Integration of multiresolution image segmentation and neural networks for object depth recovery

A novel technique for three-dimensional depth recovery based on two coaxial defocused images of an object with added pattern illumination is presented. The approach integrates object segmentation with depth estimation. Firstly segmentation is performed by a multiresolution based approach to isolate object regions from the background given the presence of blur and pattern illumination. The segmentation has three sub-procedures: image pyramid formation; linkage adaptation; and unsupervised clustering. These maximise the object recognition capability while ensuring accurate position information. For depth estimation, lower resolution information with a strong correlation to depth is fed into a three-layered neural network as input feature vectors and processed using a Back-Propagation algorithm. The resulting depth model of object recovery is then used with higher resolution data to obtain high accuracy depth measurements. Experimental results are presented that show low error rates and the robustness of the model with respect to pattern variation and inaccuracy in optical settings.