Search results for "segmentation"

Change-points detection for variance piecewise constant models

2011

A new approach based on the fit of a generalized linear regression model is introduced for detecting change-points in the variance of heteroscedastic Gaussian variables, with piecewise constant variance function. This approach overcome some limitations of both exact and approximate well-known methods that are based on successive application of search and tend to overestimate the real number of changes in the variance of the series. The proposed method just requires the computation of a gamma GLM with log-link, resulting in a very efficient algorithm even with large sample size and many change points to be estimated.

Resuming Shapes with Applications

2004

Many image processing tasks need some kind of average of different shapes. Frequently, different shapes obtained from several images have to be summarized. If these shapes can be considered as different realizations of a given random compact set, then the natural summaries are the different mean sets proposed in the literature. In this paper, new mean sets are defined by using the basic transformations of Mathematical Morphology (dilation, erosion, opening and closing). These new definitions can be considered, under some additional assumptions, as particular cases of the distance average of Baddeley and Molchanov. The use of the former and new mean sets as summary descriptors of shapes is i…

Registration and fusion of segmented left atrium CT images with CARTO electrical maps for the ablative treatment of atrial fibrillation

2005

This study aims to extract the interior surface of the left atrium (LA) and pulmonary veins (PVs) from threedimensional tomographic data and to integrate it with LA CARTO electrical maps. The separation of LA and PVs from other overlapping structures of the heart was performed processing 3D CT data by marker-controlled watershed segmentation and surface extraction. CARTO maps were then registered on the L A internal surface by a stochastic optimization algorithm based on simulated annealing. The residual registration error resulted inferior to 3 mm. The integration between electrophysiological and high resolved anatomic information of LA results feasible and may constitute a significant sup…

Museum visitors’ heterogeneity and experience processing

2019

Abstract This research examines the relationships between affective and cognitive antecedents and consequences of satisfaction under a market heterogeneity approach. It includes co-creation of preparatory activities. The sample consisted of 276 museum visitors in London. Two analysis have been conducted: structural equation model and latent class path analysis. The paper contributes to the development of a theoretical framework for further understanding of service experience in which co-creation plays an important role. Two segments were identified: 1) emotional (with lower degree of co-creation, equally distributed by age and nationality); 2) rational (higher degree of co-creation, younger…

Individual Decisions and Perceived Form in Collective Free Improvisation

2015

International audience; This paper proposes a first attempt to study quantitatively Collective Free Improvisation (CFI). We report an experiment designed to study the relationship between the improvisers’ individual high-level decisions and the improvisation’s form perceived by external auditors. We recorded 16 trios in which the improvisers used a MIDI-pedal to indicate in real-time a significant change in their own musical production. Expert listeners were later asked to segment each improvisation so that we obtained their perception of the structure. By analysing the correlations between musicians’ individual decisions and listeners’ segmentation points, we discuss how the improvisation’…

Including invariances in SVM remote sensing image classification

2012

This paper introduces a simple method to include invariances in support vector machine (SVM) for remote sensing image classification. We rely on the concept of virtual support vectors, by which the SVM is trained with both the selected support vectors and synthetic examples encoding the invariance of interest. The algorithm is very simple and effective, as demonstrated in two particularly interesting examples: invariance to the presence of shadows and to rotations in patchbased image segmentation. The improved accuracy (around +6% both in OA and Cohen's κ statistic), along with the simplicity of the approach encourage its use and extension to encode other invariances and other remote sensin…

A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images

2022

Abstract In precision agriculture, the accurate segmentation of crops and weeds in agronomic images has always been the center of attention. Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds. This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmentation of crops and weeds in color images. Agronomic images of two different databases were used for the segmentation algorithms. Using the thresholding technique, everything except plants was removed from the images. Afterward, semantic segmentation was applied usin…

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

2020

Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.

2016

This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.

An Automatic Three-Dimensional Fuzzy Edge Detector

2009

Three-dimensional object analysis is of particular interest in many research fields. In this context, the most common data representation is boundary mesh, namely, 2D surface embedded in 3D space. We will investigate the problem of 3D edge extraction, that is, salient surface regions characterized by high flexure. Our automatic edge detection method assigns a value, proportional to the local bending of the surface, to the elements of the mesh. Moreover, a proper scanning window, centered on each element, is used to discriminate between smooth zones of the surface and its edges. The algorithm does not require input parameters and returns a set of elements that represent the salient features …