Ordinal Shape Coding and Correlation for Orientation-invariant 2D Shape Matching

The human brain and visual system is highly robust and efficient at recognising objects. Although biologically inspired approaches within the field of Computer Vision are often considered as state of the art, a complete understanding of how the brain and visual system works has not yet been unlocked. Benefits of such an understanding are twofold with respect to Computer Vision: firstly, a more robust object recognition system could be produced and secondly a computer architecture as efficient as the brain and visual system would significantly reduce power requirements. Therefore it is worthy to pursue and evaluate biologically inspired theories of object recognition. This engineering doctorate thesis provides an implementation and evaluation of a biologically inspired theory of object recognition called Ordinal Shape Coding and Correlation (OSCC). The theory is underpinned by relative coding and correlation within the human brain and visual system. A derivation of the theory is illustrated with respect to an implementation alongside proposed extensions. As a result, a hierarchical sequence alignment method is proposed for the correlation of multi- dimensional ordinal shape descriptors for the context of orientation-invariant 2D shape descriptor matching. Orientation-invariant 2D shape descriptor matching evaluations are presented which cover both synthetic data and the public MNIST handwritten digits dataset. Synthetic data evaluations show that the proposed OSCC method can be used as a discriminative orientation-invariant 2D shape descriptor. Furthermore, it is shown that the close competitor Shape Context (SC) method outperforms the OSCC method when applied to the MNIST handwritten digits dataset. However, it is shown that OSCC outperforms the SC method when appearance and bending energy costs are removed from the SC method to compare pure shape descriptors. Future work proposes that bending energy and appearance costs are integrated into the OSCC pipeline for further OCR evaluations.