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
Data Efficient Visual Place Recognition Using Extremely JPEG-Compressed Images
Mihnea-Alexandru Tomita,Bruno Ferrarini,Michael Milford,Klaus D. McDonald-Maier,Shoaib Ehsan +4 more
TL;DR: By introducing compression, the VPR performance is drastically reduced, especially in the higher spectrum of compression, and a CNN is presented which is optimized for JPEG compressed data and performs more consistently with the image transformations detected in extremely compressed JPEG images.
Proceedings ArticleDOI
Comparison of deep learning and hand crafted features for mining simulation data
TL;DR: The results show that the deep learning-based methods, as well as hand crafted feature based approaches, are well-capable to accurately describe the content of the CFD simulation output on the proposed dataset.
Book ChapterDOI
Biologically Inspired Keypoints
TL;DR: This chapter describes methods to extract and represent biologically inspired keypoints and shows how to reconstruct a keypoint descriptor to qualitatively analyze its behavior.
Journal ArticleDOI
An Optical Remote Sensing Image Matching Method Based on the Simple and Stable Feature Database
Hui Long,Hongjian You +1 more
TL;DR: In this article , the authors proposed an optical remote sensing image matching method based on a simple stable feature database, which can save storage space for reference data while increasing image processing speed.
Book ChapterDOI
Performance Analysis of Feature Detection and Description (FDD) Methods on Accident Images
TL;DR: The result shows, under feature detectors, CenSurE and SIFT performs better with reference to repeatability and matching score, while SIFT and ORB are better in the category of feature descriptors for the analysis of accident images.
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.