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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Book ChapterDOI
Local Feature Design Concepts
TL;DR: This chapter examines several concepts related to local feature descriptor design—namely local patterns, shapes, spectra, distance functions, classification, matching, and object recognition, including selected concepts common to both detector and descriptor methods.
Comparison of feature extractors for real- time object detection on android smartphone
Nurul Atiqah Ismail,Ammar Anuar +1 more
TL;DR: This paper presents the analysis of real-time object detection method for embedded system particularly the Android smartphone and shows that FAST algorithm has the best combination of speed and object detection performance.
Journal ArticleDOI
Robust Extrinsic Calibration of Multiple RGB-D Cameras with Body Tracking and Feature Matching.
TL;DR: In this article, a robust registration method of multiple RGB-D cameras was proposed for human body tracking system provided by Azure Kinect SDK to estimate a coarse global registration between cameras and refine it using feature matching However, the matched feature pairs include mismatches, hindering good performance.
Visual navigation for mobile robots using the Bag-of-Words algorithm
TL;DR: BoWSLAM enables a robot to reliably navigate and map previously unknown environments, in real-time, using only a single camera, based on the popular RANSAC framework, and goes considerably further than previous single camera SLAM schemes.
Dissertation
Long-range stereo visual odometry for unmanned aerial vehicles
TL;DR: Novel parameterisations and initialisation routines were developed for the long-range case of stereo visual odometry and new optimisation techniques were implemented to improve the robustness of visual Odometry in this difficult scenario.
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.