Multiple kernel Gaussian process classification for generic 3D object recognition
Erik Rodner, Doaa Hegazy, Joachim Denzler · 2010
We present an approach to generic object recognition with range information obtained using a Time-of-Flight camera and colour images from a visual sensor. Multiple sensor information is fused with Bayesian kernel combination using Gaussian processes (GP) and hyper-parameter optimisation. We study the suitability of approximate GP classification methods for such tasks and present and evaluate different image kernel functions for range and colour images. Experiments show that our approach significantly outperforms previous work on a challenging dataset which boosts the recognition rate from 78% to 88%.