5,461 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
Bloom Filters and Compact Hash Codes for Efficient and Distributed Image Retrieval
This paper presents a novel method for efficient image retrieval, based on a
simple and effective hashing of CNN features and the use of an indexing
structure based on Bloom filters. These filters are used as gatekeepers for the
database of image features, allowing to avoid to perform a query if the query
features are not stored in the database and speeding up the query process,
without affecting retrieval performance. Thanks to the limited memory
requirements the system is suitable for mobile applications and distributed
databases, associating each filter to a distributed portion of the database.
Experimental validation has been performed on three standard image retrieval
datasets, outperforming state-of-the-art hashing methods in terms of precision,
while the proposed indexing method obtains a speedup
Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases
In this paper we present an efficient method for visual descriptors retrieval
based on compact hash codes computed using a multiple k-means assignment. The
method has been applied to the problem of approximate nearest neighbor (ANN)
search of local and global visual content descriptors, and it has been tested
on different datasets: three large scale public datasets of up to one billion
descriptors (BIGANN) and, supported by recent progress in convolutional neural
networks (CNNs), also on the CIFAR-10 and MNIST datasets. Experimental results
show that, despite its simplicity, the proposed method obtains a very high
performance that makes it superior to more complex state-of-the-art methods
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
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