Search results for "categorization"
showing 10 items of 199 documents
The categorization of amateur cyclists as research participants: findings from an observational study.
2018
Sampling bias is an issue for research involving cyclists. The heterogeneity of cyclist populations, on the basis of skill level and riding purpose, can generate incorrect inferences about one specific segment of the population of interest. In addition, a more accurate categorization would be helpful when physiological parameters are not available. This study proposes using self-reported data to categorize amateur cyclist types by varying skill levels and riding purposes, therefore improving sample selection in experimental studies. A total of 986 cyclists completed an online questionnaire between February and October 2016. Two-step cluster analyses were performed to generate distinct group…
Cluster-Randomized Studies.
2018
Background Cluster-randomized trials (CRT) are needed to compare interventions that are allocated to entire groups of subjects, rather than to individuals. Publications about CRT have become steadily more common over the past decade. Readers of such publications should be able to categorize and interpret the findings of CRT correctly while considering the methodological requirements applicable to this type of study. Methods This review is based on a selection of pertinent literature and on the authors' expertise. CRT-specific methodological aspects of the planning, performance, and interpretation of studies are discussed. Results Readers of publications on CRT should check whether due consi…
Categorizing migrants by disempowering the right to asylum. A focus on the Sicilian implementation of the “Hotspot approach”.
2017
This contribution aims to shed light on the specific mechanism of migrant categorization implemented by the so-called «Hotspot approach», which was launched by the EU Agenda on Migration in May 2015. This approach is here envisaged as a response to the current changes in the composition of migration towards Europe. Provisions contained in EU and Italian policy documents are compared with concrete practices enacted on the ground by investigating two case studies: the initial opening of the Hotspot at Milo, in Trapani, and the first months of functioning of the Hotspot on Lampedusa. The empirical research covers the period between the last months of 2015 and the beginning of 2016. The short-t…
Ad-Hoc Segmentation Pipeline for Microarray Image Analysis
2006
Microarray is a new class of biotechnologies able to help biologist researches to extrapolate new knowledge from biological experiments. Image Analysis is devoted to extrapolate, process and visualize image information. For this reason it has found application also in Microarray, where it is a crucial step of this technology (e.g. segmentation). In this paper we describe MISP (Microarray Image Segmentation Pipeline), a new segmentation pipeline for Microarray Image Analysis. The pipeline uses a recent segmentation algorithm based on statistical analysis coupled with K-Means algorithm. The Spot masks produced by MISP are used to determinate spots information and quality measures. A software …
Chaotic multiagent system approach for MRF-based image segmentation
2005
In this paper, we propose a new chaotic approach for image segmentation based on multiagent system (MAS). We consider a set of segmentation agents organized around a coordinator agent. Each segmentation agent performs iterated conditional modes (ICM) starting from its own initial image created using a chaotic mapping. The coordinator agent diversifies the initial images using a crossover and a chaotic mutation operators. The efficiency of our chaotic MAS approach is shown through some experimental results.
Hidden Markov Random Fields and Direct Search Methods for Medical Image Segmentation
2016
The goal of image segmentation is to simplify the representation of an image to items meaningful and easier to analyze. Medical image segmentation is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There is no one way to perform the segmentation. There are several methods based on HMRF. Hidden Markov Random Fields (HMRF) constitute an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we investigate direct search methods that are Nelder-Mead and Torczon methods to solve this optimization problem. The quality of segmentation is evaluated on grou…
Characterizing cognitive problem-solving strategies in patients’ everyday life: The case of patients with Type 1 diabetes
2021
Introduction:Numerous quantitative studies have shown the importance of executive functions (planning, attention, inhibition, and short-term memory) for diabetes treatment compliance. Those studies also point to the paucity of data on action strategies employed by persons with diabetes. The aim of this study is to characterize the action strategies used in six situations typically encountered by persons with Type 1 diabetes (no comorbidities).Methods:This qualitative multiple-case study concerns adult patients with no comorbidities. Eighteen patients were presented with six clinical vignettes portraying emblematic situations and then interviewed. After categorization, the 108 situations wer…
Image Segmentation based on Genetic Algorithms Combination
2005
The paper describes a new image segmentation algorithm called Combined Genetic segmentation which is based on a genetic algorithm. Here, the segmentation is considered as a clustering of pixels and a similarity function based on spatial and intensity pixel features is used. The proposed methodology starts from the assumption that an image segmentation problem can be treated as a Global Optimization Problem. The results of the image segmentations algorithm has been compared with recent existing techniques. Several experiments, performed on real images, show good performances of our approach compared to other existing methods.
3D skeleton-based human action classification: A survey
2016
In recent years, there has been a proliferation of works on human action classification from depth sequences. These works generally present methods and/or feature representations for the classification of actions from sequences of 3D locations of human body joints and/or other sources of data, such as depth maps and RGB videos.This survey highlights motivations and challenges of this very recent research area by presenting technologies and approaches for 3D skeleton-based action classification. The work focuses on aspects such as data pre-processing, publicly available benchmarks and commonly used accuracy measurements. Furthermore, this survey introduces a categorization of the most recent…
A Coupled Schema of Probabilistic Atlas and Statistical Shape and Appearance Model for 3D Prostate Segmentation in MR Images
2012
International audience; A hybrid framework of probabilistic atlas and statistical shape and appearance model (SSAM) is proposed to achieve 3D prostate segmentation. An initial 3D segmentation of the prostate is obtained by registering the probabilistic atlas to the test dataset with deformable Demons registration. The initial results obtained are used to initialize multiple SSAMs corresponding to the apex, central and base regions of the prostate gland to incorporate local variabilities. Multiple mean parametric models of shape and appearance are derived from principal component analysis of prior shape and intensity information of the prostate from the training data. The parameters are then…