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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Journal ArticleDOI
Motion detail preserving optical flow estimation
TL;DR: In this article, a novel optical flow estimation method is proposed, which reduces the reliance of the flow estimates on their initial values propagated from the coarser level and enables recovering many motion details in each scale.
Book ChapterDOI
What does classifying more than 10,000 image categories tell us?
TL;DR: A study of large scale categorization including a series of challenging experiments on classification with more than 10,000 image classes finds that computational issues become crucial in algorithm design and conventional wisdom from a couple of hundred image categories does not necessarily hold when the number of categories increases.
Proceedings ArticleDOI
Object categorization using co-occurrence, location and appearance
TL;DR: This work introduces a novel approach to object categorization that incorporates two types of context-co-occurrence and relative location - with local appearance-based features and uses a conditional random field (CRF) to maximize object label agreement according to both semantic and spatial relevance.
Book
A Review of Point Cloud Registration Algorithms for Mobile Robotics
TL;DR: A historical perspective of the registration problem is given and it is shown that the plethora of solutions can be organized and differentiated according to a few elements and guidelines for the choice of geometric registration configuration are provided.
Journal ArticleDOI
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration
TL;DR: This paper describes an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU), which demonstrates accurate estimation of both the calibration parameters and the local scene structure.
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.