137 research outputs found
Stereo image processing system for robot vision
More and more applications (path planning, collision avoidance
methods) require 3D description of the surround world. This paper
describes a stereo vision system that uses 2D (grayscale or color) images
to extract simple 2D geometric entities (points, lines) applying a
low-level feature detector. The features are matched across views with a
graph matching algorithm. During the projective reconstruction the 3D
description of the scene is recovered. The developed system uses uncalibrated
cameras, therefore only projective 3D structure can be detected
defined up to a collineation. Using the Euclidean information about a
known set of predefined objects stored in database and the results of the
recognition algorithm, the description can be updated to a metric one
Tracking Cell Signals in Fluorescent Images
In this paper we present the techniques for tracking cell signal in GFP (Green Fluorescent Protein) images of growing cell colonies. We use such tracking for both data extraction and dynamic modeling of intracellular processes. The techniques are based on optimization of energy functions, which simultaneously determines cell correspondences, while estimating the mapping functions. In addition to spatial mappings such as affine and Thin-Plate Spline mapping, the cell growth and cell division histories must be estimated as well. Different levels of joint optimization are discussed. The most unusual tracking feature addressed in this paper is the possibility of one-to-two correspondences caused by cell division. A novel extended softassign algorithm for solutions of one-to-many correspondences is detailed in this paper. The techniques are demonstrated on three sets of data: growing bacillus Subtillus and e-coli colonies and a developing plant shoot apical meristem. The techniques are currently used by biologists for data extraction and hypothesis formation
Correspondence matching with modal clusters
The modal correspondence method of Shapiro and Brady aims to match point-sets by comparing the eigenvectors of a pairwise point proximity matrix. Although elegant by means of its matrix representation, the method is notoriously susceptible to differences in the relational structure of the point-sets under consideration. In this paper, we demonstrate how the method can be rendered robust to structural differences by adopting a hierarchical approach. To do this, we place the modal matching problem in a probabilistic setting in which the correspondences between pairwise clusters can be used to constrain the individual point correspondences. We demonstrate the utility of the method on a number of synthetic and real-world point-pattern matching problems
Deep Reinforcement Learning of Graph Matching
Graph matching (GM) under node and pairwise constraints has been a building
block in areas from combinatorial optimization, data mining to computer vision,
for effective structural representation and association. We present a
reinforcement learning solver for GM i.e. RGM that seeks the node
correspondence between pairwise graphs, whereby the node embedding model on the
association graph is learned to sequentially find the node-to-node matching.
Our method differs from the previous deep graph matching model in the sense
that they are focused on the front-end feature extraction and affinity function
learning, while our method aims to learn the back-end decision making given the
affinity objective function whether obtained by learning or not. Such an
objective function maximization setting naturally fits with the reinforcement
learning mechanism, of which the learning procedure is label-free. These
features make it more suitable for practical usage. Extensive experimental
results on both synthetic datasets, Willow Object dataset, Pascal VOC dataset,
and QAPLIB showcase superior performance regarding both matching accuracy and
efficiency. To our best knowledge, this is the first deep reinforcement
learning solver for graph matching
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