238 research outputs found
Grouping Straight Line Segments in Real Images
In this paper, we discuss straight line extraction as a part of the image interpretation process. Favoring the use of line drawings as intermediate data for the extraction, we survey the current methods, which all achieve a polygonal approximation of lines, and show that they are not appropriate for the identification of straight elements in a scene. We propose a new approach which uses a scale invariant criterion and is based on the characterization of prime segments in a line, and develop an original method for obtaining these prime segments. Results show that we significantly improve the performance of straight line extraction. The methodology we have used here is applicable to a large class of segmentation problems
Fast, Approximate Piecewise-Planar Modeling Based on Sparse Structure-from-Motion and Superpixels
BĂłdis-SzomorĂş A., Riemenschneider H., Van Gool L., ''Fast, approximate piecewise-planar modeling based on sparse structure-from-motion and superpixels'', 27th IEEE conference on computer vision and pattern recognition - CVPR 2014, pp. 469-476, June 23-28, 2014, Columbus, Ohio, USA.status: publishe
Piecewise rigid curve deformation via a Finsler steepest descent
This paper introduces a novel steepest descent flow in Banach spaces. This
extends previous works on generalized gradient descent, notably the work of
Charpiat et al., to the setting of Finsler metrics. Such a generalized gradient
allows one to take into account a prior on deformations (e.g., piecewise rigid)
in order to favor some specific evolutions. We define a Finsler gradient
descent method to minimize a functional defined on a Banach space and we prove
a convergence theorem for such a method. In particular, we show that the use of
non-Hilbertian norms on Banach spaces is useful to study non-convex
optimization problems where the geometry of the space might play a crucial role
to avoid poor local minima. We show some applications to the curve matching
problem. In particular, we characterize piecewise rigid deformations on the
space of curves and we study several models to perform piecewise rigid
evolution of curves
Directional Estimation for Robotic Beating Heart Surgery
In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart
Directional Estimation for Robotic Beating Heart Surgery
In robotic beating heart surgery, a remote-controlled robot can be used to carry out the operation while automatically canceling out the heart motion. The surgeon controlling the robot is shown a stabilized view of the heart. First, we consider the use of directional statistics for estimation of the phase of the heartbeat. Second, we deal with reconstruction of a moving and deformable surface. Third, we address the question of obtaining a stabilized image of the heart
A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric images
Real-time magnetic resonance imaging (RT-MRI) of human speech production is
enabling significant advances in speech science, linguistics, bio-inspired
speech technology development, and clinical applications. Easy access to RT-MRI
is however limited, and comprehensive datasets with broad access are needed to
catalyze research across numerous domains. The imaging of the rapidly moving
articulators and dynamic airway shaping during speech demands high
spatio-temporal resolution and robust reconstruction methods. Further, while
reconstructed images have been published, to-date there is no open dataset
providing raw multi-coil RT-MRI data from an optimized speech production
experimental setup. Such datasets could enable new and improved methods for
dynamic image reconstruction, artifact correction, feature extraction, and
direct extraction of linguistically-relevant biomarkers. The present dataset
offers a unique corpus of 2D sagittal-view RT-MRI videos along with
synchronized audio for 75 subjects performing linguistically motivated speech
tasks, alongside the corresponding first-ever public domain raw RT-MRI data.
The dataset also includes 3D volumetric vocal tract MRI during sustained speech
sounds and high-resolution static anatomical T2-weighted upper airway MRI for
each subject.Comment: 27 pages, 6 figures, 5 tables, submitted to Nature Scientific Dat
Design and Implementation of a Motif-based Compression Algorithm for Biometric Signals
Wearable devices are becoming a natural and economic means to gather biometric data from users: this thesis is centered around lossy data compression techniques, whose aim is to minimize the amount of information that is to be stored on their onboard memory and subsequently transmitted over wireless interfaces. A new class of codebook based (CB) compression algorithms is proposed, designed to be energy efficient, online and amenable to any type of signal exhibiting recurrent patternsope
Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models
To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented.
The modeling of increasing level of information is used to extract, represent and link image features to semantic content.
The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images
Spectral Dimensionality Reduction
In this paper, we study and put under a common framework a number of non-linear dimensionality reduction methods, such as Locally Linear Embedding, Isomap, Laplacian Eigenmaps and kernel PCA, which are based on performing an eigen-decomposition (hence the name 'spectral'). That framework also includes classical methods such as PCA and metric multidimensional scaling (MDS). It also includes the data transformation step used in spectral clustering. We show that in all of these cases the learning algorithm estimates the principal eigenfunctions of an operator that depends on the unknown data density and on a kernel that is not necessarily positive semi-definite. This helps to generalize some of these algorithms so as to predict an embedding for out-of-sample examples without having to retrain the model. It also makes it more transparent what these algorithm are minimizing on the empirical data and gives a corresponding notion of generalization error. Dans cet article, nous étudions et développons un cadre unifié pour un certain nombre de méthodes non linéaires de réduction de dimensionalité, telles que LLE, Isomap, LE (Laplacian Eigenmap) et ACP à noyaux, qui font de la décomposition en valeurs propres (d'où le nom "spectral"). Ce cadre inclut également des méthodes classiques telles que l'ACP et l'échelonnage multidimensionnel métrique (MDS). Il inclut aussi l'étape de transformation de données utilisée dans l'agrégation spectrale. Nous montrons que, dans tous les cas, l'algorithme d'apprentissage estime les fonctions propres principales d'un opérateur qui dépend de la densité inconnue de données et d'un noyau qui n'est pas nécessairement positif semi-défini. Ce cadre aide à généraliser certains modèles pour prédire les coordonnées des exemples hors-échantillons sans avoir à réentraîner le modèle. Il aide également à rendre plus transparent ce que ces algorithmes minimisent sur les données empiriques et donne une notion correspondante d'erreur de généralisation.non-parametric models, non-linear dimensionality reduction, kernel models, modèles non paramétriques, réduction de dimensionalité non linéaire, modèles à noyau
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