  We propose a hybrid method for stereo disparity estimation by combining block
and region-based stereo matching approaches. It generates dense depth maps from
disparity measurements of only 18 % image pixels (left or right). The
methodology involves segmenting pixel lightness values using fast K-Means
implementation, refining segment boundaries using morphological filtering and
connected components analysis; then determining boundaries' disparities using
sum of absolute differences (SAD) cost function. Complete disparity maps are
reconstructed from boundaries' disparities. We consider an application of our
method for depth-based selective blurring of non-interest regions of stereo
images, using Gaussian blur to de-focus users' non-interest regions.
Experiments on Middlebury dataset demonstrate that our method outperforms
traditional disparity estimation approaches using SAD and normalized cross
correlation by up to 33.6 % and some recent methods by up to 6.1 %. Further,
our method is highly parallelizable using CPU and GPU framework based on Java
Thread Pool and APARAPI with speed-up of 5.8 for 250 stereo video frames (4,096
x 2,304).
