Stereovision algorithms

For my project, supposed to segment closest hand region from camera, 开发者_如何学运维I initially try openCV's stereovision example. However, disparity map looks very bad and its useless for me. Is there any other method which is better than openCV implementation and have some output(image-video). Because, my time is limited, I must choose one better algorithm and implement this.

Thank you.

OpenCV implements a number of stereo block matching algorithms some of them pretty cutting edge.

Disparity maps always look bad except in very simple circumstances - the first step is to try and improve the source images, the lighting and the background. I

If it was easy then everybody would eb doing it and there would be no market for expensive 3D laser scanners.

Try the different block matching algorithms provided by OpenCV. The little bit of experimentation I've done so far seems to indicate that cv::StereoSGBM gives better disparity maps than cv::StereoBM, but is slower.

The performance of the block matching algorithms will depend on what parameters they are initialized with. Have a look at the stereo examples again here, notice line 195-222 where the algorithms are initialized.

I also suggest you use some basic GUI (OpenCV:s highgui for example) to manipulate these parameters real-time when finetuning the algorithm.