الفهرس 
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Abstract
This thesis is an example of how high performance computing tech-
niques can be used to speedup the processing of computationally inten-
sive problems. The problem of machine ability to catgeorize di erent
objects is our selected problem. Object categorization is the task of
classi ng objects into generic classes. Although such task is e ort-less
for humans, it is a complex and computationally intensive for com-
puters. Object categorization is an important task for its di erent
applications such as image annotation, image retrieval, video anno-
tation,surveillance, driver assistance, autonomous robots, interactive
games. It is also used as apreparation step for object recognition.
The performance metric for the categorization task consists of two
key measurements, success categorization rate and system run-time.
Di erent trials to solve the categorization problem are currently in
place. However, few trials consider both of them. In fact, the heavy
processing tasks needed for accurate categorization system lead to in-
crease system run-time. Recently, the high performance techniques
are used to solve the problem of increasing system run-time
In this thesis, we provide a study of 3D object categorization al-
gorithms based on complex feature and a study of di erent high per-
formance computing techniques that could be used to enhance such
algorithms performance. We introduce a system that categorizes 3D
objects based on their depth information. It matches the real time
constraint with high success categorization rate compared to other
existing systems.
In the proposed categorization system, spin-images are selected as
features also Support Vector Machine is selected as a classi er. Spin-
image is a complex feature to be extracted. We investigated the task
dependancy in such feature extraction. Moreover, we modi ed the
original spin-image algorithm to eliminate the unnecessary blocking
tasks based on our task dependancy analysis.
Di erent strategies enhancing spin-point selection in order to en-
hance sucess categorization rate are evaluated. Also, we evaluated the
benift of using bag of features technique for the success categoriza-
tion rate. During our initial experiments, the success categorization
rate was approached 65%. After futher improvements, this rate has
been signi cantly increased such that we have achieved almost 81% in
0.85033 second for each single object.
Eventually, an evaluation to our implemented categorization sys-
tem is carried out in comparison to two di erent types of publicly
available 3D objects datasets. The rst one was the Princeton shape
benchmark. The second one was the RGB-D dataset. Results have
proved that our categorization system provides much more accurate
and faster categorization.