Search results for "Pattern"
showing 10 items of 4203 documents
Damage identification by Lévy ant colony optimization
2010
This paper deals with the identification of incipient damage in structural elements by non-destructive test based on experimentally measured structural dynamical response. By applycation of the Hilbert transform to the recorded signal the so-called phase of the analytical signal is recovered and a proper functional is constructed in such a way that its global minimum gives a measure of the damage level, meant as stiffness reduction. Minimization is achieved by applying a modified Ant Colony Optimization (ACO) for continuous variables, inspired by the ants’ forageing behavior. The modification consists in the application of a new perturbation operator, based on alpha stable Lévy distribution…
Special issue on pattern recognition techniques in data mining
2017
Peer Reviewed
Symmetry operators in computer vision
1996
Abstract Symmetry plays a remarkable role in perception problems. For example, peaks of brain activity are measured in correspondence with visual patterns showing symmetry . Relevance of symmetry in vision was already noted by Koler in 1929. Here, properties of a symmetry operator are reported and a new algorithm to measure local symmetries is proposed. Its performance is tested on segmentation of complex visual patterns and the classification of sparse images.
<title>Expanding context against weighted voting of classifiers</title>
2000
In the paper we propose a new method to integrate the predictions of multiple classifiers for Data Mining and Machine Learning tasks. The method assumes that each classifier stands in it's own context, and the contexts are partially ordered. The order is defined by monotonous quality function that maps each context to the value from the interval [0,1]. The classifier that has the context with better quality is supposed to predict better than the classifier from worse quality. The objective is to generate the opinion of `virtual' classifier that stands in the context with quality equal to 1. This virtual classifier must have the best accuracy of predictions due to the best context. To do thi…
Hybrid 3D-ResNet Deep Learning Model for Automatic Segmentation of Thoracic Organs at Risk in CT Images
2020
In image radiation therapy, accurate segmentation of organs at risk (OARs) is a very essential task and has clinical applications in cancer treatment. The segmentation of organs close to lung, breast, or esophageal cancer is a routine and time-consuming process. The automatic segmentation of organs at risk would be an essential part of treatment planning for patients suffering radiotherapy. The position and shape variation, morphology inherent and low soft tissue contrast between neighboring organs across each patient’s scans is the challenging task for automatic segmentation of OARs in Computed Tomography (CT) images. The objective of this paper is to use automatic segmentation of the orga…
Research production in high-impact journals of contemporary neuroscience: A gender analysis
2017
Neuroscience or Neural Science is a very active and interdisciplinary field that seeks to understand the brain and the nervous system. In spite of important advances made in recent decades, women are still underrepresented in neuroscience research output as a consequence of gender inequality in science overall. This study carries out a scientometric analysis of the 30 neuroscience journals (2009–2010) with the highest impact in the Web of Science database (Thomson Reuters) in order to quantitatively examine the current contribution of women in neuroscientific production, their pattern of research collaboration, scientific content, and the analysis of scientific impact from a gender perspect…
Motion estimation and reconstruction of piecewise planar scenes from two views
2010
The task of recovering the camera motion relative to the environment (motion estimation) is fundamental to many computer vision applications. We present an algorithm for reconstruction of piece-wise planar scenes from only two views and based on minimum line correspondences. We first recover camera rotation by matching vanishing points based on the methods already exist in the literature and then recover the camera translation by searching among a family of hypothesized planes passing through one line. Unlike algorithms based on line segments, the presented algorithm does not require an overlap between two line segments or more than one line correspondence across more than two views to reco…
Splitting criterion for hierarchical motion estimation based on perceptual coding
1998
A new entropy-constrained motion estimation scheme using variable-size block matching is proposed. It is known that fixed-size block matching as used in most video codec standards is improved by using a multiresolution or multigrid approach. In this work, it is shown that further improvement is possible in terms of both the final bit rate achieved and the robustness of the predicted motion field if perceptual coding is taken into account in the motion estimation phase. The proposed scheme is compared against other variable- and fixed-size block matching algorithms.
Diversity in search strategies for ensemble feature selection
2005
Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subse…
Robustness of texture parameters for color texture analysis
2006
This article proposes to deal with noisy and variable size color textures. It also proposes to deal with quantization methods and to see how such methods change final results. The method we use to analyze the robustness of the textures consists of an auto-classification of modified textures. Texture parameters are computed for a set of original texture samples and stored into a database. Such a database is created for each quantization method. Textures from the set of original samples are then modified, eventually quantized and classified according to classes determined from a precomputed database. A classification is considered incorrect if the original texture is not retrieved. This metho…