Book ChapterDOI
CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching
Motilal Agrawal,Kurt Konolige,Morten Rufus Blas +2 more
- pp 102-115
Reads0
Chats0
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
More filters
Book ChapterDOI
A comparative study on mobile visual recognition
TL;DR: This study identifies the algorithmic configurations that manage to optimally balance performance and computational cost, and provide a viable solution for real time mobile visual recognition.
Proceedings ArticleDOI
Optimized feature-detection for on-board vision-based surveillance
TL;DR: An evaluation and comparison of the most popular feature detectors proposed by the computer vision community are drawn and how to automatically adjust these algorithms to changing imaging conditions is analyzed.
Journal ArticleDOI
Evaluation of Keypoint Detectors and Descriptors in Arthroscopic Images for Feature-Based Matching Applications
Andres Marmol,Thierry Peynot,Anders Eriksson,Anjali Jaiprakash,Jonathan Roberts,Ross Crawford +5 more
TL;DR: The first detailed experimental evaluation of the performance of state-of-the-art feature detection and description methods on arthroscopic images is proposed and the best-performing feature in knee-arthroscopy images is DoG+SIFT, while features BRISK+SURF and BRISk+BRISK are recommended for viable implementations in real time.
Book ChapterDOI
System on chip coprocessors for high speed image feature detection and matching
TL;DR: The architecture of the compact, energy-efficient dedicated hardware processors, enabling fast feature detection and matching, are described.
Proceedings ArticleDOI
Visual recognition of a door and its knob for a humanoid robot
TL;DR: The approach is an integrated solution for visual recognition of a door and its knob with minor constraints, and it is shown in the experiment that the humanoid robot can recognize a doorand its knob reliably and quickly.
References
More filters
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.