Optical flow

A large number of contributions to this workshop is concerned with computing or making use of optical flow . This i s the term now commonly used for an intermediate representation of time-varying imagery where each pixel is assigned a velocity vector describing its temporal displacement in th e image plane or—for human vision-in the retinal field . Optical flow can be consciously experienced by human observers (e .g . when travelling in a car) and has early been recognized a s a valuable source of information pertaining to the motion an d 3D characteristics of a scene (GIBSON 50) . Thorough quantitative analyses, however, have only become available during the last five years, when an increasing number of vision researchers turned to motion problems . As can be seen fro m this workshop, interesting results on how to exploit optica l flow are still being uncovered . Before making use of optical flow it must be computed — unfortunately . As it turns out, no computational theory has yet been offered which promises satisfactory results for unrestricted real-world images . Nevertheless, considerable progres s has been made in certain restricted situations . This is als o documented by several contributions to this workshop . hi thi s introductory survey I shall try to point out the major differences in the approaches taken so far. Fig . 1 gives a rough sketch of the representations and th e processing connected with optical flow . Much of the variety o f the research contributions is due to certain assumptions abou t the visual world . These will be discussed in the following sec tion . The visual world is projected yielding intensity arrays from which optical flow computation per se proceeds . Thre e rather distinct directions of processing have been proposed . A s a first possibility, optical flow is directly computed from th e intensity array . The result is usually a dense flow field . Alternately, descriptive elements like prominent points or edges may be computed first . Points usually give rise to a sparse flo w field after correspondence is established . Edges lead to a quit e different flow compentation clue to the remaining degree of freedom . In section 3 these distinctions are elaborated in som e more detail . Finally, I shall briefly review ways of extractin g useful information from optical flow .