Search results for "Segmentation"
showing 10 items of 674 documents
Compréhension de scènes urbaines basée sur la polarisation
2021
Humans possess an innate ability to interpret scenes under any condition. Computer Vision tends to mimic these capabilities by implementing intelligent algorithms to address complex understanding problems. In this regard, we are interested in understanding outdoor urban scenes in various weather conditions. This thesis specifically addresses the problems arising from the presence of specularity in the scenes. To this end, we aim to take advantage of polarization indices to define such surfaces in addition to traditional objects. In terms of understanding, we aim to introduce polarization to the fields of computer vision and deep learning.This thesis focuses on the following underlying chall…
Multispectral imaging and its use for face recognition : sensory data enhancement
2015
In this thesis, we focus on multispectral image for face recognition. With such application,the quality of the image is an important factor that affects the accuracy of therecognition. However, the sensory data are in general corrupted by noise. Thus, wepropose several denoising algorithms that are able to ensure a good tradeoff betweennoise removal and details preservation. Furthermore, characterizing regions and detailsof the face can improve recognition. We focus also in this thesis on multispectral imagesegmentation particularly clustering techniques and cluster analysis. The effectiveness ofthe proposed algorithms is illustrated by comparing them with state-of-the-art methodsusing both…
PIRACY REVISITED: EXPLORING MUSIC USERS IN THE AGE OF TECHNOLOGY DEPENDENCY
2019
This paper empirically investigates and characterizes users of recorded music, both downloaders and purchasers. To this end we analyse the role of the variables defining the different segments of music users. In doing so, we have considered two main traits influencing the use of music. First, objective variables such as demographics, music consumption habits, music genres and technology. Second, subjective variables such as motives and attitudes towards piracy. Using data from a personal survey, subsequent latent class and fuzzy analyses show that while the former characteristics are relevant in those getting music for free from the Internet, the latter don´t pay any special part, contrary …
Marketing Sri Lanka as an International Tourist Destination
2007
Masteroppgave i økonomi og administrasjon 2007 - Høgskolen i Agder, Kristiansand Within the last two decades Sri Lanka’s tourism industry has grown dramatically as one of the main foreign exchange earners and employment provider. Since Sri Lanka depends enormously on tourism for its growth and development, this paper provides an analysis of marketing efforts within the tourism industry in Sri Lanka for foreign tourists. In compliance with this trend, the purpose of this thesis and research is to review Sri Lanka as an international tourist destination and its current marketing activities in the tourism trade, to investigate the proper use of marketing efforts, and to suggest guidelines for …
Fast nosological imaging using canonical correlation analysis of brain data obtained by two-dimensional turbo spectroscopic imaging.
2007
A new fast and accurate tissue typing technique has recently been successfully applied to prostate MR spectroscopic imaging (MRSI) data. This technique is based on canonical correlation analysis (CCA), a statistical method able to simultaneously exploit the spectral and spatial information characterizing the MRSI data. Here, the performance of CCA is further investigated by using brain data obtained by two-dimensional turbo spectroscopic imaging (2DTSI) from patients affected by glioblastoma. The purpose of this study is to investigate the applicability of CCA when typing tissues of heterogeneous tumors. The performance of CCA is also compared with that of ordinary correlation analysis on s…
Hidden Markov random field model and Broyden–Fletcher–Goldfarb–Shanno algorithm for brain image segmentation
2018
International audience; Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. Segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden-Fletcher-Goldfarb-Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. …
Depth Map Generation by Image Classification
2004
This paper presents a novel and fully automatic technique to estimate depth information from a single input image. The proposed method is based on a new image classification technique able to classify digital images (also in Bayer pattern format) as indoor, outdoor with geometric elements or outdoor without geometric elements. Using the information collected in the classification step a suitable depth map is estimated. The proposed technique is fully unsupervised and is able to generate depth map from a single view of the scene, requiring low computational resources.
Using discourse segmentation to account for the polyfunctionality of discourse markers:The case of well
2021
Abstract A large number of studies describe the many different functions of polyfunctional discourse markers like well in different contexts and from different theoretical perspectives. In the current paper, we propose to systematize the many different uses identified based on their position with respect to the discourse units they are associated with. Not only can previous findings on well be integrated into a single coherent representation of its uses and functions, but the positions with respect to the discourse units can also be associated with specific functions, thus shedding light on how the polyfunctionality of well is brought about.
Automated detection and localization system of myocardial infarction in single-beat ECG using Dual-Q TQWT and wavelet packet tensor decomposition.
2019
Abstract Background and objective It is challenging to conduct real-time identification of myocardial infarction (MI) due to artifact corruption and high dimensionality of multi-lead electrocardiogram (ECG). In the present study, we proposed an automated single-beat MI detection and localization system using dual-Q tunable Q-factor wavelet transformation (Dual-Q TQWT) denoising algorithm. Methods After denoising and segmentation of ECG, a fourth-order wavelet tensor (leads × subbands × samples × beats) was constructed based on the discrete wavelet packet transform (DWPT), to represent the features considering the information of inter-beat, intra-beat, inter-frequency, and inter-lead. To red…
Discrete wavelet transform based multispectral filter array demosaicking
2013
International audience; The idea of colour filter array may be adapted to multi-spectral image acquisition by integrating more filter types into the array, and developing associated demosaicking algorithms. Several methods employing discrete wavelet transform (DWT) have been proposed for CFA demosaicking. In this work, we put forward an extended use of DWT for mul-tispectral filter array demosaicking. The extension seemed straightforward, however we observed striking results. This work contributes to better understanding of the issue by demonstrating that spectral correlation and spatial resolution of the images exerts a crucial influence on the performance of DWT based demosaicking.