PhD ThesisThe existing methods for machine vision translate the three-dimensional
objects in the real world into two-dimensional images. These methods
have achieved acceptable performances in recognising objects. However,
the recognition performance drops dramatically when objects are transformed, for instance, the background, orientation, position in the image,
and scale. The human’s visual cortex has evolved to form an efficient
invariant representation of objects from within a scene. The superior
performance of human can be explained by the feed-forward multi-layer
hierarchical structure of human visual cortex, in addition to, the utilisation of different fields of vision depending on the recognition task.
Therefore, the research community investigated building systems that
mimic the hierarchical architecture of the human visual cortex as an
ultimate objective.
The aim of this thesis can be summarised as developing hierarchical
models of the visual processing that tackle the remaining challenges of
object recognition. To enhance the existing models of object recognition
and to overcome the above-mentioned issues, three major contributions
are made that can be summarised as the followings
1. building a hierarchical model within an abstract architecture that
achieves good performances in challenging image object datasets;
2. investigating the contribution for each region of vision for object
and scene images in order to increase the recognition performance
and decrease the size of the processed data;
3. further enhance the performance of all existing models of object
recognition by introducing hierarchical topologies that utilise the
context in which the object is found to determine the identity of
the object.
Statement ofHigher Committee For Education Development in Iraq (HCED