• Publications
  • Influence
Object recognition from local scale-invariant features
  • D. Lowe
  • Mathematics, Computer Science
    Proceedings of the Seventh IEEE International…
  • 20 September 1999
TLDR
Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Distinctive Image Features from Scale-Invariant Keypoints
  • D. Lowe
  • Computer Science
    International Journal of Computer Vision
  • 1 November 2004
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.
Unsupervised Learning of Depth and Ego-Motion from Video
TLDR
Empirical evaluation demonstrates the effectiveness of the unsupervised learning framework for monocular depth performs comparably with supervised methods that use either ground-truth pose or depth for training, and pose estimation performs favorably compared to established SLAM systems under comparable input settings.
Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration
TLDR
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.
Automatic Panoramic Image Stitching using Invariant Features
TLDR
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.
Scalable Nearest Neighbor Algorithms for High Dimensional Data
  • Marius Muja, D. Lowe
  • Computer Science, Medicine
    IEEE Transactions on Pattern Analysis and Machine…
  • 1 May 2014
TLDR
It is shown that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and an automated configuration procedure for finding the best algorithm to search a particular data set is described.
Shape indexing using approximate nearest-neighbour search in high-dimensional spaces
  • J. Beis, D. Lowe
  • Mathematics, Computer Science
    Proceedings of IEEE Computer Society Conference…
  • 17 June 1997
TLDR
This paper shows that a new variant of the k-d tree search algorithm makes indexing in higher-dimensional spaces practical, and is integrated into a fully developed recognition system, which is able to detect complex objects in real, cluttered scenes in just a few seconds.
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
  • Jim Mutch, D. Lowe
  • Mathematics, Computer Science
    International Journal of Computer Vision
  • 1 October 2008
TLDR
This work investigates the role of sparsity and localized features in a biologically-inspired model of visual object classification and demonstrates the value of retaining some position and scale information above the intermediate feature level.
Three-Dimensional Object Recognition from Single Two-Dimensional Images
  • D. Lowe
  • Computer Science
    Artif. Intell.
  • 1 March 1987
TLDR
It is argued that similar mechanisms and constraints form the basis for recognition in human vision.
A Boosted Particle Filter: Multitarget Detection and Tracking
TLDR
This work introduces a vision system that is capable of learning, detecting and tracking the objects of interest, and interleaving Adaboost with mixture particle filters, a simple, yet powerful and fully automatic multiple object tracking system.
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