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
Improving Bag-of-Features for Large Scale Image Search
TL;DR: A more precise representation based on Hamming embedding (HE) and weak geometric consistency constraints (WGC) is derived and this approach is shown to outperform the state-of-the-art on the three datasets.
Proceedings Article
Minimal Loss Hashing for Compact Binary Codes
Mohammad Norouzi,David M. Blei +1 more
TL;DR: The formulation is based on structured prediction with latent variables and a hinge-like loss function that is efficient to train for large datasets, scales well to large code lengths, and outperforms state-of-the-art methods.
Posted Content
Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly
TL;DR: A new zero-shot learning dataset is proposed, the Animals with Attributes 2 (AWA2) dataset which is made publicly available both in terms of image features and the images themselves and compares and analyzes a significant number of the state-of-the-art methods in depth.
Journal ArticleDOI
3-D Mapping With an RGB-D Camera
TL;DR: A novel mapping system that robustly generates highly accurate 3-D maps using an RGB-D camera that applies to small domestic robots such as vacuum cleaners, as well as flying robotssuch as quadrocopters.
Journal ArticleDOI
Segmentation-Based Image Copy-Move Forgery Detection Scheme
TL;DR: The main difference to the traditional methods is that the proposed scheme first segments the test image into semantically independent patches prior to keypoint extraction, and the copy-move regions can be detected by matching between these patches.
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.
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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.