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Fast Matching of Binary Descriptors for Large-scale Applications in Robot Vision
Örebro University, School of Science and Technology.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3122-693X
2016 (English)In: International Journal of Advanced Robotic Systems, ISSN 1729-8806, E-ISSN 1729-8814, Vol. 13, article id 58Article in journal (Refereed) Published
Resource type
Text
Abstract [en]

The introduction of computationally efficient binary feature descriptors has raised new opportunities for real-world robot vision applications. However, brute force feature matching of binary descriptors is only practical for smaller datasets. In the literature, there has therefore been an increasing interest in representing and matching binary descriptors more efficiently. In this article, we follow this trend and present a method for efficiently and dynamically quantizing binary descriptors through a summarized frequency count into compact representations (called fsum) for improved feature matching of binary pointfeatures. With the motivation that real-world robot applications must adapt to a changing environment, we further present an overview of the field of algorithms, which concerns the efficient matching of binary descriptors and which are able to incorporate changes over time, such as clustered search trees and bag-of-features improved by vocabulary adaptation. The focus for this article is on evaluation, particularly large scale evaluation, compared to alternatives that exist within the field. Throughout this evaluation it is shown that the fsum approach is both efficient in terms of computational cost and memory requirements, while retaining adequate retrieval accuracy. It is further shown that the presented algorithm is equally suited to binary descriptors of arbitrary type and that the algorithm is therefore a valid option for several types of vision applications.

Place, publisher, year, edition, pages
Rijeka, Croatia: InTech, 2016. Vol. 13, article id 58
Keywords [en]
Binary Descriptors, Efficient Feature Matching, Real-world Robotic Vision Applications
National Category
Robotics
Identifiers
URN: urn:nbn:se:oru:diva-49931DOI: 10.5772/62162ISI: 000372839300002OAI: oai:DiVA.org:oru-49931DiVA, id: diva2:923401
Funder
Swedish Research Council, 2011-6104Available from: 2016-04-26 Created: 2016-04-26 Last updated: 2019-04-05Bibliographically approved
In thesis
1. Studies in Semantic Modeling of Real-World Objects using Perceptual Anchoring
Open this publication in new window or tab >>Studies in Semantic Modeling of Real-World Objects using Perceptual Anchoring
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Autonomous agents, situated in real-world scenarios, need to maintain consonance between the perceived world (through sensory capabilities) and their internal representation of the world in the form of symbolic knowledge. An approach for modeling such representations of objects is through the concept of perceptual anchoring, which, by definition, handles the problem of creating and maintaining, in time and space, the correspondence between symbols and sensor data that refer to the same physical object in the external world.

The work presented in this thesis leverages notations found within perceptual anchoring to address the problem of real-world semantic world modeling, emphasizing, in particular, sensor-driven bottom-up acquisition of perceptual data. The proposed method for handling the attribute values that constitute the perceptual signature of an object is to first integrate and explore available resources of information, such as a Convolutional Neural Network (CNN) to classify objects on the perceptual level. In addition, a novel anchoring matching function is proposed. This function introduces both the theoretical procedure for comparing attribute values, as well as establishes the use of a learned model that approximates the anchoring matching problem. To verify the proposed method, an evaluation using human judgment to collect annotated ground truth data of real-world objects is further presented. The collected data is subsequently used to train and validate different classification algorithms, in order to learn how to correctly anchor objects, and thereby learn to invoke correct anchoring functionality.

There are, however, situations that are difficult to handle purely from the perspective of perceptual anchoring, e.g., situations where an object is moved during occlusion. In the absence of perceptual observations, it is necessary to couple the anchoring procedure with probabilistic object tracking to speculate about occluded objects, and hence, maintain a consistent world model. Motivated by the limitation in the original anchoring definition, which prohibited the modeling of the history of an object, an extension to the anchoring definition is also presented. This extension permits the historical trace of an anchored object to be maintained and used for the purpose of learning additional properties of an object, e.g., learning of the action applied to an object.

Place, publisher, year, edition, pages
Örebro: Örebro University, 2019. p. 93
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 83
Keywords
Perceptual Anchoring, Semantic World Modeling, Sensor-Driven Acquisition of Data, Object Recognition, Object Classification, Symbol Grounding, Probabilistic Object Tracking
National Category
Information Systems
Identifiers
urn:nbn:se:oru:diva-73175 (URN)978-91-7529-283-0 (ISBN)
Public defence
2019-04-29, Örebro universitet, Teknikhuset, Hörsal T, Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2020-02-14Bibliographically approved

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Persson, AndreasLoutfi, Amy

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