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Classes

Spring 2009 - Electronic Technologies for Art

Electronic Technologies for Art

Winter 2010 - Electronic Technologies for Art

Electronic Technologis for Art I


VIS 147A # ELECTRONIC TECHNOLOGIES FOR ART I 4 units;

This course will develop artworks and installations that utilize digital electronics. Techniques in digital electronic construction and computer interfacing will be learned that will allow for interactive control of sound, lighting and electromechanics will be learned. Students will construct devices which can responsively adapt artworks to conditions involving viewer participation, space activation and machine intelligence.

Spring 2010 - Time and Process Based Digital Media

Course page Time and Process Based Digital Media

Frameworks

Advanced Video Processing

Language Processing for Art

Workshop

Language Processing and Self-Representation

Reference

Proposals

EMPAC

DAC 2009

mediated cultures / digital ethnography

MediatedCultures @ Kansas State

http://mediatedcultures.net/mediatedculture.htm

Networked Individualism

http://en.wikipedia.org/wiki/Barry_Wellman

social dimensions

NLP Language Research

Closed Captioning

Teleprompter

Language for performance Teleprompter

Wordnet similarity

wordnet::similarity word vectors

  • coffee#n#1: nutmeg, preparation, coffee_tree, caffeine, pulverized, coffee, arabia, tea, shelf_life, packed, hot_water, drinking, topped, java, lemon_peel, ordered, cognac, irish_whiskey, beverage, perforated, cup, sweetened, infusion, espresso, cream, boiling, dehydrated, finely, whipped_cream, stimulating, bitter, cinnamon, alkaloid
  • cup#n#1: loaded, drunk, disposable, mustache, coffee, boxlike, tea, saucer, footed, drinking, eucharistic, collectively, tableware, bowl, cup, standardized, greece, rim, toast, missing, ancient_greek, drinking_vessel
  • rifle#n#1: automatic_rifle, loads, loading, rifle, action_mechanism, bore, barrel, butt_end, breech, cartridge, fired, sliding, firearm, shotgun, lever, armored, shoulder_holster, portable, lifted, semiautomatic, forward_motion, rifled
  • work#n#1: dry_rot, busywork, willing, machine_tool, technology, practical, barn, shiny, undertaking, productive, wages, inquiring, waiting, unit_of_time, preliminary, cleansing, substitute, incomplete, obliged, operations, a_great_deal, rubbing, washed, separately, ophelia, outstanding, interesting, waxing, stocks, attending, papers, labor, thoroughly, heavy_lifting, wasnt, municipal, succeed, missionary, hoped, further, mechanical, medical_care, barber, assigned, meager, budget, course_of_study, attempted, disadvantaged, boss, routine, damaging, close_to, done_for, grade, directed, systematically, recreational, duties, checked, shining, shoes, piece_of_work, polishing, sunday, soap, gawkers, leave_of_absence, improve, no_longer, cleaning, housewife, handling
  • neck: sternum, collarbone, hanging, immunity, human_being, cartilaginous, externally, cervix, aids, elderly, body_part, glandular, rings, flex, mastoid, clavicle, admired, occipital_bone, arteries, larynx, membranous, inhaled, aorta, fold, obliquely, artery, ductless, spine, on_fire, chin, graceful
  • rifle#n#1 <-> neck#n#1 = 0.0728235790450328

Generative Video through NLP

  • making edits based on semantic content
    • sequence
    • cut points?
    • decisions based on character names?
  • aesthetic strategies for text
    • side-scrolling text
    • intertitles
    • subtitles
    • titles
  • aesthetic strategies for photos
    • Ken Burns effect
    • simulated motion
  • aesthetic strategies for video
    • specifically... diverse collections of heterogenous clips and documentation
    • video collection strategies

similarity measures

Calculating the similarity of two phrases, with the goal of finding more related matches. Example: "ocean at night" to "still life, objects around the studio" versus "cat tails at the ocean"

NLP

Optical Character Recognition

  • tesseract-ocr [1]
  • ocropus [2] layout analysis, tesseract is a plugin

word evolution study at University of Reading

language associations

Closed Captioning

Electronic Literature as Performance

LCD hacks

DIY Projector from LCD panel

Fabrication

Fabrication

fabrication and rapid prototyping

Video Tools

High Resolution Video

  • 24P workflow with open source tools [3]
  • HDMI capture card (BlackMagic Intensity) [4]
  • PCI-Mini and BlackMagic card solution on MBPro [5]

Sub-titles and Closed- Captioning

subtitle manipulation

Convert Fraps Video to Youtube

  • ffmpeg -i infile.avi -vcodec mpeg4 -b 8000k outfile.mp4
  • drop audio ffmpeg -i infile.avi -vcodec mpeg4 -an -b 8000k outfile.mp4
  • resize to youtube preferred HD ffmpeg -i infile.avi -vcodec mpeg4 -s 1280x720 -b 8000k outfile.mp4

Dump video to frames

  • ffmpeg -i infile.avi -s 720x480 -f image2 infile-%03d.tga

Misc

ffmpeg to make a dvd

DV cam as a webcam

Monitor Mount

Modifying VideoInput lib for OpenFrameworks

DirectShow details: 

// Setup the other filters necessary to handle MPEG2TS Stream
// Capture Device -> MPEG-2 Demultiplexer -> ffdshow Video Decoder -> Frame Grabber

// MPEG-2 Demultiplexer Filter
// Major Type: Video
// Sub Type: MPEG2_VIDEO
// Format: MPEG2Video
// New Pin
//   Name: Video
//   Media Type: MPEG2 Program Video
// PID Mapping:
//   PID: x0810
//   Pin: Video
//   Elementary Stream (A/V only)

// ffdshow Video Decoder Filter
// Codecs
//   Format: MPEG2
//   Decoder: libmpeg2
//   Supported FOURCCs/remarks: MPEG2 codec
// In: 
//   Major Type: Video
//   Sub Type: MPEG2_VIDEO
//   Format: MPEG2Video
// Out:
//   Major Type: Video
//   Sub Type: YV12
//   Format: YV12 1536x-1080, 12bits,
//   Aspect Ratio: 16x9,
//   Interlace format: Frames
//   rcSrc=[0,0,1440,1080]
//   rcDst=[0,0,1440,1080]

CV

Motion Estimation

Identity Processing

Face detection

Face recognition

PCA
Elastic Bunch Graph Matching (EBGM)
  1. Integer to float conversion - Converts 256 gray levels into floating point equivalents.
  2. Geometric normalization – Lines up human chosen eye coordinates.
  3. Masking – Crops the image using an elliptical mask and image borders such that only the face from forehead to chin and cheek to cheek is visible.
  4. Histogram equalization – Equalizes the histogram of the unmasked part of the image.
  5. Pixel normalization – scales the pixel values to have a mean of zero and a standard deviation of one.
  • default face normalization parameters from CSU FacePerf program:
#define ELLIPSE_X    65.0
#define ELLIPSE_Y    50.0
#define ELLIPSE_A    64.0
#define ELLIPSE_B    118.0

#define EDGE_PRE     0
#define EDGE_POST    0

#define EYE_LX       30.0
#define EYE_LY       45.0
#define EYE_RX       100.0
#define EYE_RY       45.0

#define SIZE_WIDTH   130
#define SIZE_HEIGHT  150

#define NOISE        0.0
#define SCALE        1.0
#define SHIFT_X      0.0
#define SHIFT_Y      0.0

#define HIST_NONE    0
#define HIST_PRE     1
#define HIST_POST    2

Biological Limits

Structure from Motion / Motion Correction

PTAM

Projects

  • face swap. wearing faces. you as me. me as you. us as them. you as us

Book Formatting

Auto-generating Books

  • render txt files to whole-page images

Windows hacking

slipstream windows install

live CDs

XP from USB stick

Corn Fed Nation

corn fed nation

E-Ink Facemask

Performance

Semantic Web

Misc

visualization

decision making algorithms

MedTech

Transcranial Magnetic Stimulation (TMS)

Electronic Muscle Stimulation (EMS)

MedTech

Map-Reduce

Massive Data Storage

AJAX versus traditional web design

Enabling OBEX with motorola Razr V3c

Online Culture

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