Robotic sensory data fusion: a regularized color edge detection approach

Data fusion provides tools for solving problems which are characterized by distributed and diverse information sources. In this paper we focus on the problem of extracting features such as image discontinuities from both synthetic and real color images. Since edge detection is an ill-posed problem in the sense of Hadamard, Tikhonov's regularization paradigm is proposed as a tool for solving this inversion problem and restoring well-posedness. The proposed framework includes (1) a systematic view of one-dimensional as well as two-dimensional regularization, (2) extension of the standard Tikhonov regularization method by allowing space-variant regularization parameters, and (3) further extension of the regularization paradigm by adding multiple data sources to allow for data fusion. The theoretical approach is complemented by developing a series of algorithms and then solving the early vision problem of color edge detection. An evaluation of this method shows this new analytical data fusion technique output is consistently better than each of the individual RGB edge maps.