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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Journal ArticleDOI
Local image features matching for real-time seabed tracking applications
Bogdan Żak,Stanisław Hożyń +1 more
TL;DR: Investigation of the most popular feature detection and description algorithms such as SIFT, SURF, FAST, STAR, HARRIS, ORB, BRISK and FREAK indicates that the combination of the histogram equalisation technique and ORB detector and descriptor enables real-time seabed tracking with sufficient efficiency.
Book ChapterDOI
Good Edgels to Track: Beating the Aperture Problem with Epipolar Geometry
TL;DR: This work presents a method to extract edgels, which can be effectively tracked given a known camera motion scenario, and shows how a constrained version of the Lucas-Kanade tracking procedure can efficiently exploit epipolar geometry to reduce the classical KLT optimization to a 1D search problem.
Proceedings ArticleDOI
An Exploration of Feature Detector Performance in the Thermal-Infrared Modality
TL;DR: The first comprehensive study on feature detector performance on thermal-infrared images is conducted and a limiting of feature counts was found to improve the repeatability performance of several detectors.
BookDOI
Advanced Topics in Computer Vision
TL;DR: This book investigates visual features, trajectory features, and stereo matching, and reviews the main challenges of semi-supervised object recognition, and presents a framework for the visual localization of MAVs and a boosting approach for generalizing the k-NN rule.
Proceedings ArticleDOI
Segmentation Based Features for Wide-Baseline Multi-view Reconstruction
TL;DR: A novel segmentation based feature detector SFD is introduced that produces an increased number of 'good' features for accurate wide-baseline reconstruction and demonstrates at least a factor six increase in the number of reconstructed points with reduced error distribution when evaluated against ground-truth and similar computational cost to SURF/FAST.
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.