Philippe Schroeter

Segmentation of moving objects by robust motion parameter estimation over multiple frames

A method for detecting and segmenting accurately moving objects in monocular image sequences is proposed. It consists of two modules, namely a motion estimation and a motion segmentation module. The motion estimation problem is formulated as a time varying motion parameter estimation over multiple frames. Robust regression techniques are used to estimate these parameters. The motion parameters for the different moving objects are obtained by successive estimations on regions for which the previously estimated motion parameters are not valid. The segmentation module combines all motion parameters and the gray level information in order to obtain the motion boundaries and to improve them by using time integration. Experimental results on real image sequences with static or moving camera in the presence of multiple moving objects are reported.

Hierarchical image segmentation by multi- dimensional clustering and orientation-adaptive boundary refinement

In this paper we present a new multi-dimensional segmentation algorithm. We propose an orientation-adaptive boundary estimation process, embedded in a multiresolution pyramidal structure, that allows the use of different clustering procedures without spatial connectivity constraints. The presence of noise in the feature space, mainly produced by modeling errors, causes a class-overlap which can be reduced in a multiresolution pyramid. At the coarsest resolution level, the separation between the different classes is increased and the within-class variance reduced. Thus, at this level, the classes can be obtained with different multi-dimensional clustering algorithms without connectivity constraints. Small and scattered classes as well as isolated class labels are reassigned to their neighborhood by a process which guarantees the spatial connectivity. The resolution is then increased by projecting down the class labels. At each level, the borders are improved by reassigning the boundary pixels to their spatially closest class. However, the class-uncertainty astride the borders has first to be reduced, and we propose to do this by means of orientation-adaptive butterfly-shaped filters. This refinement process further eliminates spatially misclassified pixels produced by the unconstrained clustering. Experimental results show that similarly accurate boundaries are obtained with different clustering algorithms for various test images.

Spatio-Temporal Robust Motion Estimation and Segmentation

In this paper, a general spatio-temporal framework for motion estimation is presented. It allows to estimate a fully parametric motion model over an image sequence. As parametric models describe one motion only, a robust estimator is introduced in order to cope with several moving objects. The motion segmentation algorithm combines luminance and the composition of all the motions detected over a set of successive frames for motion boundaries estimation.

Tracking Based on Hierarchical Multiple Motion Estimation and Robust Regression

Keywords: LTS1 Reference LTS-ARTICLE-1993-006 Record created on 2006-06-14, modified on 2016-08-08

Motion segmentation by fuzzy clustering with automatic determination of the number of motions

A layered motion estimation scheme using fuzzy clustering is introduced in this paper. Once motion estimation is performed a modified objective criterion is applied to discard non significant classes.

Time-varying motion estimation using orthogonal polynomials and applications

This paper presents a hierarchical motion estimation method considering both a large spatial and temporal support. The problem of motion estimation is formulated as a time-varying parameter estimation problem. The main advantage of the proposed algorithm is its ability to constrain the computation of the flow field spatially and temporally at the same time, by defining each motion parameter as a linear combination of some orthogonal time functions. The number of time basis varies according to the complexity of the movement and is determined automatically by means of a multiresolution approach and of statistical tests. We also present two different applications which underline the validity of the chosen approach.