1,690 research outputs found
Robust 3-Dimensional Object Recognition using Stereo Vision and Geometric Hashing
We propose a technique that combines geometric hashing with stereo vision. The idea is to use the robustness of geometric hashing to spurious data to overcome the correspondence problem, while the stereo vision setup enables direct model matching using the 3-D object models. Furthermore, because the matching technique relies on the relative positions of local features, we should be able to perform robust recognition even with partially occluded objects. We tested this approach with simple geometric objects using a corner point detector. We successfully recognized objects even in scenes where the objects were partially occluded by other objects. For complicated scenes, however, the limited set of model features and required amount of computing time, sometimes became a proble
Astrometry.net: Blind astrometric calibration of arbitrary astronomical images
We have built a reliable and robust system that takes as input an
astronomical image, and returns as output the pointing, scale, and orientation
of that image (the astrometric calibration or WCS information). The system
requires no first guess, and works with the information in the image pixels
alone; that is, the problem is a generalization of the "lost in space" problem
in which nothing--not even the image scale--is known. After robust source
detection is performed in the input image, asterisms (sets of four or five
stars) are geometrically hashed and compared to pre-indexed hashes to generate
hypotheses about the astrometric calibration. A hypothesis is only accepted as
true if it passes a Bayesian decision theory test against a background
hypothesis. With indices built from the USNO-B Catalog and designed for
uniformity of coverage and redundancy, the success rate is 99.9% for
contemporary near-ultraviolet and visual imaging survey data, with no false
positives. The failure rate is consistent with the incompleteness of the USNO-B
Catalog; augmentation with indices built from the 2MASS Catalog brings the
completeness to 100% with no false positives. We are using this system to
generate consistent and standards-compliant meta-data for digital and digitized
imaging from plate repositories, automated observatories, individual scientific
investigators, and hobbyists. This is the first step in a program of making it
possible to trust calibration meta-data for astronomical data of arbitrary
provenance.Comment: submitted to A
Bayesian matching of unlabelled point sets using Procrustes and configuration models
The problem of matching unlabelled point sets using Bayesian inference is
considered. Two recently proposed models for the likelihood are compared, based
on the Procrustes size-and-shape and the full configuration. Bayesian inference
is carried out for matching point sets using Markov chain Monte Carlo
simulation. An improvement to the existing Procrustes algorithm is proposed
which improves convergence rates, using occasional large jumps in the burn-in
period. The Procrustes and configuration methods are compared in a simulation
study and using real data, where it is of interest to estimate the strengths of
matches between protein binding sites. The performance of both methods is
generally quite similar, and a connection between the two models is made using
a Laplace approximation
Visual localisation of electricity pylons for power line inspection
Inspection of power infrastructure is a regular maintenance event. To date the inspection process has mostly been done manually, but there is growing interest in automating the process. The automation of the inspection process will require an accurate means for the localisation of the power infrastructure components. In this research, we studied the visual localisation of a pylon. The pylon is the most prominent component of the power infrastructure and can provide a context for the inspection of the other components. Point-based descriptors tend to perform poorly on texture less objects such as pylons, therefore we explored the localisation using convolutional neural networks and geometric constraints. The crossings of the pylon, or vertices, are salient points on the pylon. These vertices aid with recognition and pose estimation of the pylon. We were successfully able to use a convolutional neural network for the detection of the vertices. A model-based technique, geometric hashing, was used to establish the correspondence between the stored pylon model and the scene object. We showed the effectiveness of the method as a voting technique to determine the pose estimation from a single image. In a localisation framework, the method serves as the initialization of the tracking process. We were able to incorporate an extended Kalman filter for subsequent incremental tracking of the camera relative to the pylon. Also, we demonstrated an alternative tracking using heatmap details from the vertex detection. We successfully demonstrated the proposed algorithms and evaluated their effectiveness using a model pylon we built in the laboratory. Furthermore, we revalidated the results on a real-world outdoor electricity pylon. Our experiments illustrate that model-based techniques can be deployed as part of the navigation aspect of a robot
Computation of protein geometry and its applications: Packing and function prediction
This chapter discusses geometric models of biomolecules and geometric
constructs, including the union of ball model, the weigthed Voronoi diagram,
the weighted Delaunay triangulation, and the alpha shapes. These geometric
constructs enable fast and analytical computaton of shapes of biomoleculres
(including features such as voids and pockets) and metric properties (such as
area and volume). The algorithms of Delaunay triangulation, computation of
voids and pockets, as well volume/area computation are also described. In
addition, applications in packing analysis of protein structures and protein
function prediction are also discussed.Comment: 32 pages, 9 figure
Learning to Navigate the Energy Landscape
In this paper, we present a novel and efficient architecture for addressing
computer vision problems that use `Analysis by Synthesis'. Analysis by
synthesis involves the minimization of the reconstruction error which is
typically a non-convex function of the latent target variables.
State-of-the-art methods adopt a hybrid scheme where discriminatively trained
predictors like Random Forests or Convolutional Neural Networks are used to
initialize local search algorithms. While these methods have been shown to
produce promising results, they often get stuck in local optima. Our method
goes beyond the conventional hybrid architecture by not only proposing multiple
accurate initial solutions but by also defining a navigational structure over
the solution space that can be used for extremely efficient gradient-free local
search. We demonstrate the efficacy of our approach on the challenging problem
of RGB Camera Relocalization. To make the RGB camera relocalization problem
particularly challenging, we introduce a new dataset of 3D environments which
are significantly larger than those found in other publicly-available datasets.
Our experiments reveal that the proposed method is able to achieve
state-of-the-art camera relocalization results. We also demonstrate the
generalizability of our approach on Hand Pose Estimation and Image Retrieval
tasks
- …