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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Classification of wild animals situated in Slovak Country
Slavomir Matuska,Rober Hudec +1 more
TL;DR: Object classification is extensive and difficult task, which consist of three main steps – feature extraction from training database, training classifier and evaluation of query image.
Um descritor robusto e eficiente de pontos de interesse: desenvolvimento e aplicações
TL;DR: Nesta tese, introduzimos tres novos descritores que combinam de maneira eficiente aparencia e informacao geometrica de images RGB-D, tendo sido superiores em tempo de processamento, consumo of memoria, taxa de reconhecimento e qualidade do registro.
Posted Content
PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data.
TL;DR: In this article, a large dataset of 2D simulations of the flow field around airfoils is presented, which contains 16,000 flow fields with which they tested and compared approaches and showed 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.
Proceedings ArticleDOI
A Comparison of CNN and Classic Features for Image Retrieval
TL;DR: In this article, the authors compare different types of features for keypoint detection using CNNs and BoW features, and show that each type of features are best in different contexts.
Proceedings ArticleDOI
A Fast and Stable Bold Feature Extraction Algorithm
Guangming Tang,Dawei Wei,Jia Hu +2 more
TL;DR: A fast and stable bold extraction algorithm (FSB) that is suitable for real-time application system and compared with SIFT and SURF, the speed is highly improved while their performances are similar.
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.