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Estimating the three-dimensional motion vector field from stereo image sequences has drawn the attention of many researchers. Due to the importance of this problem, numerous approaches to image-based motion field estimation have been proposed in the last three decades. In this work we investigate dense scene flow computation from two consecutive frames. In Chaps. 2 and 3 we review classical...
In this chapter we review the estimation of the two-dimensional apparent motion field of two consecutive images in an image sequence. This apparent motion field is referred to as optical flow field, a two-dimensional vector field on the image plane. Because it is nearly impossible to cover the vast amount of approaches in the literature, in this chapter we set the focus on energy minimization approaches...
In real world motion estimation a major source of degradation in the estimated correspondence field comes about through illumination effects. Two obvious solutions to this problem are to either explicitly model the physical effect of illumination or to pre-filter the images so as to only preserve the illumination-independent information. The first approach is quite sophisticated because it involves...
Building upon optical flow and recent developments in stereo matching estimation, we discuss in this chapter how the motion of points in the three-dimensional world can be derived from stereo image sequences. The proposed algorithm takes into account image pairs from two consecutive times and computes both depth and a 3D motion vector associated with each point in the image. We particularly investigate...
The optical flow, the disparity, and the scene flow variables are estimated by minimizing variational formulations involving a data and a smoothness term. Both of these terms are based on assumptions of gray value consistency and smoothness which may not be exactly fulfilled. Moreover, the computed minimizers will generally not be globally optimal solutions. For follow-on calculations (e.g. speed,...
Most hazards in traffic situations include other moving traffic participants. Hence, reliably detecting motion in world coordinates is a crucial step for many driver assistance systems and an important part of machine visual kinesthesia. In this chapter we present two extensions of scene flow, making it further amenable to practical challenges of driver assistance. Firstly, we present a framework...
The goal of this book was to review some of the central advances in three decades of research on motion estimation and motion analysis from image sequences—from the early variational approach of Horn and Schunck for 2D optic flow estimation to recent variational approaches for 3D scene flow estimation and extensions. In particular, we discussed several algorithmic strategies for minimizing respective...
In the first part of this chapter we complement Chap. 2 by giving two examples for data terms derived from the classic optical flow constraint and from an adaptive fundamental matrix constraint. In the second part we outline the proof for the thresholding scheme used in the data term optimizing step introduced in the refinement flow estimation in Chap. 2 . This data term consists of a...
Scene flow and the scene flow estimates have been introduced in Chap. 4 . They are computed by minimizing an energy functional consisting of a data term and a smoothness term. This can be done by either using the refinement optical flow algorithm or using the calculus of variations, namely solving the associated Euler–Lagrange equations. In this chapter we outline step by step how the scene flow...
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