Learning how to be robust: Deep polynomial regression

Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results when the input data is heavily contaminated by outliers. Moreover, the problem is even harder when outliers have strong structure. Departing from problem-tailored heuristics for robust estimation of parametric models, we explore deep convolutional neural networks. Our work aims to find a generic approach for training deep regression models without the explicit need of supervised annotation. We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand. We demonstrate the value of our findings by comparing with standard robust regression methods. Furthermore, we demonstrate how to use such models for a real computer vision problem, i.e., video stabilization. The qualitative and quantitative experiments show that neural networks are able to learn robustness for general polynomial regression, with results that well overpass scores of traditional robust estimation methods.Comment: 18 pages, conferenc

Motion characterization from temporal cooccurrences of local motion-based measures for video indexing

This paper describes an original approach for motion interpretation with a view to content-based video indexing. We exploit a statistical analysis of the temporal distribution of appropriate local motion-based measures to perform a global motion characterization. We consider motion features extracted from temporal cooccurrence matrices, and related to properties of homogeneity, acceleration or complexity. Results on various real video sequences are reported and provide a first validation of the approach. 1

Real-time estimation of dominant motion in underwater video images for dynamic positionning

International audienceIn this paper, we propose a 2D visual motion estimation method which can be exploited to achieve a dynamic positioning (eg. by gaze control) with respect to a sea-bottom area of interest of a video camera mounted on a subsea vehicle. It mainly involves a dominant 2D motion robust estimation step from underwater video sequences. Optimizations carried out on the motion estimation code have made possible the use of our algorithm in ``application-related real-time'' for scientific exploration or inspection tasks. We have developed a friendly and efficient interface to perform this algorithm in an operational context. Experiments dealing with complex real underwater scenes are reported and validate the approach

Motion segmentation and estimation through the determination of spatio-temporal primitives in an image sequence

This paper describes a motion estimation scheme front image sequences, which consists of three main stages : local processing, intermediate-level structuring, optic flow field estimation . The first stage is concerned with either the determination of spatiotemporal edges along with their local spatial direction in the image plane and the component of their associated velocity vector perpendicular to this direction, or the segmentation into regions according to motion-based hierarchically performed criteria which take into account an explicit partial motion information . Both designed algorithms utilize principles of local modeling and likely hypothesis testing. Concerning the first one, the defined likelihood ratio test is implemented according to some appropriate mask convolution, the complexity order of which is similar to conventional spatial gradient computation . The purpose of the second stage is to obtain a structured partition of the image, resulting front edge linking and/or region segmentation . Thfienall stage deals with the velocity field estimation, that-is-to-say the reconstruction of the second component of displacement vectors by combining local observations. First, a recursive stochastic gradient, used to achieve the minimization of some simple functional, enables to estimate optic flow along contours . Then, the estimation within delineated domains is considered . Our approach is in particular distinguished by treating beforehand potential discontinuities of the velocity field in the image . Moreover, it provides with a set of intermediate-level spatio-temporal primitives .Schéma d'estimation du mouvement dans des séquences d'images s'articulant en 3 étapes fondamentales: traitement local, structuration intermédiaire, estimation du champ des vitesse

Simultaneous motion detection and background reconstruction with a conditional mixed-state markov random field

In this work we present a new way of simultaneously solving the problems of motion detection and background image reconstruction. An accurate estimation of the background is only possible if we locate the moving objects. Meanwhile, a correct motion detection is achieved if we have a good available background model. The key of our joint approach is to define a single random process that can take two types of values, instead of defining two different processes, one symbolic (motion detection) and one numeric (background intensity estimation). It thus allows to exploit the (spatio-temporal) interaction between a decision (motion detection) and an estimation (intensity reconstruction) problem. Consequently, the meaning of solving both tasks jointly, is to obtain a single optimal estimate of such a process. The intrinsic interaction and simultaneity between both problems is shown to be better modeled within the so-called mixed-state statistical framework, which is extended here to account for symbolic states and conditional random fields. Experiments on real sequences and comparisons with existing motion detection methods support our proposal. Further implications for video sequence inpainting will be also discussed. © 2011 Springer Science+Business Media, LLC.postprin

Texturas de Movimiento: Campos Markovianos Mixtos y Segmentación

El objeto de este trabajo es la modelización de movimiento en secuencias de imágenes que presentan cierta dinámica estacionaria y homogénea. En este caso se adopta un modelo de Campos Aleatorios Markovianos con estados mixtos, como representación de las llamadas texturas de movimiento. El enfoque consiste en describir la distribución espacial de algún tipo de medida de movimiento, la cual consiste de dos tipos de valores: una componente discreta relativa a la ausencia de movimiento y una parte continua para mediciones diferentes de cero. Se proponen varias extensiones importantes y se aplica el modelo al problema de segmentación de texturas, tanto en secuencias sintéticas como reales.Fil: Crivelli, Tomás. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Cernuschi Frias, Bruno. Universidad de Buenos Aires. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderon; ArgentinaFil: Bouthemy, Patrick. Institut National de Recherche en Informatique et en Automatique; Franci

Motion Textures: Modeling, Classification, and Segmentation Using Mixed-State Markov Random Fields

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