
AbstractWe propose a pipeline for a synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular Deep Learning denoising approaches, wavelets-based methods, methods based on Mumford-Shah denoising etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel probabilistic Mumford-Shah denoising model (PMS), showing that it markedly-outperforms the compared common denoising methods…
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…