Generally, the key techniques of a computer vision system is composed of five parts, which are video capture, camera calibration, image pre-processing and feature extraction, stereo correspondence and 3D reconstruction.
The components of the micro stereo vision system are introduced. The process of 3D reconstruction is divided into such segments as stereo microscope, image-obtaining, micrograph processing, features extraction , stereo-matching, camera-calibration, depth recovering, surface interpolating etc.
By using simulated annealing (SA) algorithm and a lot of simulation experiments, the energy function can escape local minimum, then stereo correspondence can be realized.
We have also analyzed the process of SEM image formation, and present constraints that are useful in solving the stereo correspondence problem.
Because only one small spot on the object is illuminated at a time, the stereo correspondence problem is solved in real time.
This is used, in addition to 3D recovery via stereo correspondence, to maintain the robot motion in a purely translational path, as well as to remove the effects of any drifts from this path from each acquired image.
This scheme eliminates the window size selection problem in existing area-based stereo correspondence schemes.
This paper presents a new multi-pass hierarchical stereo-matching approach for generation of digital terrain models (DTMs) from two overlapping aerial images.
At the coarsest level of the first pass, a global stereo-matching technique, the intra-/inter-scanline matching method, is used to generate a good initial DTM for the subsequent stereo matching.
Stereo-matching is one of the most active research topics in computer vision.
We present a new approach to the stereo-matching problem.
First, the stereo-matching problem is defined in terms of finding a disparity map that satisfies two competing constraints: (1) matched points should have similar image intensity, and (2) the disparity map should vary as slowly as possible.