1,036 research outputs found
Linear Global Translation Estimation with Feature Tracks
This paper derives a novel linear position constraint for cameras seeing a
common scene point, which leads to a direct linear method for global camera
translation estimation. Unlike previous solutions, this method deals with
collinear camera motion and weak image association at the same time. The final
linear formulation does not involve the coordinates of scene points, which
makes it efficient even for large scale data. We solve the linear equation
based on norm, which makes our system more robust to outliers in
essential matrices and feature correspondences. We experiment this method on
both sequentially captured images and unordered Internet images. The
experiments demonstrate its strength in robustness, accuracy, and efficiency.Comment: Changes: 1. Adopt BMVC2015 style; 2. Combine sections 3 and 5; 3.
Move "Evaluation on synthetic data" out to supplementary file; 4. Divide
subsection "Evaluation on general data" to subsections "Experiment on
sequential data" and "Experiment on unordered Internet data"; 5. Change Fig.
1 and Fig.8; 6. Move Fig. 6 and Fig. 7 to supplementary file; 7 Change some
symbols; 8. Correct some typo
Closed-Loop Learning of Visual Control Policies
In this paper we present a general, flexible framework for learning mappings
from images to actions by interacting with the environment. The basic idea is
to introduce a feature-based image classifier in front of a reinforcement
learning algorithm. The classifier partitions the visual space according to the
presence or absence of few highly informative local descriptors that are
incrementally selected in a sequence of attempts to remove perceptual aliasing.
We also address the problem of fighting overfitting in such a greedy algorithm.
Finally, we show how high-level visual features can be generated when the power
of local descriptors is insufficient for completely disambiguating the aliased
states. This is done by building a hierarchy of composite features that consist
of recursive spatial combinations of visual features. We demonstrate the
efficacy of our algorithms by solving three visual navigation tasks and a
visual version of the classical Car on the Hill control problem
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