Book ChapterDOI
CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching
Motilal Agrawal,Kurt Konolige,Morten Rufus Blas +2 more
- pp 102-115
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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.read more
Citations
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Proceedings ArticleDOI
Appearance and Geometry Fusion for Enhanced Dense 3D Alignment
Erickson R. Nascimento,William Robson Schwartz,Gabriel L. Oliveira,Antonio W. Veira,Mario F. M. Campos,Daniel Balbino de Mesquita +5 more
TL;DR: A novel RGB-D feature descriptor called Binary Appearance and Shape Elements (BASE) is proposed that efficiently combines intensity and shape information to improve the discriminative power and enable an enhanced and faster matching process.
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Feature Learning Architecture Taxonomy and Neuroscience Background
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Book ChapterDOI
Robust Binary Feature Using the Intensity Order
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Proceedings ArticleDOI
Active-imaging-based underwater navigation
TL;DR: In this article, the authors present the first results of a feasibility study of an active-imaging-based localization method which uses a range-gated active imaging system and can yield radiometric and odometric information even in turbid water.
Qualitative Localization using Vision and Odometry for Path Following in Topo-metric Maps.
TL;DR: An optimized version of the loop-closure detection algorithm is presented that makes it possible to create consistent topo-metric maps in real-time while the robot is teleoperated and performs qualitative localization using the same loop- closure detection framework and the odometry data.
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
Chris Harris,Mike Stephens +1 more
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
Paul A. Viola,Michael Jones +1 more
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.