Learning OpenCV

Directions

Although OpenCV does not have an absolute focus on real-time algorithms, it will continue to favor real-time techniques. No one can state future plans with certainty, but the following high-priority areas are likely to be addressed.

Applications

There are more "consumers" for full working applications than there are for low-level functionality. For example, more people will make use of a fully automatic stereo solution than a better subpixel corner detector. There will be several more full applications, such as extensible single-to-many camera calibration and rectification as well as 3D depth display GUI.

3D

As already mentioned, you can expect to see better support for 3D depth sensors and combinations of 2D cameras with 3D measurement devices. Also expect better stereo algorithms. Support for structured light is also likely.

Dense Optical Flow

Because we want to know how whole objects move (and partially to support 3D), OpenCV is long overdue for an efficient implementation of Black's [Black96] dense optical flow techniques.

Features

In support of better object recognition, you can expect a full-function tool kit that will have a framework for interchangeable interest-point detection and interchangeable keys for interest-point identification. This will include popular features such as SURF, HoG, Shape Context, MSER, Geometric Blur, PHOG, PHOW, and others. Support for 2D and 3D features is planned.

Infrastructure

This includes things like a wrapper class,^[269] a good Python interface, ...