Proceedings Article10.1109/DICTA.2009.26
Incremental Object Matching with Bayesian Methods and Particle Filters
Miika Toivanen,Jouko Lampinen +1 more
- 01 Dec 2009
- pp 111-118
3
TL;DR: An incremental method that finds corresponding points of similar object instances, appearing in natural grayscale images with arbitrary location, scale and orientation, and combines the shape and appearance of the corresponding points into the posterior distribution for the location of them is presented.
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Abstract: In batch learning all the training examples have to be available at once to train the model, which often leads to slow performance and large memory requirements. Little work has been done in developing incremental object learners. In this paper, we present an incremental method that finds corresponding points of similar object instances, appearing in natural grayscale images with arbitrary location, scale and orientation. The approach is Bayesian and combines the shape and appearance of the corresponding points into the posterior distribution for the location of them. The posterior distribution is recursively sampled with particle filters to locate the most probable corresponding point sets in the image being processed. The results indicate that the matched corresponding points can be used in forming a representation of the object, which can be used in detecting instances of the object in novel test images.
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Citations
Particle Markov chain Monte Carlo methods
TL;DR: It is shown here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods, which allows not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so.
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Incremental object matching and detection with Bayesian methods and particle filters
M. Toivanen,Jouko Lampinen +1 more
TL;DR: The results indicate that the matched corresponding points can be used in forming a representation of an object with which instances of the object in novel test images are successfully detected.
8
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TL;DR: A novel method of interpreting images using an Active Appearance Model (AAM), a statistical model of the shape and grey-level appearance of the object of interest which can generalise to almost any valid example.
Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
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