BIOLOGICALLY INSPIRED OBJECT RECOGNITION SYSTEM

Object Recognition has been a field of interest to many researchers. In fact, it has been
referred to as the most important problem in machine or computer vision. Researchers
have developed many algorithms to solve the problem of object recognition that are
machine vision motivated. On the other hand, biology has motivated researchers to study
the visual system of humans and animals such as monkeys and map it into a
computational model. Some of these models are based on the feed-forward mechanism
of information communication in cortex where the information is communicated
between the different visual areas from the lower areas to the top areas in a feed-forward
manner; however, the performance of these models has been affected much by the
increase of clutter in the scene as well as occlusion. Another mechanism of information
processing in the cortex is called the feedback mechanism, where the information from
the top areas in the visual system is communicated to the lower areas in a feedback
manner; this mechanism has also been mapped into computational models. All these
models which are based on the feed-forward or feedback mechanisms have shown
promising results. However, during the testing of these models, there have been some
issues that affect their performance such as occlusion that prevents objects from being
visible. In addition, scenes that contain high amounts of clutter in them, where there are
so many objects, have also affected the performance of these models. In fact, the
performance has been reported to drop to 74% when systems that are based on these
models are subjected to one or both of the issues mentioned above. The human visual
system, naturally, utilizes both feed-forward and feedback mechanisms in the operation
of perceiving the surrounding environment. Both feed-forward and feedback
mechanisms are integrated in a way that makes the visual system of the human
outperforms any state-of-the-art system. In this research, a proposed model of object
recognition based on the integration concept of the feed-forward and feedback
mechanisms in the human visual system is presented.