Search results for "Preprocess"
showing 10 items of 54 documents
A Fuzzy One Class Classifier for Multi Layer Model
2009
The paper describes an application of a fuzzy one-class classifier (FOC ) for the identification of different signal patterns embedded in a noise structured background. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM ) that provides a preliminary signal segmentation in an interval feature space. The FOC has been tested on synthetic and real microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.
An Approach to Enhance Chatbot Semantic Power and Maintainability: Experiences within the FRASI Project
2012
The paper illustrates the implementation and semantic enhancement of a domain-oriented Question-Answering system based on a pattern-matching chat bot technology, developed within an industrial project, named FRASI. The main difficulty in building a KB for a chat bot is to handwrite all possible question-answer pairs that constitute the KB. The proposed approach simplifies the chat bot realization thanks to two solutions. The first one uses an ontology, which is exploited in a twofold manner: to construct dynamic answers as a result of an inference process about the domain, and to automatically populate, off-line, the chat bot KB with sentences that can be derived from the ontology, describi…
Using mathematical morphology for unsupervised classification of functional data
2011
This paper is concerned with the unsupervised classification of functional data by using mathematical morphology. Different morphological operators are used to extract relevant structures of the functions (considered as sets through their subgraph representations). These operators can be considered as preprocessing tools whose outputs are also functional data. We explore some dissimilarity measures and clustering methods for the classification of the transformed data. Our approach is illustrated through a detailed analysis of two data sets. These techniques, which have mainly been used in image processing, provide a flexible and robust toolbox for improving the results in unsupervised funct…
Multiple SIP strategies and bottom-up adorning in logic query optimization
1990
Preprocessing methods called “readorning” and “bottom-up adorning” are introduced as means of enlarging the application domain of magic sets and related query optimization strategies for logic databases. Readorning tries to make possible the simultaneous use of multiple sideways information passing (sip) strategies defined for a rule, thus yielding an optimization effect that may not be achieved by any particular choice of sip strategies. Bottom-up adorning is used to make magic sets applicable to cases in which potential optimizations can be derived from bindings coming upwards from rule bodies to rule heads in bottom-up evaluation. These include the cases in which we know that some base r…
Preprocessing of region of interest localization based on local surface curvature analysis for three-dimensional reconstruction with multiresolution
2009
We present an approach to integrate a preprocessing step of the region of interest ROI localization into 3-D scanners laser or ste- reoscopic. The definite objective is to make the 3-D scanner intelligent enough to localize rapidly in the scene, during the preprocessing phase, the regions with high surface curvature, so that precise scanning will be done only in these regions instead of in the whole scene. In this way, the scanning time can be largely reduced, and the results contain only per- tinent data. To test its feasibility and efficiency, we simulated the prepro- cessing process under an active stereoscopic system composed of two cameras and a video projector. The ROI localization is…
LDR Image to HDR Image Mapping with Overexposure Preprocessing
2013
International audience; Due to the growing popularity of High Dynamic Range (HDR) images and HDR displays, a large amount of existing Low Dynamic Range (LDR) images are required to be converted to HDR format to benefit HDR advantages, which give rise to some LDR to HDR algorithms. Most of these algorithms especially tackle overexposed areas during expanding, which is the potential to make the image quality worse than that before processing and introduces artifacts. To dispel these problems, we . present a new,LDR to HDR approach, unlike the existing techniques, it focuses on avoiding sophisticated treatment to overexposed areas in dynamic range expansion step. Based on a separating principl…
Fast 3D Ray Tracing for Indoor Coverage Solutions
2016
Optimal wireless indoor network planning requires huge number of iterations and evaluations of indoor coverage for each antenna location until an optimal solution is reached. Consequently, accurate and scalable calculation of power strength for indoor scenarios becomes necessary. The contribution in this paper is to reduce the complexity of 3D ray tracing for deterministic indoor power prediction. In order to achieve that while preserving the accuracy, image theory with feasible reflection volume as preprocessing approach is introduced. This proposed algorithm stores the image, its feasible reflection volume and valid area of receiving points. Significant complexity reduction is achieved by…
Remote Sensing Geometric Corrections
2016
This article reviews the different aspects of geometrical processing of remote sensing data, discussing error sources and methods to determine the transformation from the image acquisition geometry to the output cartographic product. Resampling methods are discussed to transform the input image to the output geometry. Several practical examples of remote sensing platforms are discussed, including satellite cases and airborne sensors. Validation of the resulting geometrical processed products is also discussed. Spatial mosaicking techniques and multitemporal composites used to produce multisource integrated products and advanced applications are finally considered, keeping a perspective on t…
Applying fully tensorial ICA to fMRI data
2016
There are two aspects in functional magnetic resonance imaging (fMRI) data that make them awkward to analyse with traditional multivariate methods - high order and high dimension. The first of these refers to the tensorial nature of observations as array-valued elements instead of vectors. Although this can be circumvented by vectorizing the array, doing so simultaneously loses all the structural information in the original observations. The second aspect refers to the high dimensionality along each dimension making the concept of dimension reduction a valuable tool in the processing of fMRI data. Different methods of tensor dimension reduction are currently gaining popUlarity in literature…
Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection
2016
International audience; This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various pre-processings in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and non-linear cl…