The goal of object recognition is to label objects from images and
to estimate the poses of the labeled objects. The field of object
recognition has seen tremendous progress with successful
applications in some specific domains such as face recognition.
However, the current state-of-the-art methods show unsatisfactory
results for more general object domains in complex natural
environments with visual ambiguities. In this dissertation, we aim
to enhance the object identification and categorization with the
guide of visual context and graphical model. In this work, we
propose a general framework for the cooperative object
identification and object categorization. Examplars used in
identification provide useful information of similarity in
categorization. Conversely, novel objects are rejected in
identification but the proposed object categorization can label the
novel objects and segment them out for database update in
identification. This work can be helpful to the engineers in
artificial intelligence and machine vision.
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