D
David G. Lowe
Researcher at University of British Columbia
Publications - 108
Citations - 91375
David G. Lowe is an academic researcher from University of British Columbia. The author has contributed to research in topics: Cognitive neuroscience of visual object recognition & Feature (computer vision). The author has an hindex of 52, co-authored 108 publications receiving 83353 citations. Previous affiliations of David G. Lowe include Courant Institute of Mathematical Sciences & Google.
Papers
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: 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.
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.
Proceedings Article
Fast approximate nearest neighbors with automatic algorithm configuration
Marius Muja,David G. Lowe +1 more
TL;DR: A system that answers the question, “What is the fastest approximate nearest-neighbor algorithm for my data?” and a new algorithm that applies priority search on hierarchical k-means trees, which is found to provide the best known performance on many datasets.
Journal ArticleDOI
Automatic Panoramic Image Stitching using Invariant Features
Matthew Brown,David G. Lowe +1 more
TL;DR: This work forms stitching as a multi-image matching problem, and uses invariant local features to find matches between all of the images, and is insensitive to the ordering, orientation, scale and illumination of the input images.
Proceedings ArticleDOI
Unsupervised Learning of Depth and Ego-Motion from Video
TL;DR: In this paper, an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences is presented, which uses single-view depth and multiview pose networks with a loss based on warping nearby views to the target using the computed depth and pose.