Biologically motivated feature extraction using the spiral architecture

We present a biologically motivated approach to fast feature extraction on hexagonal pixel based images using the concept of eye tremor in combination with the use of the spiral architecture and convolution of non-overlapping gradient masks. We generate seven feature maps “a-trous” that can be combined into a single complete feature map, and we demonstrate that this approach is significantly faster than the use of conventional spiral convolution or the use of a neighbourhood address look-up table on hexagonal images.

[1]  Masayuki Nakajima,et al.  Design and Evaluation of More Accurate Gradient Operators on Hexagonal Lattices , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Qiang Wu,et al.  An Approach to Edge Detection on a Virtual Hexagonal Structure , 2007, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007).

[3]  D. Dacey,et al.  Receptive field structure of H1 horizontal cells in macaque monkey retina. , 2002, Journal of vision.

[4]  Masatoshi Okutomi,et al.  Comparison of image alignment on hexagonal and square lattices , 2010, 2010 IEEE International Conference on Image Processing.

[5]  Peter Baranyi,et al.  Edge detection model based on involuntary eye movements of the eye-retina system , 2007 .

[6]  Jayanthi Sivaswamy,et al.  Hexagonal Image Processing: A Practical Approach , 2014, Advances in Pattern Recognition.

[7]  Phillip Sheridan,et al.  Spiral architecture for machine vision , 1996 .

[8]  Gordon E. Legge,et al.  Visual span: A sensory bottleneck on reading speed , 2010 .

[9]  Alin Achim,et al.  18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, September 11-14, 2011 , 2011, ICIP.

[10]  Bryan W. Scotney,et al.  Adaptive tri-direction edge detection operators based on the spiral architecture , 2010, 2010 IEEE International Conference on Image Processing.