78,974 research outputs found
Discrete and Continuous Optimization for Motion Estimation
The study of motion estimation reaches back decades and has become one of the central topics of research in computer vision. Even so, there are situations where current approaches fail, such as when there are extreme lighting variations, significant occlusions, or very large motions. In this thesis, we propose several approaches to address these issues. First, we propose a novel continuous optimization framework for estimating optical flow based on a decomposition of the image domain into triangular facets. We show how this allows for occlusions to be easily and naturally handled within our optimization framework without any post-processing. We also show that a triangular decomposition enables us to use a direct Cholesky decomposition to solve the resulting linear systems by reducing its memory requirements. Second, we introduce a simple method for incorporating additional temporal information into optical flow using inertial estimates of the flow, which leads to a significant reduction in error. We evaluate our methods on several datasets and achieve state-of-the-art results on MPI-Sintel. Finally, we introduce a discrete optimization framework for optical flow computation. Discrete approaches have generally been avoided in optical flow because of the relatively large label space that makes them computationally expensive. In our approach, we use recent advances in image segmentation to build a tree-structured graphical model that conforms to the image content. We show how the optimal solution to these discrete optical flow problems can be computed efficiently by making use of optimization methods from the object recognition literature, even for large images with hundreds of thousands of labels
Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks
Architecture optimization, which is a technique for finding an efficient
neural network that meets certain requirements, generally reduces to a set of
multiple-choice selection problems among alternative sub-structures or
parameters. The discrete nature of the selection problem, however, makes this
optimization difficult. To tackle this problem we introduce a novel concept of
a trainable gate function. The trainable gate function, which confers a
differentiable property to discretevalued variables, allows us to directly
optimize loss functions that include non-differentiable discrete values such as
0-1 selection. The proposed trainable gate can be applied to pruning. Pruning
can be carried out simply by appending the proposed trainable gate functions to
each intermediate output tensor followed by fine-tuning the overall model,
using any gradient-based training methods. So the proposed method can jointly
optimize the selection of the pruned channels while fine-tuning the weights of
the pruned model at the same time. Our experimental results demonstrate that
the proposed method efficiently optimizes arbitrary neural networks in various
tasks such as image classification, style transfer, optical flow estimation,
and neural machine translation.Comment: Accepted to AAAI 2020 (Poster
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
Techniques for dense semantic correspondence have provided limited ability to
deal with the geometric variations that commonly exist between semantically
similar images. While variations due to scale and rotation have been examined,
there lack practical solutions for more complex deformations such as affine
transformations because of the tremendous size of the associated solution
space. To address this problem, we present a discrete-continuous transformation
matching (DCTM) framework where dense affine transformation fields are inferred
through a discrete label optimization in which the labels are iteratively
updated via continuous regularization. In this way, our approach draws
solutions from the continuous space of affine transformations in a manner that
can be computed efficiently through constant-time edge-aware filtering and a
proposed affine-varying CNN-based descriptor. Experimental results show that
this model outperforms the state-of-the-art methods for dense semantic
correspondence on various benchmarks
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
We present a global optimization approach to optical flow estimation. The
approach optimizes a classical optical flow objective over the full space of
mappings between discrete grids. No descriptor matching is used. The highly
regular structure of the space of mappings enables optimizations that reduce
the computational complexity of the algorithm's inner loop from quadratic to
linear and support efficient matching of tens of thousands of nodes to tens of
thousands of displacements. We show that one-shot global optimization of a
classical Horn-Schunck-type objective over regular grids at a single resolution
is sufficient to initialize continuous interpolation and achieve
state-of-the-art performance on challenging modern benchmarks.Comment: To be presented at CVPR 201
Disparity and Optical Flow Partitioning Using Extended Potts Priors
This paper addresses the problems of disparity and optical flow partitioning
based on the brightness invariance assumption. We investigate new variational
approaches to these problems with Potts priors and possibly box constraints.
For the optical flow partitioning, our model includes vector-valued data and an
adapted Potts regularizer. Using the notation of asymptotically level stable
functions we prove the existence of global minimizers of our functionals. We
propose a modified alternating direction method of minimizers. This iterative
algorithm requires the computation of global minimizers of classical univariate
Potts problems which can be done efficiently by dynamic programming. We prove
that the algorithm converges both for the constrained and unconstrained
problems. Numerical examples demonstrate the very good performance of our
partitioning method
Joint Optical Flow and Temporally Consistent Semantic Segmentation
The importance and demands of visual scene understanding have been steadily
increasing along with the active development of autonomous systems.
Consequently, there has been a large amount of research dedicated to semantic
segmentation and dense motion estimation. In this paper, we propose a method
for jointly estimating optical flow and temporally consistent semantic
segmentation, which closely connects these two problem domains and leverages
each other. Semantic segmentation provides information on plausible physical
motion to its associated pixels, and accurate pixel-level temporal
correspondences enhance the accuracy of semantic segmentation in the temporal
domain. We demonstrate the benefits of our approach on the KITTI benchmark,
where we observe performance gains for flow and segmentation. We achieve
state-of-the-art optical flow results, and outperform all published algorithms
by a large margin on challenging, but crucial dynamic objects.Comment: 14 pages, Accepted for CVRSUAD workshop at ECCV 201
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
Videos for outdoor scene often show unpleasant blur effects due to the large
relative motion between the camera and the dynamic objects and large depth
variations. Existing works typically focus monocular video deblurring. In this
paper, we propose a novel approach to deblurring from stereo videos. In
particular, we exploit the piece-wise planar assumption about the scene and
leverage the scene flow information to deblur the image. Unlike the existing
approach [31] which used a pre-computed scene flow, we propose a single
framework to jointly estimate the scene flow and deblur the image, where the
motion cues from scene flow estimation and blur information could reinforce
each other, and produce superior results than the conventional scene flow
estimation or stereo deblurring methods. We evaluate our method extensively on
two available datasets and achieve significant improvement in flow estimation
and removing the blur effect over the state-of-the-art methods.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
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