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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Efficient & Effective Prioritized Matching for Large-Scale Image-Based Localization
TL;DR: This paper presents an approach for large scale image-based localization that is both efficient and effective and demonstrates that it offers the best combination of efficiency and effectiveness among current state-of-the-art approaches for localization.
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The MOPED framework: Object recognition and pose estimation for manipulation
TL;DR: MOPED, a framework for Multiple Object Pose Estimation and Detection that seamlessly integrates single-image and multi-image object recognition and pose estimation in one optimized, robust, and scalable framework is presented.
Proceedings ArticleDOI
Robust text detection in natural images with edge-enhanced Maximally Stable Extremal Regions
TL;DR: A novel text detection algorithm is proposed, which employs edge-enhanced Maximally Stable Extremal Regions as basic letter candidates and Letters are paired to identify text lines, which are subsequently separated into words.
Proceedings ArticleDOI
Visualizing and Understanding Neural Models in NLP
TL;DR: In this article, the authors describe strategies for visualizing compositionality in neural models for NLP, inspired by similar work in computer vision, and introduce methods for visualising a unit's salience, the amount that it contributes to the final composed meaning from first-order derivatives.
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A review of semantic segmentation using deep neural networks
TL;DR: The field of semantic segmentation as pertaining to deep convolutional neural networks is reviewed and comprehensive coverage of the top approaches is provided and the strengths, weaknesses and major challenges are summarized.
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.