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 | 1 | initial version |
Start with Bayesian Probability, then read about Bayesian Error Rate. From there read about identifying simple properties of an object (such as height). Set a threshold on height, now you have seperation. This is the absolute most basic parametric based object recognition algorithm, but it shows you the concepts of probability and error rates, which is the basis of all (I dare say) all object recognition methods. (parametric and non)
Source: Pattern Classification (duda, hart, stork) - A must read for object recognition.
| | 2 | No.2 Revision |
Start with Bayesian Probability, then read about Bayesian Error Rate. From there read about identifying simple properties of an object (such as height). Set a threshold on height, now you have seperation. separation. This is the absolute most basic parametric based object recognition algorithm, but it shows you the concepts of probability and error rates, which is the basis of all (I dare say) all object recognition methods. (parametric and non)
Source: Pattern Classification (duda, hart, stork) - A must read for object recognition.
| | 3 | No.3 Revision |
Start with Bayesian Probability, then read about Bayesian Error Rate. From there read about identifying simple properties of an object (such as height). Set a threshold on height, now you have separation. This is the absolute most basic parametric based object recognition algorithm, but it shows you the concepts of probability and error rates, which is the basis of all (I dare say) all object recognition methods. (parametric and non)
Source: Pattern Classification (duda, hart, stork) - A must read for object recognition.
Edit: Important things to know(research): sample size, test set vs training set, you need a "Basis" in order to extract.
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