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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Proceedings ArticleDOI
Belief-propagation-based robust decoding for two-dimensional barcodes to overcome distortion and occlusion and its extension to multi-view decoding
TL;DR: Experimental results show that the proposed method can decode the 2D code with non-uniform, non-smooth distortions and occlusion.
Registration of synthetic aperture imagery using feature matching.
TL;DR: Overall, oversampled images were shown to provide better feature repeatability, increased density of features, and lower correspondence localisation errors compared to images without oversampling, while feature matching was shown to be problematic for bland scenes with coherence below 0.9.
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View-based underwater SLAM using a stereo camera
TL;DR: This paper proposes an underwater visual simultaneous localization and mapping system using a stereo camera using a global optimization method represented by a graph to eliminate the errors.
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Recent Advances in Baggage Threat Detection: A Comprehensive and Systematic Survey
TL;DR: A structured survey providing systematic insight into state-of-the-art advances in baggage threat detection is presented, including a taxonomy to classify the approaches proposed within the context of 2D and 3D CT X-ray based baggage security threat screening and a comparative analysis of the methods evaluated on four benchmarks.
Book ChapterDOI
Satellite Image Matching and Registration: A Comparative Study Using Invariant Local Features
Mohamed Tahoun,Abd El Rahman Shabayek,Hamed Nassar,Marcello Giovenco,Ralf Reulke,Eid Emary,Aboul Ella Hassanien +6 more
TL;DR: In this paper, a feature-based registration of optical and radar images from same and different sensors using invariant local features is presented, where five of these detectors compute their own descriptors (SIFT, SURF, ORB, BRISK, and BRIEF) while others use the steps involved in SIFT descriptor to compute the feature vectors describing the detected keypoints.
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.