الفهرس 
AN OVERVIEW OF 3-D OBJECT RECOGNITION APPROACHESOnly 14 pages are availabe for public view
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Abstract
Mobile robots require vision capabilities to sense and adapt to
the environment. The task of 3-D object recognition is an
important component of visual sensing. 3-D object recognition
is however a very difficult task because of the infinite amount of
variations in the 3-D real-life scene.
In this thesis, an appearance-based 3-D object recognition
model that recognizes 3-D objects from multiple 2-D views is
proposed. The framework of the recognition model contains two
main processes: Features extraction process and Learning and
classification process.
Three types of features are used, each independently, in the
recognition process. First, Hu moment features are used for
recognition, as they are invariant to translation, rotation and
translation of objects. This property makes them suitable for the
3-D object recognition task. Second, a combination of the Hu
invariants and the Affine Moment Invariants (AMIs), Hu-AMIs
features, are used. AMIs are invariant to affine transformation
and using them with Hu invariants improves the recognition of
the model. Finally, the color features are used for recognition as
they contain very useful information about the appearance of the
objects.
Learning and recognition are performed using the Support
Vector Machines (SVMs) network. SVMs are newly introduced
networks that have high generalization capability.
Many experiments are performed to demonstrate the
performance of each feature type together with the SVMs in
recognition. The main experiments are:
• Testing the performance of the recognition process as
varying the number of training views per an