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.read more
Citations
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Proceedings ArticleDOI
Discriminative spatial saliency for image classification
TL;DR: This work proposes to learn the discriminative spatial saliency of images while simultaneously learning a max margin classifier for a given visual classification task, and treats the saliency maps as latent variables and allow them to adapt to the image content to maximize the classification score, while regularizing the change in thesaliency maps.
Proceedings ArticleDOI
Towards autonomous robotic butlers: Lessons learned with the PR2
Jonathan Bohren,Radu Bogdan Rusu,E. Gil Jones,Eitan Marder-Eppstein,Caroline Pantofaru,Melonee Wise,Lorenz Mosenlechner,Wim Meeussen,Stefan Johannes Josef Holzer +8 more
TL;DR: A new task-level executive system, SMACH, based on hierarchical concurrent state machines, which controls the overall behavior of the system and integrates several new components that are built on top of the PR2's current capabilities.
Proceedings ArticleDOI
Food recognition using statistics of pairwise local features
TL;DR: A new representation for food items is proposed that calculates pairwise statistics between local features computed over a soft pixel-level segmentation of the image into eight ingredient types and is significantly more accurate at identifying food than existing methods.
Journal ArticleDOI
Multiple Kernel Learning for Visual Object Recognition: A Review
TL;DR: It is argued that given a sufficient number of training examples and feature/kernel types, MKL is more effective for object recognition than simple kernel combination, and among the various approaches proposed for MKL, the sequential minimal optimization, semi-infinite programming, and level method based ones are computationally most efficient.
Journal ArticleDOI
Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification.
TL;DR: This paper proposes a novel multi-view learning model which performs clustering/semi-supervised classification and local structure learning simultaneously and can allocate ideal weight for each view automatically without explicit weight definition and penalty parameters.
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.