#20: Sketch Principles

1. Five Have

• Perceptivity of Recognition
• Recognition must be build upon human's perception, so a sliding window used in a sketch recognition is different from one used in a computer vision application.
• Intuitive UI
• According to many paper such as  KimChi paper, the role of interface is very important for efficient sketching
• High tolerance
• This can be also explained by the sliding window of a computer vision application. If the tolerance is low, the difference which users are not able to find will bring different results. Then, this can cause low usability.
• less constrains
• Users want to draw sketches with computer in the way they draw with a pen and a paper. Constrains hinder free sketches.
• Consistency
• Same input - same output

2. Five Have not

• Inconsistency
• Same input - different output
• Many limitations
• Not allow users to sketch drawing in their own way
• Poor editing
• Editing takes a major portion of sketching. If it is poor, there is not much benefit.
• Bad UI
• Lower usability
• Low tolerance
• Users want to draw a horizontal line, but the line might be a little curved or leaned. if tolerance is low, this line or most lines drawn by users cannot be interpreted as a horizontal line.

Paper Review #19: Tahuti

1. Paper Bibliography

• Title: Tahuti: A Sketch Recognition System for UML Class Diagram

• Authors: Hammond, Tracy, and Randall Davis.

• Publication: AAAI Spring Symposium on Sketch Understanding. Vol. 3. No. 2. 2002.

2. Summary

• A Dual view, multi-stroke sketch recognition 
• Sketch freedom
• Taget application: UML diagram
• Focus on geometric properties

3. Terminology

• [WIKIPEDIA] Wizard of Oz experiment:
• a Wizard of Oz experiment is a research experiment in which subjects interact with a computer system that subjects believe to be autonomous, but which is actually being operated or partially operated by an unseen human being. 

4. Details

• Multi- layer framework

5. Evaluation

• Scale 0 to 5
• A) A paint program
• B) Rational Rose
• C) Tahuti in interpreted view
• D) Tahuti in drawn view
• Result
• Drawing 
• A: 2.25, B: 1.75, C: 4.375, D:3.1
• Editing
• A: 1.65, B.1.925, C: 4.825, D: 2.6

Paper Review #18: What!?! No Rubine Features?

1. Paper Bibliography

• Title: What!?! No Rubine Features?: Using Geometric-based Features to Produce Normalized Confidence Values for Sketch Recognition

• Authors: Paulson, Brandon, et al.

• Publication: HCC Workshop: Sketch Tools for Diagramming. 2008.

2. Summary

• Goal
• A hybrid approach that combines features from both traditional gesture-based recognition systems and geometric-based recognition systems.

3. Details

• Gesture-based recognition + Geometric-based recognition
• Rubine work (gesture part)
• Hierarchical structure (geometric part)

 

• 44 Features

• Data
• 1800 sketches

4. Evaluation

• Feature sub-selection

Paper Review #17: PaleoSketch

1. Paper Bibliography

• Title: PaleoSketch: accurate primitive sketch recognition and beautification

• Authors: Paulson, Brandon, and Tracy Hammond.

• Publication:  Proceedings of the 13th international conference on Intelligent user interfaces. ACM, 2008.

2. Terminology

• NDDE: Normalized distance between direction extremes
• DCR: Diction change Rate

3. Summary

• A new low-level recognition and beautification system that can recognize eight primitive shapes, as well as combinations of these primitives, with recognition rates at 98.56%. 
• Polyline: high DCR, low NDDE
• Curve: low DCR, high NDDE

4. Details & Evaluation

• Pre recognition
• Resampling
• Compute two features
• NDDE = dist(point with the highest delta, point with the lowest delta) / the length of the stroke
• DCR = biggest diction change / average direction change
• Line test

• Polyline, Ellipse, Circle, Arc, Spiral, Helix and Complex test

• Evaluation

Paper Review #16: LADDER

1. Paper Bibliography

• Title: LADDER, a sketching language for user interface developers

• Authors: Hammond, Tracy, and Randall Davis.

• Publication: Computers & Graphics 29.4 (2005): 518-532.

2. Summary

• LADDER is a language to describe how sketched diagrams in a domain are drawn, display and edited
• To simplify a development of a new sketch recognition interface

3. Details

• Three main parts
• Domain description
• Definitions of shapes: predefined shapes -> constraints
• Hierarchical, Abstract, 
• Vectors = sub components
• Translation
• Generating shape recognition
• Generating editing recognition
• Generating shape exhibitors
• Sketch Recognition system
• Recognition
• Editing
• Display
• Multi-domain recognitions system
• Primitive shapes -> domain shapes
• Constrains solver
• Take in a shape description and initial location for all of the sub shapes and outputs the shape with all the constraints satisfied.
• Example

4. Evaluation

• Auto-generation

you can download and run the code: http://srl.cse.tamu.edu/srlng/research/project/3

Paper Review #15: Recognizing Interspersed Sketches Quickly

1. Paper Bibliography

• Title: Tahuti: A Sketch Recognition System for UML Class Diagram

• Authors: Hammond, Tracy, and Randall Davis.

• Publication: Proceedings of Graphics Interface 2009. Canadian Information Processing Society, 2009.

Paper Review #14: Using Entropy to Distinguish Shape Versus Text in Hand-Drawn Diagrams

1. Paper Bibliography

• Title: Using Entropy to Distinguish Shape Versus Text in Hand-Drawn Diagrams

• Authors: Bhat, Akshay, and Tracy Hammond. 

• Publication: IJCAI. Vol. 9. 2009.

2. Summary

• Distinguish between shapes and texts 
• Text has higher entropy rate than shapes
•  Entropy rate = distinguishing factor
• Using only the entropy rate, a correct classification rate is 92.06%
• Favorable performance without training data

3. Terminology

• Entropy rate: accurate criterion of classification

4. Details

• Entropy
• Each point in a stroke is assigned a symbol based on the angle (corresponding angle in the Table 1)

In this figure, text has various symbol compared to rectangle.

• This symbol is the random variable on the basis of which we can calculate entropy.

• Use a zero-order entropy
• each symbol's probability of occurrence is determined independent of the previous symbols.
• Define an entropy model 'alphabet'
• Implementation
• Stroke Grouping
• A spatial and temporal threshold
• The successful rate: 99.78%
• After groping
• Resample 
• Transform each stroke into a string of symbols
• Calculate the percentage of the occurrence of symbols
• Classification
• Confidence Measure
• in order to integrate a classifier into other sketch recognition 
• Data collecting and Testing

5. Evaluation