Serena Falocco

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Il mio gatto

The word “cascade” in the classifier name means that the resultant classifier consists of several simpler classifiers ( stages ) that are applied subsequently to a region of interest until at some stage the candidate is rejected or all the stages are passed. The word “boosted” means that the classifiers at every stage of the cascade are complex themselves and they are built out of basic classifiers using one of four different boosting techniques (weighted voting). Currently Discrete Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported.

The basic classifiers are decision-tree classifiers with at least 2 leaves. Haar-like features are the input to the basic classifers, and are calculated as described below.

The word “cascade” in the classifier name means that the resultant classifier consists of several simpler classifiers ( stages ) that are applied subsequently to a region of interest until at some stage the candidate is rejected or all the stages are passed. The word “boosted” means that the classifiers at every stage of the cascade are complex themselves and they are built out of basic classifiers using one of four different boosting techniques (weighted voting). Currently Discrete Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported.

The word “cascade” in the classifier name means that the resultant classifier consists of several simpler classifiers ( stages ) that are applied subsequently to a region of interest until at some stage the candidate is rejected or all the stages are passed. The word “boosted” means that the classifiers at every stage of the cascade are complex themselves and they are built out of basic classifiers using one of four different boosting techniques (weighted voting). Currently Discrete Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported.

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The word “cascade” in the classifier name means that the resultant classifier consists of several simpler classifiers ( stages ) that are applied subsequently to a region of interest until at some stage the candidate is rejected or all the stages are passed. The word “boosted” means that the classifiers at every stage of the cascade are complex themselves and they are built out of basic classifiers using one of four different boosting techniques (weighted voting). Currently Discrete Adaboost, Real Adaboost, Gentle Adaboost and Logitboost are supported.

Il mio gatto

There is a lot of frameworks and different api, and all of them require lot of learning and code writing to describe what you really want to do. Despite of incredible flexibility, this approach is very annoying. Most of use cases reduce to several standard variations, for which there is no examples at all. Let's look at electronic engineering - it have simple parts, like transistors, ICs, microcontrollers, and circuit was done by hands. But look for state of art in this field - it is model oriented design. Engineer describe process to perform in diagrams and software generate code to run on microprocessor. With this approach it is easy to make changes, maintain and test things. Why not use this in general programming?

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Il mio gatto

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