ShortStraw: A Simple and Effective Corner Finder for Polylines

Paper
Wolin, Aaron, Brian Eoff, and Tracy Hammond. "ShortStraw: A Simple and Effective Corner Finder for Polylines." SBM. 2008.
Direct Link: http://www.researchgate.net/profile/Tracy_Hammond/publication/220772398_ShortStraw_A_Simple_and_Effective_Corner_Finder_for_Polylines/links/0deec529f76e58d523000000.pdf

Summary

This paper introduces a simplistic approach to corner finding using the concept of straws. Straws, as defined in the paper, a very simple way of finding the arc length of the angle between lines, identifying a characteristic property (threshold) for corners and using this approach to discover them. The system implements both bottom up and top down approach. After discovering candidate corners, it further filters corners by comparing consecutive candidates for straightness, ie if they are corners, then they shouldn't form a straight line.

Discussion

Pros

Simple yet effective approach.
They present thorough comparison of their work and existing work along with insights on limitations of the work.

Cons.

If anything, i would have liked to see data on effects of window size on performance. Also, some of the suggested improvements could have easily been tested and presented.

A domain-independent system for sketch recognition.

Paper
Yu, Bo, and Shijie Cai. "A domain-independent system for sketch recognition." Proceedings of the 1st international conference on Computer graphics and interactive techniques in Australasia and South East Asia. ACM, 2003.
Publication Link: http://dl.acm.org/citation.cfm?id=604499

Summary

In this work, Bo et al. developed a user interface for recognizing low-level and high level sketches without prior domain knowledge as a source for performance improving constraints. Their system is able to recognize smooth curves, hybrid shapes and polylines. 

Discussion

Pros

One interesting merit is their ability to use only low-level geometric content of the strokes to achieve object recognition.
They make very good use of diagram illustrations that help to understand some of the poorly described algorithms used in the paper.
The equations presented for direction and curvature are interesting. Since they are not cited, I assume they developed this metric, which proved to be quite useful.
Overall I think the work is impressive.

Cons.

I feel that there are too many hard-coded rules used in this work to make it truly 'generic'. The authors themselves attest to this fact.
They also do not provide any useful data indicating where the system fails and why, which i feel is important. 

Sketch based interfaces: early processing for sketch understanding

Paper
Sezgin, Tevfik Metin, Thomas Stahovich, and Randall Davis. "Sketch based interfaces: early processing for sketch understanding." ACM SIGGRAPH 2006 Courses. ACM, 2006.
Publication Link: http://dl.acm.org/citation.cfm?id=1185783

Summary

Sezgin et al. develop an intelligent sketch interface. The system allows free-form sketch input, and subsequently beautifies and identifies basic components entered. This is accomplished in 3 stages: a stroke approximation stage, a beautification stage, and a final recognition stage. The first stage uses an algorithm based on speed thresholds to identify properties of basic strokes. The second stage makes minor adjustments to formalize sketch properties (such as straightness of lines, curves etc) and enforcement of geometric properties such as perceived parallel or orthogonal relationships. While the final stage applies basic object recognition using template matching.

Discussion

Pros

The paper was very well written.
The use of motion characteristics of sketches was very interesting.

Cons.

 I wonder why they only use speed. I would think acceleration and other properties relating to movement can provide useful information as well.

What!?! no Rubine features?: using geometric-based features to produce normalized confidence values for sketch recognition.

Paper
Paulson, Brandon, et al. "What!?! no Rubine features?: using geometric-based features to produce normalized confidence values for sketch recognition." HCC Workshop: Sketch Tools for Diagramming. 2008.
Direct Link: https://www.cs.auckland.ac.nz/research/conferences/skekchws/proceedings/vlhcc_stws_p57.pdf

Summary

This work develops a novel method to provide uniform confidence measurements to geometrically recognized complex shapes. To achieve this, the authors use a combination of geometric and gesture based features with a quadratic classifier to recognize single stroke primitives such as lines, ellipses, helix etc. They apply feature subset selection to reduce a 44 dimension feature set to about 9 dimensions and provide a direction for future application of their work.

Discussion

Pros

It is an interesting approach to providing confidence measures to geometric features.
The use of feature selection methods resulted in fairly high accuracy, but more important is the demonstration of useful confidence measures in addition to high accuracy.
The paper was very well written.

Cons.

I find it odd that the authors dedicated a full page or more to describing sequential forward selection technique in feature subset selection. To me, it diverted attention away from the primary objective of the work, which was in developing accurate geometric based features with reliable, uniform confidence measures.

They also failed to elaborate on performance. I mean, the primary purpose was not really recognizing but giving confidence estimates. This ability is not at all discussed or shown in their results section. They spent way too much time talking about SFS...

Visual similarity of pen gestures

Paper
Long Jr, A. Chris, et al. "Visual similarity of pen gestures." Proceedings of the SIGCHI conference on Human Factors in Computing Systems. ACM, 2000.
Publication Link: http://dl.acm.org/citation.cfm?id=332458

Summary

This research tries to measure similarity between pen/sketch gestures as perceived by human beings. With the ability to measure similarity, the authors hypothesis that the latter will serve as a good input for feature sets that quantify gesture characteristics. These features, they claim, will better differentiate between features in a way that can be captured computationally.

Discussion

Pros

The idea sounds quite novel.
They performed two experiments that were able to validate claims through multiple differing observations.
The method is a simplistic approach that seems to be effective for capturing differentiators that were otherwise not so intuitive (example: the aspect vs. log(aspect) observation).

Cons.

The paper was not easy to read or follow. 
They do not do a good job of plainly expressing exactly what they did and what their results were. It felt like pulling a tooth to be honest. But it was fairly good research notwithstanding. 

Specifying gestures by example.

Paper
Rubine, Dean. Specifying gestures by example. Vol. 25. No. 4. ACM, 1991.
Publication Link: http://dl.acm.org/citation.cfm?id=122753

Summary

In this paper, Rubine introduces a gesture recognition based framework for a user interface. The primary driver for the recognition used by the GRANDMA system is a set of classical physics based features (13 of them). These features capture the dynamics of a stroke. In doing so, the features try to differentiate between the speed, angular velocity, shape, length and other similar characteristics of features.

Discussion

Pros

The method used to develop these features are very simplistic but appear to be effective based on the results delivered in the paper. I'd be interested in seeing the full dissertation for this work to see what was left out.
The use of graphics and pictorial illustration was masterful. Many of the concepts used in the paper are captured and described beautifully in the text.

Cons.

I find none. This was a very thorough first step at using features to describe gestures (which intuitively are just odd shapes). And on that note, i think a fine motivation for the features would have been tying these features to classical geometry, physics, and psychology. How does the human brain recognize the difference between a circle and a square? Or better: If one were asked to traverse a path (physically) while blindfolded, how does one mentally picture the shape of the path? The mechanics involved in this (how the brain intuitively solves this problem) are not very different from Rubines approach. 

Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes

Paper
Wobbrock, Jacob O., Andrew D. Wilson, and Yang Li. "Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes." Proceedings of the 20th annual ACM symposium on User interface software and technology. ACM, 2007.
Publication Link: http://dl.acm.org/citation.cfm?id=1294238

Summary

The $1 recognizer paper describes a 'simple' template matching algorithm that handles scaling, rotation and translation. The authors describe the algorithm, along with implementation details. They also compare their algorithm with two other well known algorithms used in gesture recogniton (Rubine, and dynamic time warping).  

Discussion

Pros

It is a simple algorithm to implement.
The heuristic approach to reducing the iterations for rotation alignment was very creating and interesting. 

Cons.

The paper was difficult to follow. 
I would like to know if their heuristic for rotation and alignment has some theoretical grounding in psychology. Do people tend to draw or start a drawing based on some mental orientation or projection of the object? Maybe I missed the motivation behind this heuristic but I feel it is a key contribution that should have been better highlighted.
It's simplicity makes it very limited in its capability. I suspect that the size of the 'template' library will grow significantly given its high sensitivity to variation in shapes.

I'd also like to see the running time for this algorithm and how it compares with others.