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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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Book ChapterDOI

StOCaMo: Online Calibration Monitoring for Stereo Cameras

TL;DR: StOCaMo as discussed by the authors is based on epipolar constraints and validates calibration parameters on a single frame with no temporal tracking, which is shown to be more effective than the standard epipolar error.
Book ChapterDOI

Robust Binary Keypoint Descriptor Based on Local Hierarchical Octagon Pattern

TL;DR: This paper presents a binary keypoint descriptor based on a newly proposed local pattern named local hierarchical octagon pattern (LHOP), which is much faster than SURF and ORB descriptors by creatively combing a newly designed orientation estimation method and the slanted integral image.
Proceedings ArticleDOI

Wide-scoped Around View Detection

TL;DR: A detection system to highlight the wide-scoped around-view monitor system by detecting possible obstacles around the driving environment by estimating the ego-motion of the vehicle using the input image sequence of the cameras.
Journal ArticleDOI

Sequence-Based Filtering for Visual Route-Based Navigation: Analyzing the Benefits, Trade-Offs and Design Choices

- 01 Jan 2022 - 
TL;DR: In this paper , the authors investigated the relationship between the performance of single-frame-based place matching techniques and the use of sequence-based filtering on top of those methods, and analyzed individual trade-offs, properties and limitations for different combinations of single frame-based and sequential techniques.
Patent

Rapid image matching algorithm based on octagonal filter bank

Yiguang Liu, +1 more
TL;DR: In this paper, a novel feature matching algorithm is proposed based on a locally stacked octagonal filter model, which is combined with a newly-provided feature point direction computing method, so that the algorithm is rapider than traditional algorithms including SIFT, SURF, ORB and the like.
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.