Computer vision seeks to develop algorithms that replicate one of the most amazing capabilities ofthe human brain – inferring properties of the external world purely by means of the light reflectedfrom various objects to the eyes. We can determine how far away these objects are, how they areoriented with respect to us, and in relationship to various other objects. We reliably guess theircolors and textures, and we can recognize them - this is a chair, this is my dog Fido, this is a pictureof Bill Clinton smiling. We can segment out regions of space corresponding to particular objectsand track them over time, such as a basketball player weaving through the court.
In this course, we will study the concepts and algorithms behind some of the remarkable suc-cesses of computer vision – capabilities such as face detection, handwritten digit recognition, re-constructing three-dimensional models of cities, automated monitoring of activities, segmentingout organs or tissues in biological images, and sensing for control of robots. We will build thisup from fundamentals – an understanding of the geometry and radiometry of image formation,core image processing operations, as well as tools from statistical machine learning. On completing this course, a student would understand the key ideas behind the leading techniques for the mainproblems of computer vision - reconstruction, recognition and segmentation – and have a sense ofwhat computers today can or can not do.
Prof. Trevor Darrell
http://www.eecs.berkeley.edu/~trevor
Prof. Alyosha Efros
http://www.eecs.berkeley.edu/~efros
Location: 306 SODA Hall
Time: Tuesdays & Thursdays, 12:30 pm - 2:00 pm