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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Journal ArticleDOI
Corner detector based on global and local curvature properties
Xiaochen He,Nelson H. C. Yung +1 more
TL;DR: A curvature-based corner detector that detects both fine and coarse features accurately at low computational cost and forms extremely well in both fields is proposed.
Journal ArticleDOI
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
TL;DR: The proposed method for the selection of salient regions likely to contain objects, based on bottom-up visual attention, can enable one-shot learning of multiple objects from complex scenes, and can strongly improve learning and recognition performance in the presence of large amounts of clutter.
Proceedings ArticleDOI
Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild
TL;DR: This paper proposes to jointly optimize a part-based, trained in-the-wild, flexible appearance model along with a global shape model which results in a joint translational motion model for the model parts via Gauss-Newton (GN) optimization.
Proceedings ArticleDOI
Pagerank for product image search
Yushi Jing,Shumeet Baluja +1 more
TL;DR: This paper cast the image-ranking problem into the task of identifying "authority" nodes on an inferred visual similarity graph and proposes an algorithm to analyze the visual link structure that can be created among a group of images.
Proceedings ArticleDOI
Tracking the invisible: Learning where the object might be
TL;DR: This work proposes a method to learn supporters which are, be it only temporally, useful for determining the position of the object of interest and exploits the General Hough Transform strategy.
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.