Object recognition by dynamic link matching with multiple blob formation

We present a one-to-many object matching system based on neural dynamic link architecture. When an input image containing multiple objects is given, if some of them are similar to a stored model, the system can establish geometric transformation-invariant mappings between the model and the corresponding object regions in the input image. This can be achieved by extending the fast dynamic link matching (FDLM) algorithm to allow multiple blob formation. Numerical simulations of neural layer dynamics indicate that multiple blobs can be developed where the layer input is sufficiently strong against the background level. To extract matched regions from the input layer, we consider each neural layer as a graph and introduce another neural system based on local edge mappings. This system can roughly detect the matched regions with neighborhood-preserving mappings without global cost functions