Using density and spatial cues for clustering image pixels in real time

The goal of our work is efficient clustering of object pixels from a sequence of live images for use in real-time applications including object recognition and tracking. We propose a novel approach to clustering object pixels into separate objects using density and spatial cues. The suggested method runs in linear time, accounts for image noise and yields real-time performance.