Junction Analysis

by Shawn Arseneau

Gradient-based junctions form key features in such applications as object classification, motion
segmentation, and image enhancement. Asymmetric junctions arise from the merging
of an odd number of contour end-points such as at a Y-junction. Without an asymmetric
representation of such a structure, it will be identified in the same category as X-junctions.
This has severe consequences when distinguishing between features in object classification,
discerning occlusion from disocclusion in motion segmentation and in properly modeling
smoothing boundaries in image enhancement.
A novel method is proposed that results in an improved approximation of the underlying
contours, through the use of asymmetric junctions. The method combines the ability to represent
asymmetric information, as do a number of convolution methods, with the robustness
of local support obtained from diffusion schemes. This work investigates several different
design paradigms of the asymmetric tensor diffusion algorithm. The proposed approach
proved superior to existing techniques by properly accounting for asymmetric junctions
over a wide range of scenarios.