Simple Trial for Border Ownership Detection

I implemented a simple algorithm to determine the border ownership in a visual receptive field.  The idea is based on the physiological finding that there are neurons in the visual field, excitatory for cross-directional nearby edges and inhibitory for iso-directional nearby edges.  Researchers such as Sakai and Nishimura (2006) have proposed models of border ownership coding with this finding.
What I have done is:

• Apply Gabor filters to find 0° and 90° edges.
• Sum up excitatory and inhibitory effects for cross and iso-directional nearby edges.
• Determine which side is more excitatory for each edge.

(Coding was done with OpenCV Java API.)

The following are sample pictures:

Fig1: the input

Fig2: Gabor filter applied (0°)
(There are two edge lines for
the right side due to, perhaps,
the phase setting of Gabor filter.)

Fig3: Gabor filter applied (90°)

Fig4: Border ownership detected
(indicated with gray area in either
side of edges (Fig2 + Fig))

Afterthoughts

The experiment was part of my attempt to find an algorithm for identifying Spelke's objects.  While the algorithm I tried above may be a heuristics used in the brain, it does not seem so straightforward to represent the coherence of Spelke's objects such as local color coherence over time.  So, I leave the result above as tentative and go for exploring other algorithms.  Besides, to make the algorithm efficient, I would have to hack around the code (it took 15 seconds to process the Lenna picture without sensible result).

Reference

• Jonathan R. Williford and Rudiger von der Heydt (2013). Border-ownership coding. Scholarpedia, 8(10):30040.
• Sakai, K. and Nishimura, H. (2006). Surrounding suppression and facilitation in the determination of border ownership. Journal of Cognitive Neuroscience 18 (4): 562-579. doi:10.1162/jocn.2006.18.4.562.