A localized correlation function for stereoscopic image matching

We propose and study a localized correlation function for stereoscopic matching . The latter is based on the wavelet decomposition of the input images . Contrarily to « coarse-to-fine » algorithms, this one simultaneously processes information at the different scales . The localized correlation function is defined by locally integrating with respect to the scale variable . We show that it is equivalent to the definition of a correlation kernel, which is extremely precise in terms of position and disparity . The definition can then be modified in order to account for the local frequency content of the images . Then, we suggest pre-processings of the images : we argue in favour of the use of multiresolution contrast techniques, associated to a quadratic normalization .Nous proposons et étudions une fonction de corrélation localisée permettant la mise en correspondance d'images stéréoscopiques. Celle-ci est fondée sur la décomposition en échelles des images d'entrée. A l'inverse des algorithmes de type « coarse-to-fine », celui-ci traite simultanément les informations des différentes bandes de fréquence. Pour cela, nous définissons en chaque point des deux images une fonction de corrélation localisée par intégration sur le paramètre d'échelle, dont nous montrons qu'elle est équivalente à la définition d'un noyau de corrélation extrêmement fin dans les paramètres de position et de disparité. La définition peut ensuite être modifiée pour prendre en compte la composition fréquentielle locale des images d'entrées. Enfin, nous nous intéressons au problème de la normalisation préalable des images à apparier et justifions le choix du contraste multirésolution associé à une normalisation quadratique

Spatial Motion Doodles: Sketching Animation in VR Using Hand Gestures and Laban Motion Analysis

International audienceWe present a method for easily drafting expressive character animation by playing with instrumented rigid objects. We parse the input 6D trajectories (position and orientation over time)-called spatial motion doodles-into sequences of actions and convert them into detailed character animations using a dataset of parameterized motion clips which are automatically fitted to the doodles in terms of global trajectory and timing. Moreover, we capture the expres-siveness of user-manipulation by analyzing Laban effort qualities in the input spatial motion doodles and transferring them to the synthetic motions we generate. We validate the ease of use of our system and the expressiveness of the resulting animations through a series of user studies, showing the interest of our approach for interactive digital storytelling applications dedicated to children and non-expert users, as well as for providing fast drafting tools for animators

Graph Matching via Sequential Monte Carlo

International audienceGraph matching is a powerful tool for computer vision and machine learning. In this paper, a novel approach to graph matching is developed based on the sequential Monte Carlo framework. By constructing a sequence of intermediate target distributions, the proposed algorithm sequentially performs a sampling and importance resampling to maximize the graph matching objective. Through the sequential sampling procedure, the algorithm effectively collects potential matches under one-to-one matching constraints to avoid the adverse effect of outliers and deformation. Experimental evaluations on synthetic graphs and real images demonstrate its higher robustness to deformation and outliers

Time-lapsing biodiversity: an open source method for measuring diversity changes by remote sensing

Understanding biodiversity changes in time is crucial to promptly provide management practices against diversity loss. This is overall true when considering global scales, since human-induced global change is expected to make significant changes on the Earth's biota. Biodiversity management and planning is mainly based on field observations related to community diversity, considering different taxa. However, such methods are time and cost demanding and do not allow in most cases to get temporal replicates. In this view, remote sensing can provide a wide data coverage in a short period of time. Recently, the use of Rao's Q diversity as a measure of spectral diversity has been proposed in order to explicitly take into account differences in a neighbourhood considering abundance and relative distance among pixels. The aim of this paper was to extend such a measure over the temporal dimension and to present an innovative approach to calculate remotely sensed temporal diversity. We demonstrated that temporal beta-diversity (spectral turnover) can be calculated pixel-wise in terms of both slope and coefficient of variation and further plotted over the whole matrix / image. From an ecological and operational point of view, for prioritisation practices in biodiversity protection, temporal variability could be beneficial in order to plan more efficient conservation practices starting from spectral diversity hotspots in space and time. In this paper, we delivered a highly reproducible approach to calculate spatio-temporal diversity in a robust and straightforward manner. Since it is based on open source code, we expect that our method will be further used by several researchers and landscape managers

Double down on remote sensing for biodiversity estimation. A biological mindset

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the “spectral species”—sets of pixels with a similar spectral signal—and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept

Time Scale Approach for Chirp Detection

International audienceTwo different approaches for joint detection and estimation of signals embedded in stationary random noise are considered and compared, for the subclass of amplitude and frequency modulated signals. Matched filter approaches are compared to time-frequency and time scale based approaches. Particular attention is paid to the case of the so-called " power-law chirps " , characterized by monomial and polynomial amplitude and frequency functions. As target application, the problem of gravitational waves at interferometric detectors is considered

An Analysis of Errors in Graph-Based Keypoint Matching and Proposed Solutions

International audienceAn error occurs in graph-based keypoint matching when key-points in two different images are matched by an algorithm but do not correspond to the same physical point. Most previous methods acquire keypoints in a black-box manner, and focus on developing better algorithms to match the provided points. However to study the complete performance of a matching system one has to study errors through the whole matching pipeline, from keypoint detection, candidate selection to graph optimisation. We show that in the full pipeline there are six different types of errors that cause mismatches. We then present a matching framework designed to reduce these errors. We achieve this by adapting keypoint detectors to better suit the needs of graph-based matching, and achieve better graph constraints by exploiting more information from their keypoints. Our framework is applicable in general images and can handle clutter and motion discontinuities. We also propose a method to identify many mismatches a posteriori based on Left-Right Consistency inspired by stereo matching due to the asymmetric way we detect keypoints and define the graph

Unsupervised classemes

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-33885-4_41Proceedings of Information Fusion in Computer Vision for Concept Recognition at the ECCV 2012In this paper we present a new model of semantic features that, unlike previously presented methods, does not rely on the presence of a labeled training data base, as the creation of the feature extraction function is done in an unsupervised manner. We test these features on an unsupervised classification (clustering) task, and show that they outperform primitive (low-level) features, and that have performance comparable to that of supervised semantic features, which are much more expensive to determine relying on the presence of a labeled training set to train the feature extraction function

Coherent states on spheres

We describe a family of coherent states and an associated resolution of the identity for a quantum particle whose classical configuration space is the d-dimensional sphere S^d. The coherent states are labeled by points in the associated phase space T*(S^d). These coherent states are NOT of Perelomov type but rather are constructed as the eigenvectors of suitably defined annihilation operators. We describe as well the Segal-Bargmann representation for the system, the associated unitary Segal-Bargmann transform, and a natural inversion formula. Although many of these results are in principle special cases of the results of B. Hall and M. Stenzel, we give here a substantially different description based on ideas of T. Thiemann and of K. Kowalski and J. Rembielinski. All of these results can be generalized to a system whose configuration space is an arbitrary compact symmetric space. We focus on the sphere case in order to be able to carry out the calculations in a self-contained and explicit way.Comment: Revised version. Submitted to J. Mathematical Physic