00100	Some other names for Pajaro Dunes
00200	
00300	Harry Andrews--USC
00400	Wm Pratt--USC
00500	Bob McGhee--Ohio State
00600	Sam Dwyer--Univ of Missouri
00700	Charles Harlow--Univ of Missouri, Columbia, Mo
00800	Prof. Riseman--Univ of Mass, Amherst
00900	Bruce McCormick--Univ of Illinois,Chicago
01000	Jack Sklansky--UC, Irvine
01100	
     

00100	make films
00200		disable timeout on III controller and work lightly loaded
00300		other ways
00400			computer controlled single frame
00500			to run dpy n times
00600				FOO,,74;RESTORE FLAGS
00700				<BYTE(8)0,0,200>+12;TEST CONTROL BIT
00800				-1,,20;SHUTS OFF WORLD
00900				<BYTE (8)0,40,0>+12;ZERO EDGE OVERFLOW AND DON'T SKIP
01000				<BYTE(11)DX,0>+46;MOVE RIGHT DX
01100				<BYTE(8)0,0,40>+12;TEST EDGE OVFL, SKIP IF NOT
01200				<BYTE(8)200>+12;IF NO SKIP,TURN ON CONTROL BIT
01300				0
01400				FOO,,64	;SAVE FLAGS
01500	
01600	
01700	film prints
01800		slight advantage at 4 copies for internegative and optical track
01900		.081$/ft 1-9 copies from internegative
02000		.34$/ft for internegative and optical track
02100		vs .18$/ft electroprint from A/B rolls and consumes original slightly
02200		
02300			DX=1024/N
02400				INIT FOO TO 0
02500	
02600	
02700	protection usable with alias
02800		RENAME ←FOO.BAR/P=205
02900	
03000	operate at MIT
03100		:? gets list of commands
03200		:?? gets longer list of commands
03300		:LISTF GJS;	FILE DIRECTORY
03400		:PRINT		TYPES OUT FILE
03500	
03600	SAIL DEBUG
03700		RU SAILDD[S,AIL]
03800		*←FLOWER/1M
03900	
04000		L3600/7
04100		2M#P
04200		1M#P
04300		3M#P
04400	
     

00100	LOU,RCB,WAP,BGB,MJH,PDQ,KKP
00200	BGB DEMOS
00300	
00400	RUN 0[1,BGB]	! arm motion display;
00500		n	! any number slows down action;
00600	
00700	R G			! geomed;
00800		ctrl ∞		! make torus;
00900		alt-mode	! hidden line;
01000		H		! help;
01100	
01200	
01300	R CRE
01400		ctrl I	HIP[CRE,BGB]	! input, file name for horse;
01500			DOL[CRE,BGB]
01600		meta ↔		! play film of images;
01700		H		! histograms;
01800		Q		! contours at 20,40,60;
01900	HELP CRE		! at monitor level;
02000	
02100	
02200	AL GEM,BGB
02300	
02400	
02500	RU G
02600		I	HIP0		! horse;
02700		ctrl I	TMP		! camera model for horse;
02800		meta Z	HEAD
02900		meta Z	BASE
03000	
03100	
03200	R G
03300		I	DODECA[B3D,BGB]		! dodecahedron;
03400		I	STAR[B3D,BGB]
03500		ctrl¬		! subtraction of bodies;
03600		ctrl ∪		! union of bodies;
03700		ctrl ∩		! intersection of bodies;
03800		K 		! kill;
03900		C		! copy;
04000	
     

00100	Categorize by goals (how many, how complex)
00200	  by objects(how many, how complex)
00300	  by knowledge available, required
00400	
00500	We discuss these issues in the context of some paradigms:
00600	PARADIGM 1:  We know what is in the visual environment, where the
00700	objects are, and remember colors and details of all objects.  Moreover,
00800	we know that nothing else is in the environment.
00900	PARADIGM 2:  We know some of the objects, and some loose spatial
01000	relations among them.  We do not know what else is in the environment.
01100	PARADIGM 3:  We are in a "known" environment, such as an ordinary office,
01200	or outside, with a complex set of goals, complex environment and
01300	rich universe of objects.
01400	PARADIGM 4: We are shown a photograph without knowing the context.
01500	
01600	In the first paradigm, if the goals are few and objects few,
01700	some relatively simple techniques may be used to recognize objects
01800	[Tenenbaum, Freuder].  A tlephone may be the only black object.  We
01900	note that Tenenbaum's work uses no shape information, only pointwise
02000	properties such as color, and depth from a ranging device.
02100	
02200	HOW WILL THESE GENERALIZE
02300	
02400	FUNDAMENTAL VISION PROBLEMS
02500	segmentation-body,part
02600	obscuration-indexing
02700	representation
02800	computational complexity
02900	linking-indexing
03000	description vs recognition
03100	use of context
03200	learning
03300	low level,bottom up  vs high level,top down
03400	
03500	How does each appear
03600	What are the options
     

00100			VISUAL PERCEPTION AND AUTOMATION
00200	
00300	
00400	Minsky and Papert argue that vision is a prime problem area for
00500	the study of intelligence.  They see the main problems as the description
00600	of data and procedures to represent a variety of kinds of knowledge.  This
00700	knowledge we now conceive of in terms of microtheories, well worked out
00800	systems of knowledge in small domains such as topology, geometry, chemistry,
00900	biology, psychology and human relations.  We come to see the problem as
01000	implementing these microtheories as the semantics of particular domains.
01100	The most important microtheories of vision are related to representations.
01200	Two representations help us with problems like:
01300		how do we describe a complex object
01400		how do we relate a scene just after moving our head to that
01500			just before.
01600	These are the representations of shape and space.  As problem areas,
01700	the microtheories of vision are relatively well-defined, since they
01800	are derived from models of topology and geometry, and properties of the
01900	physical world.  Some of the complication of social and psychological models
02000	does not widely enter in vision.  
02100	
02200	Some areas which have been attacked have been:
02300		theoretical understanding of line-drawings  Huffman,Clowes,Waltz
02400		structural learning	Winston
02500		use of world model for outdoor scenes	Yakimovsky
02600		representation of complex objects	Binford,Agin,Nevatia
02700		computational geometry-computational complexity
02800	There is a great need and opportunity for work in perceptual strategies
02900	which has the advantage of a well-defined problem domain for which we
03000	have great intuition, so that we can use semantics to do otherwise very
03100	hard problems.  There is also a need for good systems work.
03200	
03300	This is an applications area with promise.  We have a serious effort in
03400	programmable automation.  The application area is a source of good problems
03500	in AI at this stage.  We hope that more support for AI will come from the
03600	realization of the impact of these techniques on problems outside the
03700	university.
     

00100	using our representation of complex objects, describe a few objects for
00200		example, pump base
00300	
00400	extend the ideas of Huffman and Clowes with dual graphs (see Mackworth))
00500	
00600	fourier transform edge verifier
00700	
00800	Texture analysis:
00900	
01000		2-d shape description by projection
01100		cluster descriptors
01200	
01300	improve convergence of calibration model by choosing improved non-singular
01400		parameters
01500	
01600	stereo correlation, analysis and programming
01700	
01800	edge finder for curves
01900	
02000	use visual feedback to put a screw into a hole.
     

00100	
00200	
00300	
00400	
00500	
00600	
00700	
00800	
00900	
01000	
01100	
01200	
01300	
01400	
01500	
01600	
01700	
01800					VISION SEMINAR
01900	
02000	
02100	
02200	
02300	
02400	
02500	
02600					    TOPIC
02700		 	   ANALYSIS OF LINE DRAWINGS AND SURFACES
02800			   A Lecture Series by Prof David Huffman
02900	
03000	
03100	
03200	
03300	
03400	
03500	
03600	
03700	
03800	
03900	
04000	
04100					FIRST MEETING
04200				    3PM, 14 JANUARY,1974
04300				    Serra House Conf Room
04400	
04500	
04600			    Realizable and Unrealizable Objects
04700				   Prof David Huffman
     

00100	Informal followup discussions later in week
00200	
00300	Lectures as planned
00400	2.  Dual picture and some of its applications
00500	3.  Realizability of Pictures of General Polyhedra
00600	4.  Change of Vantage Point and 
00700		Determination of Illumination and Shade
00800	5.  Non-Planar surface; Intersections and Shadows
00900	6.  Zero Curvature Surfaces
01000	7.  Paper Folding