A hierarchical SOM to identify and recognize objects in sequences of stereo images

Abstract

Identification and recognition of objects in digital images is a fundamental task in robotic vision. Here we propose an approach based on clustering of features extracted from HSV color space and depth, using a hierarchical self organizing map (HSOM). Binocular images are first preprocessed using a watershed algorithm; adjacent regions are then merged based on HSV similarities. For each region we compute a six element features vector: median depth (computed as disparity), median H, S, V values, and the X and Y coordinates of its centroid. These are the input to the HSOM network which is allowed to learn on the first image of a sequence. The trained network is then used to segment other images of the same scene. If, on the new image, the same neuron responds to regions that belong to the same object, the object is considered as recognized. The technique achieves good results, recognizing up to 82% of the objects.