Object recognition is a natural process of the human brain performed in the visual cortex. It is dependent on a binocular depth perception system that renders a three-dimensional representation of the objects in a scene. Hitherto, computer and software systems are been used to simulate the perception of three-dimensional environments with the aid of sensors to capture real-time images. In the process, such images are used as input data for further analysis and development of algorithms, an essential ingredient for simulating the complexity of human vision, so as to achieve scene interpretation for object recognition, similar to the way the human brain perceives it.
The rapid pace of technological advancements in hardware and software, are continuously bringing the machine-based process for object recognition nearer to the inhuman vision prototype. The key in this field, is the development of algorithms in order to achieve robust scene interpretation. A lot of recognizable and significant effort has been successfully carried out over the years in 2D object recognition, as opposed to 3D.
Significant research still needs to be done towards the enhancement of 3D object recognition in order to achieve a better interpretation and understanding of reality and the relationship between objects in a scene.
My goal is to continue my research in this field and to show how RGB and depth information can be utilized in order to develop a new class of 3D object recognition algorithms, analogous to the perception processed by the human brain.