11,767 research outputs found
Better Feature Tracking Through Subspace Constraints
Feature tracking in video is a crucial task in computer vision. Usually, the
tracking problem is handled one feature at a time, using a single-feature
tracker like the Kanade-Lucas-Tomasi algorithm, or one of its derivatives.
While this approach works quite well when dealing with high-quality video and
"strong" features, it often falters when faced with dark and noisy video
containing low-quality features. We present a framework for jointly tracking a
set of features, which enables sharing information between the different
features in the scene. We show that our method can be employed to track
features for both rigid and nonrigid motions (possibly of few moving bodies)
even when some features are occluded. Furthermore, it can be used to
significantly improve tracking results in poorly-lit scenes (where there is a
mix of good and bad features). Our approach does not require direct modeling of
the structure or the motion of the scene, and runs in real time on a single CPU
core.Comment: 8 pages, 2 figures. CVPR 201
Reducing "Structure From Motion": a General Framework for Dynamic Vision - Part 2: Experimental Evaluation
A number of methods have been proposed in the literature for estimating scene-structure and ego-motion from a sequence of images using dynamical models. Although all methods may be derived from a "natural" dynamical model within a unified framework, from an engineering perspective there are a number of trade-offs that lead to different strategies depending upon the specific applications and the goals one is targeting.
Which one is the winning strategy? In this paper we analyze the properties of the dynamical models that originate from each strategy under a variety of experimental conditions. For each model we assess the accuracy of the estimates, their robustness to measurement noise, sensitivity to initial conditions and visual angle, effects of the bas-relief ambiguity and occlusions, dependence upon the number of image measurements and their sampling rate
Reducing “Structure from Motion”: a general framework for dynamic vision. 2. Implementation and experimental assessment
For pt.1 see ibid., p.933-42 (1998). A number of methods have been proposed in the literature for estimating scene-structure and ego-motion from a sequence of images using dynamical models. Despite the fact that all methods may be derived from a “natural” dynamical model within a unified framework, from an engineering perspective there are a number of trade-offs that lead to different strategies depending upon the applications and the goals one is targeting. We want to characterize and compare the properties of each model such that the engineer may choose the one best suited to the specific application. We analyze the properties of filters derived from each dynamical model under a variety of experimental conditions, assess the accuracy of the estimates, their robustness to measurement noise, sensitivity to initial conditions and visual angle, effects of the bas-relief ambiguity and occlusions, dependence upon the number of image measurements and their sampling rate
Shape Interaction Matrix Revisited and Robustified: Efficient Subspace Clustering with Corrupted and Incomplete Data
The Shape Interaction Matrix (SIM) is one of the earliest approaches to
performing subspace clustering (i.e., separating points drawn from a union of
subspaces). In this paper, we revisit the SIM and reveal its connections to
several recent subspace clustering methods. Our analysis lets us derive a
simple, yet effective algorithm to robustify the SIM and make it applicable to
realistic scenarios where the data is corrupted by noise. We justify our method
by intuitive examples and the matrix perturbation theory. We then show how this
approach can be extended to handle missing data, thus yielding an efficient and
general subspace clustering algorithm. We demonstrate the benefits of our
approach over state-of-the-art subspace clustering methods on several
challenging motion segmentation and face clustering problems, where the data
includes corrupted and missing measurements.Comment: This is an extended version of our iccv15 pape
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