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The problem of object detection in image and video has been treated by a large number of researchers. Many design factors degrade the reliability of the problem solutions, such as manual modeling of the object, manual features selection, handcrafting architecture, and learning algorithm selection. Here, a generalized object detection and localization system is presented. It has the ability to learn the object model with the processes of feature selection and architecture building automated by adopting the AdaBoost algorithm as a feature selection and meta-learning algorithm. The output of the training phase is a cascade of classifiers which can be used to classify parts of an image within a search window as either object or non object.