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Part 3: discriminative methods

Part 3: discriminative methods. Discriminative methods. Object detection and recognition is formulated as a classification problem. The image is partitioned into a set of overlapping windows, and a decision is taken at each window about if it contains a target object or not.

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Recognition using Boosting

Recognition using Boosting. Modified from various sources including http:// people.csail.mit.edu/torralba/courses/6.869/6.869.computervision.htm. Background. Computer screen. Bag of image patches. In some feature space. Object Recognition.

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Adaboost and Object Detection

Adaboost and Object Detection. Xu and Arun. Principle of Adaboost. Three cobblers with their wits combined equal Zhuge Liang the master mind. Failure is the mother of success. Weak classifier. Strong classifier. Weight. Features vector. +1 ( ). y t =. -1 ( ).

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Previous weak classifier performs - PowerPoint PPT Presentation

Part 3: discriminative methods

Recognition using Boosting

Adaboost and Object Detection