perm filename DOC[DOC,TOB]3 blob
sn#129338 filedate 1974-11-01 generic text, type T, neo UTF8
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.
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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
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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