Motion Estimation from Disparity Images

A new method for 3D rigid motion estimation from stereo is proposed in this paper. The appealing feature of this method is that it directly uses the disparity images obtained from stereo matching. We assume that the stereo rig has parallel cameras and show, in that case, the geometric and topological properties of the disparity images. Then we introduce a rigid transformation (called d-motion) that maps two disparity images of a rigidly moving object. We show how it is related to the Euclidean rigid motion and a motion estimation algorithm is derived. We show with experiments that our approach is simple and more accurate than standard approaches

Plan-view Trajectory Estimation with Dense Stereo Background Models

In a known environment, objects may be tracked in multiple views using a set of back-ground models. Stereo-based models can be illumination-invariant, but often have undefined values which inevitably lead to foreground classification errors. We derive dense stereo models for object tracking using long-term, extended dynamic-range imagery, and by detecting and interpolating uniform but unoccluded planar regions. Foreground points are detected quickly in new images using pruned disparity search. We adopt a 'late-segmentation' strategy, using an integrated plan-view density representation. Foreground points are segmented into object regions only when a trajectory is finally estimated, using a dynamic programming-based method. Object entry and exit are optimally determined and are not restricted to special spatial zones

Using multiple-hypothesis disparity maps and image velocity for 3-D motion estimation

In this paper we explore a multiple hypothesis approach to estimating rigid motion from a moving stereo rig. More precisely, we introduce the use of Gaussian mixtures to model correspondence uncertainties for disparity and image velocity estimation. We show some properties of the disparity space and show how rigid transformations can be represented. An algorithm derived from standard random sampling-based robust estimators, that efficiently estimates rigid transformations from multi-hypothesis disparity maps and velocity fields is given

Untethered Gesture Acquisition and Recognition for a Multimodal Conversational System

Humans use a combination of gesture and speech to convey meaning, and usually do so without holding a device or pointer. We present a system that incorporates body tracking and gesture recognition for an untethered human-computer interface. This research focuses on a module that provides parameterized gesture recognition, using various machine learning techniques. We train a support vector classifier to model the boundary of the space of possible gestures, and train Hidden Markov Models on specific gestures. Given a sequence, we can find the start and end of various gestures using a support vector classifier, and find gesture likelihoods and parameters with a HMM. Finally multimodal recognition is performed using rank-order fusion to merge speech and vision hypotheses

Neutrons for probing the ice nucleation on atmospheric soot particles

Soot resulting from combustion of kerosene in aircraft engines can act as condensation nuclei for water/ice in the atmosphere and promote the formation of contrails that turn into artificial cirrus clouds and affect the climate. The mechanisms of nucleation of water/ice particles are not well identified. Studies “in situ” are difficult to realize, so we try to determine by neutron diffraction the nucleation of water/ice adsorbed on soot collected at the outlet of an aircraft engine combustor within the conditions of the upper troposphere. The results are compared with those obtained on model laboratory soot. The comparison highlights the role of chemical impurities and structural defects of original aircraft engine soot on the nucleation of water/ice in atmospheric conditions

Closed-form solutions for the Euclidean calibration of a stereo rig

In this paper we describe a method for estimating the internal parameters of the left and right cameras associated with a stereo image pair. The stereo pair has known epipolar geometry and therefore 3-D projective reconstruction of pairs of matched image points is available