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· the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

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Page 1: · the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

Page 2: · the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

Page 3: · the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

Page 4: · the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

Page 5: · the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

Page 6: · the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

Page 7: · the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

Page 8: · the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

Page 9: · the strong/weak classifiers and integral image, Adaboost, ... called a weight vector and wo a bias. Use Figure 3.1. For a point x on the decision surface, show that the normal

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