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Book ChapterDOI

CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching

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TLDR
A suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation are introduced.
Abstract
We explore the suitability of different feature detectors for the task of image registration, and in particular for visual odometry, using two criteria: stability (persistence across viewpoint change) and accuracy (consistent localization across viewpoint change). In addition to the now-standard SIFT, SURF, FAST, and Harris detectors, we introduce a suite of scale-invariant center-surround detectors (CenSurE) that outperform the other detectors, yet have better computational characteristics than other scale-space detectors, and are capable of real-time implementation.

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Citations
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Journal ArticleDOI

Performance Evaluation of Feature Detectors and Descriptors Beyond the Visible

TL;DR: A comprehensive quantitative investigation into the performance of feature detectors and descriptors is presented and would serve to filling the gap in the literature as most analyzes have been based on visible band imagery.
Journal ArticleDOI

Exploring Performance Bounds of Visual Place Recognition Using Extended Precision

TL;DR: To design more reliable and effective localization systems this letter presents a generic evaluation framework based on the new Extended Precision performance metric for VPR that allows assessment of the upper and lower bounds of VPR performance and finds statistically significant performance differences between VPR methods.
Proceedings ArticleDOI

Feature and pose constrained visual Aided Inertial Navigation for computationally constrained aerial vehicles

TL;DR: A Feature and Pose Constrained Extended Kalman Filter (FPC-EKF) is developed for highly dynamic computationally constrained micro aerial vehicles that efficiently incorporates measurements from hundreds of opportunistic visual features to constrain the motion estimate, while allowing navigating and sustained tracking with respect to a few persistent features.
Proceedings ArticleDOI

Autonomous landing and ingress of micro-air-vehicles in urban environments based on monocular vision

TL;DR: This is the first demonstration of onboard, vision-based autonomous landing and ingress algorithms that do not use special purpose scene markers to identify the destination, and it is demonstrated with two different quadrotor MAV platforms.
Journal ArticleDOI

Topological simultaneous localization and mapping: a survey

TL;DR: This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.
References
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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Proceedings ArticleDOI

A Combined Corner and Edge Detector

TL;DR: The problem the authors are addressing in Alvey Project MMI149 is that of using computer vision to understand the unconstrained 3D world, in which the viewed scenes will in general contain too wide a diversity of objects for topdown recognition techniques to work.
Book ChapterDOI

SURF: speeded up robust features

TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Proceedings ArticleDOI

Robust real-time face detection

TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.