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
Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features.
TL;DR: This work proposes a novel BIQA model that utilizes the joint statistics of two types of commonly used local contrast features: 1) the gradient magnitude (GM) map and 2) the Laplacian of Gaussian response.
Proceedings ArticleDOI
Pulling Things out of Perspective
TL;DR: This paper shows that a pixel-wise depth classifier can be reduced to a much simpler classifier predicting only the likelihood of a pixel being at an arbitrarily fixed canonical depth, removing the training data bias towards certain depths and the effect of perspective.
Book ChapterDOI
Simultaneous Localization and Mapping
TL;DR: Das Simultaneous Localization and Mapping Problem, beziehungsweise Teilprobleme davon, werden, je nach verwendeter Sensorik, auch als Bundelausgleichung, Structure from Motion oder SLAM bezeichnet.
Journal ArticleDOI
UAV-based remote sensing of the Super-Sauze landslide : evaluation and results.
TL;DR: In this paper, a radio-controlled mini quad-rotor UAV of the Super-Sauze, France landslide has been used to produce a high-resolution ortho-mosaic of the entire landslide and digital terrain models (DTMs) of several regions.
Proceedings ArticleDOI
Learning to recognize objects in egocentric activities
TL;DR: The key to this approach is a robust, unsupervised bottom up segmentation method, which exploits the structure of the egocentric domain to partition each frame into hand, object, and background categories and uses Multiple Instance Learning to match object instances across sequences.
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.