Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TLDR
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.Abstract:
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. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.read more
Citations
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Book ChapterDOI
A Linear Time Histogram Metric for Improved SIFT Matching
Ofir Pele,Michael Werman +1 more
TL;DR: A new metric between histograms such as SIFT descriptors and a linear time algorithm for its computation and extensive experimental results show that the method outperforms state of the art distances.
Proceedings ArticleDOI
Tour the world: Building a web-scale landmark recognition engine
Yan-Tao Zheng,Ming Zhao,Yang Song,Hartwig Adam,Ulrich Buddemeier,Alessandro Bissacco,Fernando Brucher,Tat-Seng Chua,Hartmut Neven +8 more
TL;DR: This paper leverages the vast amount of multimedia data on the Web, the availability of an Internet image search engine, and advances in object recognition and clustering techniques, to address issues of modeling and recognizing landmarks at world-scale.
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CREST: Convolutional Residual Learning for Visual Tracking
TL;DR: In this paper, the discriminative correlation filters (DCFs) are reformulated as a one-layer convolutional neural network for an end-to-end training.
Journal ArticleDOI
Infrared thermography (IRT) applications for building diagnostics: A review
TL;DR: In this article, a review of the state-of-the-art literature and research regarding the passive and active infrared thermography for building diagnostics is presented, with a focus on the building sector, as well as the advantages, limitations and potential sources of errors of IRT employment.
Book ChapterDOI
Description of interest regions with center-symmetric local binary patterns
TL;DR: Experimental results show that the CS-LBP descriptor outperforms the SIFT descriptor for most of the test cases, especially for images with severe illumination variations.
References
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Proceedings ArticleDOI
Object recognition from local scale-invariant features
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Book
Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Multiple View Geometry in Computer Vision.
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
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.
Journal ArticleDOI
Robust wide-baseline stereo from maximally stable extremal regions
TL;DR: The high utility of MSERs, multiple measurement regions and the robust metric is demonstrated in wide-baseline experiments on image pairs from both indoor and outdoor scenes.