Computer stereo vision
Computer stereo vision is the extraction of 3D information from digital images, such as those obtained by a CCD camera.
The values in this disparity map are inversely proportional to the scene depth at the corresponding pixel location.
3D stereo displays find many applications in entertainment, information transfer and automated systems.
Stereo vision is highly important in fields such as robotics to extract information about the relative position of 3D objects in the vicinity of autonomous systems.
Other applications for robotics include object recognition,[5] where depth information allows for the system to separate occluding image components, such as one chair in front of another, which the robot may otherwise not be able to distinguish as a separate object by any other criteria.
Scientific applications for digital stereo vision include the extraction of information from aerial surveys, for calculation of contour maps or even geometry extraction for 3D building mapping, photogrammetric satellite mapping, or calculation of 3D heliographical information such as obtained by the NASA STEREO project.
In the adjacent diagram light from the point A is transmitted through the entry points of pinhole cameras at B and D, onto image screens at E and H. In the attached diagram the distance between the centers of the two camera lens is BD = BC + CD.
The method described above for evaluating smoothness is based on information theory, and an assumption that the influence of the color of a voxel influences the color of nearby voxels according to the normal distribution on the distance between points.
An image comprising random dots would have no smoothness, and inferences about neighboring points would be useless.
[citation needed] The normal distribution is Probability is related to information content described by message length L, so, For the purposes of comparing stereoscopic images, only the relative message length matters.
Based on this, the information measure I, called the Sum of Squares of Differences (SSD) is, where, Because of the cost in processing time of squaring numbers in SSD, many implementations use Sum of Absolute Difference (SAD) as the basis for computing the information measure.
Firstly the information needed to express one image in terms of the other is derived.
For example, if an object in front occludes an area of sky behind, the measure of smoothness favors the blue pixels all being grouped together at the same depth.
The total measure of smoothness uses the distance between voxels as an estimate of the expected standard deviation of the color difference, The total information content is then the sum, The z component of each pixel must be chosen to give the minimum value for the information content.
The minimum total information measure is, The depth functions for the left and right images are the pair, The minimization problem is NP-complete.
However methods exist for computers based on heuristics that approximate the result in a reasonable amount of time.
[7] Efficient implementation of stereoscopic vision is an area of active research.
