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Pen

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Mobile and Ubiquitous Computing (Mynatt & Starner) Pattern Recognition (Bobick) ... Tappable, hunt-and-peck. QWERTY vs. alphabetical. Can you think of other options? ... – PowerPoint PPT presentation

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Title: Pen


1
Pen Gesture Input
2
Agenda
  • Questions?
  • Poster session 2 moved
  • Pen input
  • free-form
  • soft keyboards
  • recognition
  • Gesture recognition

3
Before we begin
  • If this is interesting, you might explore Spring
    2000 courses
  • UI Software Design (MacIntyre)
  • Mobile and Ubiquitous Computing (Mynatt
    Starner)
  • Pattern Recognition (Bobick)
  • Computer Vision (Starner, Essa)
  • Computational Perception (Starner, Essa)

4
Pen input
  • Free-form ink
  • Soft keyboards
  • Recognition systems
  • generalize to gesture-based systems

5
Free-form ink
  • ink as data
  • humans can interpret
  • time-stamping
  • implicit object detection
  • special-purpose domain objects

6
Free-form ink examples
  • Ink-Audio integration
  • Tivoli (Xerox PARC)
  • Classroom 2000 (GT)
  • Dynomite (FX-PAL)
  • FiloChat (HP Labs)
  • The Audio Notebook (MIT)
  • FlatLand (Xerox PARC)

7
Soft Keyboards
  • common on small mobile devices
  • many varieties
  • tapping interfaces
  • simple gesture recognizers

8
Tappable, hunt-and-peck
  • QWERTY vs. alphabetical
  • Can you think of other options?

9
T9 (Tegic Communications)
  • Alternative tapping interface
  • phone layout plus dictionary
  • mobile phone interface

10
Quickwrite (Perlin)
  • Unistroke recognizer

11
Cirrin (Mankoff)
  • Word-level unistroke recognizer

12
Recognizing pen input
  • Graffiti
  • unistroke alphabet
  • Other pen gesture recognizers
  • for commands
  • measure features of strokes
  • usually no good for complex strokes

13
Handwriting recognition
  • Lots of resources
  • see Web
  • good commercial systems
  • Two major techniques
  • on-line
  • off-line

14
Mixing modes of pen use
  • Users want free-form and commands
  • or commands vs. text
  • How to switch between them?
  • (1 mode) recognize which applies
  • (2 modes) visible mode switch
  • (1.5 modes) special pen action switches

15
Correcting recognition errors
  • Really slows effective input
  • word-prediction can prevent errors
  • Various strategies
  • repetition (erase and write again)
  • n-best list
  • other multiple alternative displays

16
Other interesting applications
  • Signature verification
  • Note-taking
  • group (NotePals by Landay _at_ Berkeley)
  • student (StuPad by Truong _at_ GT)
  • meetings (Tivoli and other commercial)
  • Sketching systems
  • early storyboard support (SILK, Cocktail Napkin)

17
Gesture Recognition
  • Emerging area
  • Perceptual User Interfaces
  • mainly computer vision researchers

18
Advantages
  • Using Wizard of Oz techniques, we find
  • Consistent gestures for certain commands
  • Easy to learn
  • Remembered over time
  • DFAB interaction framework

19
Issues with Gesture Recognition
  • Does a gesture clearly indicate
  • functional informatin
  • scope
  • target information
  • Variability

20
Non-obvious gestures
  • Biometric signals
  • smart floor
  • affective computing

21
Videos
  • FlatLand (Mynatt PARC)
  • MERL (Freeman)
  • Digital Desk (Wellner)
  • StuPad (Truong)
  • American Sign Language (Starner)
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