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
Aggregating Local Image Descriptors into Compact Codes
TL;DR: This paper first presents and evaluates different ways of aggregating local image descriptors into a vector and shows that the Fisher kernel achieves better performance than the reference bag-of-visual words approach for any given vector dimension.
Proceedings ArticleDOI
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
TL;DR: PoseNet as mentioned in this paper uses a CNN to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation.
Proceedings ArticleDOI
Lost in quantization: Improving particular object retrieval in large scale image databases
TL;DR: In this paper, a weighted set of visual words is obtained by selecting words based on proximity in descriptor space, and this representation may be incorporated into a standard tf-idf architecture and how spatial verification is modified in the case of this soft-assignment.
Proceedings ArticleDOI
Understanding deep image representations by inverting them
TL;DR: In this article, a general framework was proposed to invert representations such as HOG and Bag of Visual Words (BOW) to reconstruct the image itself, which can be applied to CNNs too.
Proceedings ArticleDOI
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
TL;DR: This work integrates the cascade-of-rejectors approach with the Histograms of Oriented Gradients features to achieve a fast and accurate human detection system that can process 5 to 30 frames per second depending on the density in which the image is scanned, while maintaining an accuracy level similar to existing methods.
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.