Search results for "Pattern recognition"
showing 10 items of 2301 documents
A new shape-oriented classification method for UV/VIS-spectra
1996
A new shape-oriented classification method is described. It is shown, how shapes of UV/VIS-spectra can be classified and coded and how a classification technique can be used to improve database search operations for pre-selections or even shape-oriented identifications.
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
2016
In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of info…
Conceptual representations of actions for autonomous robots
2001
An autonomous robot involved in long and complex missions should be able to generate, update and process its own plans of action. In this perspective, it is not plausible that the meaning of the representations used by the robot is given from outside the system itself. Rather, the meaning of internal symbols must be firmly anchored to the world through the perceptual abilities and the overall activities of the robot. According to these premises, in this paper we present an approach to action representation that is based on a "conceptual" level of representation, acting as an intermediate level between symbols and data coming from sensors. Symbolic representations are interpreted by mapping …
Anchoring symbols to conceptual spaces: the case of dynamic scenarios.
2003
In recent years, there have been several proposals for the realization of models inspired to biological solutions for pattern recognition. In this work we propose a new approach, based on a hierarchical modular structure, to realize a system capable to learn by examples and recognize objects in digital images. The adopted techniques are based on multiresolution image analysis and neural networks. Performance on two different data sets and experimental timings on a single instruction multiple data (SIMD) machine are also reported.
Entropy characteristics of heart rate wavelet multiscale components in epileptic children before and after seizures
2020
In this work, we analyze the information content of the multiple time scale components of heart rate variability (HRV) in children with focal epilepsy. HRV components are extracted from 30 pediatric patients, monitored 10 min and 10 s before and after focal epileptic seizures, using wavelet multiscale decomposition (with 5, 15, 30, 60, 120, 180 s time scale), and then characterized computing Entropy (E), permutation entropy (PE), conditional entropy (CE) and information storage (IS). Moving from preictal to postictal windows, we find statistically significant differences in the CE and IS values of HRV components at short time scales, which reflect autonomic imbalance and appear as potential…
Alignment of cone beam computed tomography data using intra-oral fiducial markers.
2009
This article illustrates a new method to align and merge two partially overlapping volumes each of them generated by cone beam computed tomography (CBCT). The aggregate volume covers a larger area of investigation and is determined by localizing one fixed LEGO brick in both of the primal volumes. Based on the LEGO brick an approximate registration of the volumes is determined. Afterwards we improve the transformation by minimizing the difference in overlapping space. In this paper we present a method which automates these two steps and provides an aligned volume.
Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of t…
2019
Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage. Besides invasive diagnosis of SCC by means of biopsy and histo-pathologic assessment, Confocal Laser Endomicroscopy (CLE) has emerged as noninvasive method that was successfully used to diagnose SCC in vivo. For interpretation of CLE images, however, extensive training is required, which limits its applicability and use in clinical practice of the method. To aid diagnosis of SCC in a broader scope, automatic detection methods have been proposed. This work compares two methods with regard to their applicability in a transfer learning sense, i.e. training on one tissue type (f…
On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases
2011
Published version of an article in the journal: Pattern Analysis and Applications. Also available from the publisher at: http://dx.doi.org/10.1007/s10044-011-0199-9 We consider the micro-aggregation problem which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the micro-data file, is known to be NP-hard, and has been tackled using many heuristic solutions. In this paper, we would like to demonstrate that in the process of developing micro-aggregation techniques (MATs), it is expedient to incorporate information about the dependence between the random variable…
A PARALLEL ALGORITHM FOR ANALYZING CONNECTED COMPONENTS IN BINARY IMAGES
1992
In this paper, a parallel algorithm for analyzing connected components in binary images is described. It is based on the extension of the Cylindrical Algebraic Decomposition (CAD) to a two-dimensional (2D) discrete space. This extension allows us to find the number of connected components, to determine their connectivity degree, and to solve the visibility problem. The parallel implementation of the algorithm is outlined and its time/space complexity is given.
Two-view “cylindrical decomposition” of binary images
2001
This paper describes the discrete cylindrical algebraic decomposition (DCAD) construction along two orthogonal views of binary images. The combination of two information is used to avoid ambiguities for image recognition purposes. This algorithm associates an object connectivity graph to each connected component, allowing a complete description of the structuring information. Moreover, an easy and compact representation of the scene is achieved by using strings in a five letter alphabet. Examples on complex digital images are also provided. © 2001 Elsevier Science Inc.