Michael Tusch, Founder and CEO of Apical Imaging, presents the "Better Image Understanding Through Better Sensor Understanding" tutorial within the "Front-End Image Processing for Vision Applications" technical session at the October 2013 Embedded Vision Summit East.
One of the main barriers to widespread use of embedded vision is its reliability. For example, systems which detect people some of the time, or which produce frequent false detections, are of limited use. Why is it that algorithms which work well in the lab don't work so well in real-world conditions? Cameras perform a great deal of image processing in order to make video look natural and realistic. Often this leads to unpredictable variations in the input data to embedded vision algorithms. Tusch shows that an understanding of the specific image sensor characteristics coupled with information gleaned by image processing methods, has a very significant impact on accuracy of modern embedded vision algorithms in difficult visual environments.
