Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 3) STRUCTURE FROM MOTION METHODS <s> In the general structure-from-motion (SFM) problem involving several moving objects in a scene, the essential first step is to segment moving objects independently. We attempt to deal with the problem of optical flow estimation and motion segmentation over a pair of images. We apply a mean field technique to determine optical flow and motion boundaries and present a deterministic algorithm. Since motion discontinuities represented by line process are embedded in the estimation of the optical flow, our algorithm provides accurate estimates of optical flow especially along motion boundaries and handles occlusion and multiple motions. We show that the proposed algorithm outperforms other well-known algorithms in terms of estimation accuracy and timing. <s> BIB001 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 3) STRUCTURE FROM MOTION METHODS <s> The segmentation of foreground objects in camera images is a fundamental step in many computer vision applications. For visual effect creation, the foreground segmentation is required for the integration of virtual objects between scene elements. On the other hand, camera and scene estimation is needed to integrate the objects perspectively correct into the video. <s> BIB002 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 3) STRUCTURE FROM MOTION METHODS <s> We present a novel optimisation framework for the estimation of the multi-body motion segmentation and 3D reconstruction of a set of point trajectories in the presence of missing data. The proposed solution not only assigns the trajectories to the correct motion but it also solves for the 3D location of multi-body shape and it fills the missing entries in the measurement matrix. Such a solution is based on two fundamental principles: each of the multi-body motions is controlled by a set of metric constraints that are given by the specific camera model, and the shape matrix that describes the multi-body 3D shape is generally sparse. We jointly include such constraints in a unique optimisation framework which, starting from an initial segmentation, iteratively enforces these set of constraints in three stages. First, metric constraints are used to estimate the 3D metric shape and to fill the missing entries according to an orthographic camera model. Then, wrongly segmented trajectories are detected by using sparse optimisation of the shape matrix. A final reclassification strategy assigns the detected points to the right motion or discards them as outliers. We provide experiments that show consistent improvements to previous approaches both on synthetic and real data. <s> BIB003 </s> Dependent Motion Segmentation in Moving Camera Videos: A Survey <s> 3) STRUCTURE FROM MOTION METHODS <s> Structure from Motion or the sparse 3D reconstruction out of individual photos is a long studied topic in computer vision. Yet none of the existing reconstruction pipelines fully addresses a progressive scenario where images are only getting available during the reconstruction process and intermediate results are delivered to the user. Incremental pipelines are capable of growing a 3D model but often get stuck in local minima due to wrong (binding) decisions taken based on incomplete information. Global pipelines on the other hand need the access to the complete viewgraph and are not capable of delivering intermediate results. In this paper we propose a new reconstruction pipeline working in a progressive manner rather than in a batch processing scheme. The pipeline is able to recover from failed reconstructions in early stages, avoids to take binding decisions, delivers a progressive output and yet maintains the capabilities of existing pipelines. We demonstrate and evaluate our method on diverse challenging public and dedicated datasets including those with highly symmetric structures and compare to the state of the art. <s> BIB004
For motion segmentation in real scenes, a practical and robust motion segmentation scheme must take into account object's structure and motion in space. As a result, 3D motion segmentation is employed in most practical segmentation systems. One kind of most important 3D methods is the structure from motion (SFM) method, which can deal with 3D scenes with significant depth variations BIB001 . The idea of SFM is inspired by the phenomenon that humans act in a 3D world while they only sense 2D projection of it  . Thanks to the reconstruction of 3D geometry, SFM has been widely used in applications related to the recovery of 3D geometry from 2D image motions, such as robotic navigation, object tracking, animation, active vision, 3D coding and mosaic, and so on  . Since the observation data has one less dimension with respect to the unknown environment to be estimated, the recovery of 3D structure from 2D projection is not an easy task. Several assumptions have been proposed to simplify the general problem of 3D models from 2D imagery to formulate SFM task. One essential constriant is to assume the objects in the scene are moving rigidly, or, equivalently only the camera is allowed to move BIB003 . Another important simplification is that feature points have been located and correspondences have been established between feature points in the two frames BIB002 . Existing SFM techniques differ from each other in their camera (geometry) model, linearity (in parameters estimation), number of features, restrictions on camera motion and scene structure BIB004 .