Transcript 2D, 3D and Multi-Touch Gestures Made Easier

2D, 3D AND MULTI-TOUCH
GESTURES MADE EASIER
Joshua Sunshine
What is a gesture?
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“Gesture-based interfaces offer an alternative to
traditional keyboard, menu, and direct manipulation
interfaces.” [Rubine]
“Pen, finger, and wand gestures are increasingly
relevant to many new user interfaces.” [Wobbrock]
“Input paths of recognized shapes.” [Me]
What can gesture?
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Mouse: Adobe Illustrator, SKETCH
Pen: Palm Pilot, Interactive Whiteboards
Finger: IPhone
Body: Minority Report
Face: ATM PINs
Wii-mote: Wii games
Why support gestures?
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Efficient
A
single stroke can indicate:
 The
operation
 The operand
 Additional parameters
A
proofreader’s mark indicates [Rubine] :
 that
a move should occur (operation)
 the text that should be moved (the operand)
 and the new location of the text (an additional param)
Why support gestures?
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Natural
 Chinese
brush painting
 Musical Score
 Chemical Formula
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No other choice
 IPhone
(touch screens in general)
 Table Top
 Interactive Whiteboard
Videos
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World Builder
Opera face recognition
Gesture support, two approaches
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Ad hoc = “Recognizers that use heuristics
specifically tuned to a predefined set of gestures.”
[Wobbrock 2007]
 Application
Specific: e.g. Chinese Brush Painting,
Musical scores, Chemical Formulas
 Platform: e.g. IPhone gesture libraries
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Systematic = Allow for definition of new gestures.
 Toolkit
or framework
 Simple algorithm
Ad hoc vs. systematic
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Ad hoc can be hard to implement because gestures
collide [Long 1999].
Ad hoc doesn’t allow definition of new gestures
Harder to perfect gestures in a systematic system
Consistency of gestures across applications is better
in ad hoc systems
GRANDMA
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Gesture Recognizers Automated in a Novel Direct
Manipulation Architecture
Co-developed with a the Gesture-based Drawing
Program (GDP)
Major features: build by example
Extensions:
 multi-touch
 eager
gestures
GDP design
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Gesture input handlers are associated with classes
 Any
subclass is also automatically associated with
gesture handler. Examples:
 Associating
the “X” shaped delete gesture with
GraphicalObject automatically associates it with all
Rectangle, Line, Circle, etc. objects.
 Associating the “L-shaped” create rectangle with
GdpTopView associates it with any GDP window.
 Note
the difference with the interactor model, which we
implemented in our homework, which associates input
handlers with groups.
GRANDMA recipe
Create a new gesture handler and associate it
with a class.
Draw gesture ~15 times.
Define semantics (in Objective C)
1.
2.
3.
1.
2.
3.
Gesture is recognized
Mouse movements after recognition
Gesture finishes
Formal Definitions
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Gesture is represented as an array g of P sample
points:
Gp = (xp, yp, tp)
0 ≤ p ≤ P
Problem: Given an input gesture g0 and set {C1,
C2,…} of gesture classes determine which class g
belongs to.
GRANDMA Algorithm
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13 Features
A gesture class is a set of weights assigned to each
feature
 Gestures
are given a grade by the linear evaluation
function resulting from the weights
 A gesture is assigned to the class with the maximum
grade.
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Training assigns weights to the 13 features
Gestures are rejected if the grade assigned to two
classes is similar
Limitations
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Only supports single stroke gestures.
 Why?
Avoids segmentation problem.
 Why? More usable, a single stroke is associated with a
single operation.
 Supports only a subset of the gestures that are part of
my definition.
 Segmentation problem = problem of recognizing when
one stroke ends and the next one begins.
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Fails to distinguish between some gestures
Hard to make size independent gestures
GRANDMA in Amulet
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Amulet was extended to allow gesture input
[Landay]
GestureInteractor
Interactor calls callback function which decides what
to do with the result of classification.
Classifiers are created with the GRANDMA training
algorithm
The features of GDP I discussed weren’t used.
$1 recognizer
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Most recognizers are hard to write:
 Hidden
Markov Models
 Neural networks
 GRANDMA requires programmers to computer matrix
inversions, discriminants, and Mahalanobis distances
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Toolkits are not available in every setting
Therefore creative types (e.g. curious college
sophomores) don’t implement gesture in the UIs
$1 goals
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Resilience to sampling
Require no advance math
Small code
Fast
1-gesture training
Return an N-best list with scores
$1 algorithm
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Resample the input
N
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evenly spaced points
Rotate
 “Indicative”
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Scale
 Reference
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angle between centroid and start point
square
Re-rotate and Score
 Score
built from average distance between candidate
and template points
Really $1?
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Hold up back page of paper
Thoughts?
My opinion:
 Algorithm
is simple enough to re-implement
 Major barrier is discovery problem
Limitations
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Cannot distinguish between gestures whose
identities depend on aspect ratios, orientations
 Square
from rectangle
 Up arrow from down arrow
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Cannot be distinguished based on speed
$1 Evaluation
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User study
 10
users
 16 gesture types
 30 entries each: 10 slow, 10 medium, 10 fast
 Compared recognizers
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Results:
 $1
.98% errors, GRADMA 7.17% errors
 Medium speed is best
 $1 and GRANDMA were fast enough
DiamondSpin
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Video
Toolkit for efficient prototyping of multi-person
shared displays (target = touch-screen tabletop)
Defines API for building tabletop applications
Gestures are defined in an ad-hoc manner
Conclusion
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Gesture support makes building gestures easy
Multi-touch gestures are easy
There remain significant challenges to building the
gestures of the future:
 Many
limitations of current approaches in 2D
 3D gestures are supported only in an ad-hoc manner
References (slide 1 of 2)
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Chia Shen, Frédéric D. Vernier, Clifton Forlines, Meredith Ringel.
"DiamondSpin: an extensible toolkit for around-the-table
interaction", In CHI '04, p. 167-174. ACM DL Ref
JO Wobbrock, AD Wilson, Y Li. "Gestures without libraries, toolkits
or training: a $1 recognizer for user interface prototypes", In UIST
'07, p. 159-168. ACM DL Ref
Dean Rubine, "Specifying Gestures by Example", Computer Graphics,
Volume 25, Number 4, July 1991, p. 329-337. ACM DL Ref
James A. Landay, Brad A. Myers. "Extending an existing user
interface toolkit to support gesture recognition." CHI'93 extended
abstracts, Pages: 91 - 92. ACM DL Ref
T. Westeyn, H. Brashear, A. Atrash, and T. Starner. "Georgia tech
gesture toolkit: supporting experiments in gesture recognition." In
Proceedings of the 5th international conference on Multimodal
interfaces, pages 85-92. ACM DL Ref
References (slide 2 of 2)
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Kent Lyons, Helene Brashear, Tracy Westeyn, Jung Soo Kim, and
Thad Starner. "GART: The Gesture and Activity Recognition Toolkit."
In HCI ‘07. Springer Ref
Jason I. Hong, James A. Landay. "SATIN: a toolkit for informal inkbased applications." In UIST '00: CHI Letters, vol 2, issue 2, p. 6372. ACM DL Ref
J. Allan Christian Long, J. A. Landay, and L. A. Rowe. " Implications
for a gesture design tool." In CHI '99, p. 40-47. ACM Press, 1999.
ACM DL Ref
B MacIntyre, M Gandy, S Dow, JD Bolter. "DART: a toolkit for rapid
design exploration of augmented reality experiences." ACM DL Ref
RC Zeleznik, KP Herndon, JF Hughes. "SKETCH: An interface for
sketching 3D scenes." In SIGGRAPH 96, p. 163-170. ACM DL Ref
SATIN
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Video