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Journal ArticleDOI

Distinctive Image Features from Scale-Invariant Keypoints

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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.

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Citations
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Merging Occupancy Grid Maps From Multiple Robots

TL;DR: A concrete approach to multirobot mapping is presented in form of a special similarity metric and a stochastic search algorithm that guides the search algorithm toward optimal solutions.
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Recent Progress in Diagnosing the Reliability of Electrical Equipment by Using Infrared Thermography

TL;DR: Typical engineering solutions using recent technologies are reviewed which could be used to improve the quality of IRT inspection and various automatic diagnostic systems are proposed for faster and more accurate analysis.
Proceedings ArticleDOI

Adaptive as-natural-as-possible image stitching

TL;DR: A novel stitching method, that uses a smooth stitching field over the entire target image, while accounting for all the local transformation variations, that is more robust to parameter selection, and hence more automated compared with state-of-the-art methods.
Proceedings ArticleDOI

Exploring features in a Bayesian framework for material recognition

TL;DR: In this article, an augmented Latent Dirichlet Allocation (aLDA) model was proposed to combine low and mid-level features under a Bayesian generative framework and learn an optimal combination of features.
Proceedings Article

Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images

TL;DR: A factored 3-way RBM is proposed that uses the states of its hidden units to represent abnormalities in the local covariance structure of an image to provide a probabilistic framework for the widely used simple/complex cell architecture.
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

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

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.
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How can distinctive features theory be applied to elision?

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