|Ph.D Student||Rozenfeld Stas|
|Subject||Improving Model Estimation via Statistical Tools in|
|Department||Department of Industrial Engineering and Management||Supervisors||Professor Ilan Shimshoni|
|Professor Michael Lindenbaum|
|Full Thesis text|
Noise and outliers in the input to computer vision applications tend to damage the results and can even render them useless. In our work we suggest an approach to reduce the severity of these phenomena. The improvement is achieved by applying appropriate statistical tools. Two well known general computer vision tools are considered for testing the approach, robust regression and epipolar geometry estimation. A relatively new specific task of the three dimensional mirroring surface recovery is considered for testing purpose as well. The approach includes the following steps: 1) In each considered problem the results are obtained through optimization; 2) the cost function of the optimization problem is modified into a likelihood function form, causing it to reflect correctly the statistical properties of the data corruption; 3) the corresponding (maximum likelihood) problem is then solved. An additional improvement is achieved in the three dimensional mirroring surface recovery process by utilizing robust regression and a statistically valid heteroscedastic approach. In this process a dense depth map is built based only on a sparse set of initial points and using one dimensional homographies. For all the considered problems the performance improvement is verified using experiments on real data. In the experiments the three dimensional dense shape of real mirroring objects was recovered, the epipolar geometry was estimated from noisy data and in the presence of outliers.