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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Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images.
TL;DR: This paper compares the performance of three different image matching techniques, i.e., SIFT, SURF, and ORB, against different kinds of transformations and deformations such as scaling, rotation, noise, fish eye distortion, and shearing and shows that which algorithm is the best more robust against each kind of distortion.
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Deep Convolutional Ranking for Multilabel Image Annotation
TL;DR: In this paper, a significant performance gain could be obtained by combining convolutional architectures with approximate top-k$ ranking objectives, as they naturally fit the multilabel tagging problem.
Proceedings ArticleDOI
Real-Time Monocular SLAM with Straight Lines
TL;DR: This work describes how straight lines can be added to a monocular Extended Kalman Filter Simultaneous Mapping and Localisation (EKF SLAM) system in a manner that is both fast and which integrates easily with point features.
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Aggregating Deep Convolutional Features for Image Retrieval
Artem Babenko,Victor Lempitsky +1 more
TL;DR: This paper investigates possible ways to aggregate local deep features to produce compact global descriptors for image retrieval and shows that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated.
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Rigid-Motion Scattering for Texture Classification.
Laurent Sifre,Stéphane Mallat +1 more
TL;DR: A rigid-motion scattering computes adaptive invariants along translations and rotations, with a deep convolutional network, that preserves joint rotation and translation information, while providing global invariants at any desired scale.
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.