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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.

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Citations
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Journal ArticleDOI

Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning

TL;DR: A set of kinematic features that are derived from the optical flow for human action recognition in videos, including divergence, vorticity, symmetric and antisymmetric flow fields, and third principal invariant of rate of rotation tensor is proposed.
Proceedings ArticleDOI

From structure-from-motion point clouds to fast location recognition

TL;DR: A fast location recognition technique based on structure from motion point clouds is presented, and Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database.
Patent

Methods and arrangements for identifying objects

TL;DR: In this article, product packaging is digitally watermarked over most of its extent to facilitate high-throughput item identification at retail checkouts, and piles of items can be 3D-modelled and virtually segmented into geometric primitives to aid identification.
Journal ArticleDOI

Real-time markerless tracking for augmented reality: the virtual visual servoing framework

TL;DR: In this paper, nonlinear pose estimation is formulated by means of a virtual visual servoing approach and has been validated on several complex image sequences including outdoor environments.
Journal ArticleDOI

Understanding deep convolutional networks.

TL;DR: Deep convolutional networks provide state-of-the-art classifications and regressions results over many high-dimensional problems and a mathematical framework is introduced to analyse their properties.
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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