15 research outputs found
GASP : Geometric Association with Surface Patches
A fundamental challenge to sensory processing tasks in perception and
robotics is the problem of obtaining data associations across views. We present
a robust solution for ascertaining potentially dense surface patch (superpixel)
associations, requiring just range information. Our approach involves
decomposition of a view into regularized surface patches. We represent them as
sequences expressing geometry invariantly over their superpixel neighborhoods,
as uniquely consistent partial orderings. We match these representations
through an optimal sequence comparison metric based on the Damerau-Levenshtein
distance - enabling robust association with quadratic complexity (in contrast
to hitherto employed joint matching formulations which are NP-complete). The
approach is able to perform under wide baselines, heavy rotations, partial
overlaps, significant occlusions and sensor noise.
The technique does not require any priors -- motion or otherwise, and does
not make restrictive assumptions on scene structure and sensor movement. It
does not require appearance -- is hence more widely applicable than appearance
reliant methods, and invulnerable to related ambiguities such as textureless or
aliased content. We present promising qualitative and quantitative results
under diverse settings, along with comparatives with popular approaches based
on range as well as RGB-D data.Comment: International Conference on 3D Vision, 201
Generalized sequential tree-reweighted message passing
This paper addresses the problem of approximate MAP-MRF inference in general
graphical models. Following [36], we consider a family of linear programming
relaxations of the problem where each relaxation is specified by a set of
nested pairs of factors for which the marginalization constraint needs to be
enforced. We develop a generalization of the TRW-S algorithm [9] for this
problem, where we use a decomposition into junction chains, monotonic w.r.t.
some ordering on the nodes. This generalizes the monotonic chains in [9] in a
natural way. We also show how to deal with nested factors in an efficient way.
Experiments show an improvement over min-sum diffusion, MPLP and subgradient
ascent algorithms on a number of computer vision and natural language
processing problems
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Optimization methods are at the core of many problems in signal/image
processing, computer vision, and machine learning. For a long time, it has been
recognized that looking at the dual of an optimization problem may drastically
simplify its solution. Deriving efficient strategies which jointly brings into
play the primal and the dual problems is however a more recent idea which has
generated many important new contributions in the last years. These novel
developments are grounded on recent advances in convex analysis, discrete
optimization, parallel processing, and non-smooth optimization with emphasis on
sparsity issues. In this paper, we aim at presenting the principles of
primal-dual approaches, while giving an overview of numerical methods which
have been proposed in different contexts. We show the benefits which can be
drawn from primal-dual algorithms both for solving large-scale convex
optimization problems and discrete ones, and we provide various application
examples to illustrate their usefulness
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Building on recent progress at the intersection of combinatorial optimization
and deep learning, we propose an end-to-end trainable architecture for deep
graph matching that contains unmodified combinatorial solvers. Using the
presence of heavily optimized combinatorial solvers together with some
improvements in architecture design, we advance state-of-the-art on deep graph
matching benchmarks for keypoint correspondence. In addition, we highlight the
conceptual advantages of incorporating solvers into deep learning
architectures, such as the possibility of post-processing with a strong
multi-graph matching solver or the indifference to changes in the training
setting. Finally, we propose two new challenging experimental setups. The code
is available at https://github.com/martius-lab/blackbox-deep-graph-matchingComment: ECCV 2020 conference pape
Zero-Assignment Constraint for Graph Matching with Outliers
Graph matching (GM), as a longstanding problem in computer vision and pattern
recognition, still suffers from numerous cluttered outliers in practical
applications. To address this issue, we present the zero-assignment constraint
(ZAC) for approaching the graph matching problem in the presence of outliers.
The underlying idea is to suppress the matchings of outliers by assigning
zero-valued vectors to the potential outliers in the obtained optimal
correspondence matrix. We provide elaborate theoretical analysis to the
problem, i.e., GM with ZAC, and figure out that the GM problem with and without
outliers are intrinsically different, which enables us to put forward a
sufficient condition to construct valid and reasonable objective function.
Consequently, we design an efficient outlier-robust algorithm to significantly
reduce the incorrect or redundant matchings caused by numerous outliers.
Extensive experiments demonstrate that our method can achieve the
state-of-the-art performance in terms of accuracy and efficiency, especially in
the presence of numerous outliers
Product graph-based higher order contextual similarities for inexact subgraph matching
This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem.European Union Horizon 202