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Object Identification and Categorization with Visual Context
Hierarchical Graphical Model-based Approaches
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The goal of object recognition is to label objects fromimages and to estimate the poses of the labeled objects. The fieldof object recognition has seen tremendous progress with successfulapplications in some specific domains such as face recognition.However, the current state-of-the-art methods show unsatisfactoryresults for more general object domains in complex naturalenvironments with visual ambiguities. In this dissertation, we aimto enhance the object identification and categorization with theguide of visual context and graphical model. In this work, wepropose a general framework for the cooperative objectidentification and object categorization. Examplars used inidentification provide useful information of similarity incategorization. Conversely, novel objects are rejected inidentification but the proposed object categorization can label thenovel objects and segment them out for database update inidentification. This work can be helpful to the engineers inartificial intelligence and machine vision.
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