Sunday, February 3, 2013
Saturday, February 2, 2013
Object Recognition Day 01
Challenges in visual object recognition
Matching and learning visual objects is challenging because of the following factors.- Illumination conditions
- Object pose
- Camera viewpoint
- Partial occlusions
- Unrelated background clutter
- Computational complexity
- Scalability
State of the art of visual object recognition methods
A good feature extraction method needs to have the following properties.
- Being able to extract local invariant features (e.g. scale invariance, affine invariance)
- Fast to extract
- Robust to viewpoint variations
- Retaining enough discriminative power to allow for reliable matching
References
Kristen Grauman and Bastian Leibe (2011) . Visual Object Recognition (Synthesis Lectures on Artificial Intelligence & Machine Learning). Morgan & Claypool.Visual Information Day 01
Australia's Tax Take - Winners and Losers
![](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhadxsZdMMs8837_8OLIgx3JBZGssEhmPQb1rX9tSLY-I2AxPH_JIaTAdcNSY8u3Z5BTGsaoa7TNcJlaHEtPQRWO7E6Jh14lQm9qqnWCQPO9t4bVDU8pRApQDq2g33-7XSSQFrBmNdvmJA/s640/Australia's+tax+take+-+winners+and+losers.png)
(Source:The Weekend Australia Newspaper, February 2-3, 2013)
(Source:The Age Newspaper, February 2, 2013)
(Source:The Age Newspaper, February 2, 2013)
(Source:The Age Newspaper, February 2, 2013)
Victoria, Australia House prices 1 year to 30 October 2012
(Source: Australian Property Investor Magazine, February 2013 Issue)
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