🚀 go-pugleaf

------------------------------------------------------------------------------

	       Course Announcement - January 1993 - Please Post
				       
			     Graduate Seminar on
				       

			 NEURAL NETWORKS AND VISION:

      Nonlocal Cortical Interactions, Filling-in, and Object Perception
				       

			   Prof. Jonathan Marshall
	       Department of Computer Science, UNC-Chapel Hill
				       
     COMP 290 (section 050), 3.0 credits, Registration call number: 21617
		[Note: this course will be offered even though
	       it does not appear in the directory of classes.]
				       
	       TuTh 9:30-10:45, Spring 1993; starts January 12
			   Sitterson Hall, room 325

This graduate research seminar will focus on modeling and understanding
nonlocal interactions in visual cortex and in visual perception.  Nonlocal
interactions include such phenomena as:

    o "filling-in" or diffusion of color, motion, depth, and surface
      information across wide regions of an image;
    o visual transparency effects, where more than one surface can be seen
          within an image region;
    o "amodal" representations of temporarily occluded visual objects;
    o grouping operations (establishing linkages among perceptually
      related data);
    o scission operations (breaking linkages between unrelated data);
    o steering operations, whereby visible portions of a visual group or
      object can control the representations of invisible or occluded
      portions of the same group.
    o development of long-range connections across visual cortex, whereby
      data about one part of an image can become able to influence the
      processing or perception of data about some distant parts;
    o development of specific dissociation of neural connections, whereby
      some neurons representing nearby parts of an image can become unable
      to influence one another strongly, despite their proximity.

Introductory information will also be provided on neural networks and on
vision.  The seminar will cover selected papers from the literature on neural
networks and vision.  Introductory topics include:

    o basic architecture of animal visual systems;
    o basic introduction to neural networks;
    o how to read papers in neurophysiology, psychophysics, and neural
      networks;
    o computational aspects of vision;
    o computational behavior of neural networks;
    o edge detection, orientation detection;
    o perception of visual motion, depth, transparency, and occlusion;
    o visual segmentation, boundaries, and grouping;
    o learning, adaptation, and self-organization.

The field of neural network modeling is concerned with both how neurons in
animal brains communicate and perform tasks for perception, cognition, and
motor control, and how networks of artificial neuron-like processing elements
can solve computational problems.  Neural network research is thus highly
interdisciplinary: computational theory helps explain and predict findings in
neurobiology and psychophysics, while neurobiological and psychological
results suggest new methods for solving computational problems.

The course workload will consist of readings, class presentation(s), class
participation, and a research project or paper.  Prior exposure to material on
neural networks or vision is not a prerequisite, but would likely be helpful.
Graduate students in other departments, as well as those in computer science,
are encouraged to take the course.  This course is open to students who have
not studied neural networks or vision, and it is also open to students who
previously took my Neural Networks and Vision course (Spring 1991), since the
material covered will be about 75% new.

For more information, contact Prof. Jonathan Marshall at (919) 962-1887
(marshall@cs.unc.edu).

------------------------------------------------------------------------------