Proceedings ArticleDOI
Object recognition from local scale-invariant features
David G. Lowe
- Vol. 2, pp 1150-1157
TLDR
Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.Abstract:
An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.read more
Citations
More filters
Posted Content
Challenges in Representation Learning: A report on three machine learning contests
Ian Goodfellow,Dumitru Erhan,Pierre Luc Carrier,Aaron Courville,Mehdi Mirza,Ben Hamner,William Cukierski,Yichuan Tang,David Thaler,Dong-Hyun Lee,Yingbo Zhou,Chetan Ramaiah,Fangxiang Feng,Ruifan Li,Xiaojie Wang,Dimitris Athanasakis,John Shawe-Taylor,Maxim Milakov,John Park,Radu Ionescu,Marius Popescu,Cristian Grozea,James Bergstra,Jingjing Xie,Lukasz Romaszko,Bing Xu,Zhang Chuang,Yoshua Bengio +27 more
TL;DR: The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge as mentioned in this paper.
Posted Content
An Introduction to Deep Learning for the Physical Layer
Timothy J. O'Shea,Jakob Hoydis +1 more
TL;DR: A fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process is developed.
Proceedings ArticleDOI
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
TL;DR: Three deep neural network architectures are adapted to energy disaggregation and it is found that all three neural nets achieve better F1 scores than either combinatorial optimisation or factorial hidden Markov models and that the neural net algorithms generalise well to an unseen house.
Posted Content
Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, manifold learning, anddeep learning.
Proceedings ArticleDOI
Discriminative clustering for image co-segmentation
TL;DR: This paper combines existing tools for bottom-up image segmentation such as normalized cuts, with kernel methods commonly used in object recognition, used within a discriminative clustering framework to obtain a combinatorial optimization problem which is relaxed to a continuous convex optimization problem that can be solved efficiently for up to dozens of images.
References
More filters
Journal ArticleDOI
Color indexing
Michael J. Swain,Dana H. Ballard +1 more
TL;DR: In this paper, color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models, and they can differentiate among a large number of objects.
Journal ArticleDOI
Generalizing the hough transform to detect arbitrary shapes
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
Journal ArticleDOI
Visual learning and recognition of 3-D objects from appearance
Hiroshi Murase,Shree K. Nayar +1 more
TL;DR: A near real-time recognition system with 20 complex objects in the database has been developed and a compact representation of object appearance is proposed that is parametrized by pose and illumination.
Journal ArticleDOI
Local grayvalue invariants for image retrieval
Cordelia Schmid,Roger Mohr +1 more
TL;DR: This paper addresses the problem of retrieving images from large image databases with a method based on local grayvalue invariants which are computed at automatically detected interest points and allows for efficient retrieval from a database of more than 1,000 images.
Journal ArticleDOI
A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry
TL;DR: A robust approach to image matching by exploiting the only available geometric constraint, namely, the epipolar constraint, is proposed and a new strategy for updating matches is developed, which only selects those matches having both high matching support and low matching ambiguity.