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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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Proceedings ArticleDOI

Evaluation of low-complexity visual feature detectors and descriptors

TL;DR: An up-to-date detailed, clear, and complete evaluation of local feature detector and descriptors, focusing on the methods that were designed with complexity constraints is provided, providing a much needed reference for researchers in this field.
Journal ArticleDOI

Tissue Tracking and Registration for Image-Guided Surgery

TL;DR: An integrated framework for accurately tracking tissue in surgical stereo-cameras at real-time speeds is presented and the salient feature framework is extended to support region tracking in order to maintain the spatial correspondence of a tracked region of tissue or a medical image registration to the surrounding tissue.
Proceedings ArticleDOI

Vision based robot localization by ground to satellite matching in GPS-denied situations

TL;DR: This paper proposes a two-step approach to matching images captured from an unmanned ground vehicle (UGV) to those from a satellite or high-flying vehicle, and shows that vision-based UGV localization from satellite maps is not only possible, but often provides better position estimates than GPS estimates, enabling the location estimates of Google Street View.
Journal ArticleDOI

Anatomy of the SIFT Method

TL;DR: In this paper, an in-depth analysis of the SIFT method is presented, focusing on the exact computation of the Gaussian scale-space which is at the heart of SIFT as well as most of its competitors.
Journal ArticleDOI

Image features for visual teach-and-repeat navigation in changing environments

TL;DR: An evaluation of standard image features in the context of long-term visual teach-and-repeat navigation of mobile robots, where the environment exhibits significant changes in appearance caused by seasonal weather variations and daily illumination changes, proposes a trainable feature descriptor based on a combination of evolutionary algorithms and Binary Robust Independent Elementary Features, which is called GRIEF.
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.