Accessible Computer Vision

Computer vision has a new and important role on the world technological stage with the advent of cheap cameras and high-performance low-power processors. Examples of its application are available throughout industry from simple face detection on compact cameras to advanced articulated modelling such as that on the Microsoft Kinect. However, implementation of a ÒsimpleÓ face detection system requires advanced knowledge of existing algorithms and their parameters, which is beyond the scope of general developers. Our approach is to develop an abstraction of computer vision, much like OpenGL provides an abstraction of computer graphics, allowing appropriately trained users to develop complex solutions without the need to specify particular algorithms or parameters.

The theme of our research within computer vision is access: we would like computer vision to be open and accessible beyond the confines of academia and computer vision experts. While many algorithms are freely and openly available, we do not consider these to be accessible, due to the knowledge and expertise required to effectively apply these to real-world problems. The direction of research we are pursuing is to create a basis from which access to computer vision techniques can be provided without requiring specialist knowledge. This requires an abstraction layer above the sophisticated techniques in vision: our key concept is an abstraction layer through which a description of the problem may be provided rather than requiring the selection of a particular algorithm i.e. we let developers describe what the problem is and have our system interpret the description to work out how to solve it, an approach we term problem-centric.