Aprenent a recrear la realitat en 3D

Les tècniques de representació d'escenes en tres dimensions tenen un problema comú : que l'escena es considera com un tot i, per tant, són relativament ineficients a l'hora de realitzar el processament geomètric d'objectes. En aquest treball s'ha proposat una nova tècnica de modelat jeràrquic d'escenes 3D estàtiques que directament té en compte els objectes presents.Investigadores de diversas universidades, entre ellas la UAB, hananalizado la relación entre el conocimiento que tienen de las plantas lasmadres Tsimane' -un grupo étnico de la Amazona boliviana- y la saludde sus hijos. Y han concluido que el estado de salud de los niños demadres que saben más sobre estas plantas es mejor.Researchers of several universities, among them UAB, have analyzedthe relation between the ethnobotanical knowledge of Tsimane ́ mothers-an ethnic group of the Bolivian Amazon- and the health of their children.Researchers have concluded that children with mothers with higherethnobotanical knowledge enjoy better health than the other

Melamine Faced Panels Defect Classification beyond the Visible Spectrum

In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution

The Richer Representation the Better Registration

International audienceIn this paper, the registration problem is formulated as a point to model distance minimization. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, this formulation avoids the correspondence search that is time-consuming. In the first stage, the target set is described through an implicit function by employing a linear least squares fitting. This function can be either an implicit polynomial or an implicit B-spline from a coarse to fine representation. In the second stage, we show how the obtained implicit representation is used as an interface to convert point-to-point registration into point-to-implicit problem. Furthermore, we show that this registration distance is smooth and can be minimized through the Levengberg-Marquardt algorithm. All the formulations presented for both stages are compact and easy to implement. In addition, we show that our registration method can be handled using any implicit representation though some are coarse and others provide finer representations; hence, a tradeoff between speed and accuracy can be set by employing the right implicit function. Experimental results and comparisons in 2D and 3D show the robustness and the speed of convergence of the proposed approach

Prior Knowledge Based Motion Model Representation

This paper presents a new approach for human walking modeling from monocular image sequences. A kinematics model and a walking motion model are introduced in order to exploit prior knowledge. The proposed technique consists of two steps. Initially, an efficient feature point selection and tracking approach is used to compute feature points' trajectories. Peaks and valleys of these trajectories are used to detect key frames-frames where both legs are in contact with the floor. Secondly, motion models associated with each joint are locally tuned by using those key frames. Differently than previous approaches, this tuning process is not performed at every frame, reducing CPU time. In addition, the movement's frequency is defined by the elapsed time between two consecutive key frames, which allows handling walking displacement at different speed. Experimental results with different video sequences are presented

Cross-spectral local descriptors via quadruplet network

This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data