Recognizing text through sound alone.

Paper
Li, Wenzhe, and Tracy Anne Hammond. "Recognizing text through sound alone." Twenty-Fifth AAAI Conference on Artificial Intelligence. 2011.
Direct Link: http://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/download/3791/4119

Summary

This paper introduces a novel approach to sketch recognition using the sound profile of a sketch drawn by scratching on a surface. The authors combine time domain features (mean amplitude) with frequency domain features (Mel-Frequency cepstral coefficients) from the sound profile, after pre-processing to achieve 80% recognition accuracy on recognizing letters in the alphabet sketched out in a constrained manner.

Discussion

Pros

The work is quite novel. I believe GoogleX came up with an idea that is somewhat similar to this one, using similar properties of surface interaction as input for android.

Cons.

The input is constrained to a given surface and a given user.

The input is also constrained to letters drawn out a specific way.

Some of these constraints can be overcome. The authors did not provide metrics on performance without these constraints so the reader can get a sense of how much improvement was gained as a result of so many constraints.

PaleoSketch: accurate primitive sketch recognition and beautification

Paper
Paulson, Brandon, and Tracy Hammond. "PaleoSketch: accurate primitive sketch recognition and beautification." Proceedings of the 13th international conference on Intelligent user interfaces. ACM, 2008.
Publication Link:http://dl.acm.org/citation.cfm?id=1378775

Summary

The work presents a primitive sketch recognition and beautification system known as paleosketch. The idea behind paleosketch is to recognize sketches based on a bottom up approach of identifying low-level primitive shapes as components that combine to form a recognizable high-level shape. The second stage of this system is to return a beautified version of the recognized shape.
To achieve this, they develop two new features in the pre-recognition stage: the normalized distance between direction extremes (NDDE) and the direction change ratio (DCR). The former computes the the difference between the point of highest direction value (ie dy/dx) and the lowest value normalized by stroke length.This feature is able to identify curved shapes (high NDDE values) from poly-lines which have lower NDDE values. The latter DCR value is computed as the maximum change in direction divided by the average change. This value is higher for a poly-line, whereas curves have a much lower value in comparison.

Discussion

Pros

The work is very thorough in presenting the details involved in the implementation.
They introduce two novel features for sketch recognition.

Cons.

A lot of thresholds are used, which are based on training data. Which seems like a lot of tuning. I would like to see how their results change as these different parameters are adjusted.

Combining corners from multiple segmenters.

Paper
Wolin, Aaron, Martin Field, and Tracy Hammond. "Combining corners from multiple segmenters." Proceedings of the Eighth Eurographics Symposium on Sketch-Based Interfaces and Modeling. ACM, 2011.
Publication Link: http://dl.acm.org/citation.cfm?id=2021185

Summary

This work proposes a very interesting meta-classifier for corner finding. By treating the outputs of an ensemble of corner-finding classifiers as features (candidates for corners in a given sketch), the authors develop a corner subset selection process using a weighted error metric to select the best subset of features (which ideally should be the corners contained in the sketch).
They test and compare the performance of their classifier with existing methods to show an improvement in all-or-nothing accuracy.


Discussion

Pros

I like the approach of treating corner candidates as features and selecting the best set.

Cons.

I wonder how this system will work with n-1 corner finding classifiers. I either missed that or it isn't included in the work. The authors motivate the advantage of each classifier, but can they perform just as well or better (or worse) with one less classifier?

Revisiting ShortStraw: improving corner finding in sketch-based interfaces

Paper
Xiong, Yiyan, and Joseph J. LaViola Jr. "Revisiting ShortStraw: improving corner finding in sketch-based interfaces." Proceedings of the 6th Eurographics Symposium on Sketch-Based Interfaces and Modeling. ACM, 2009.
Publication Link: http://dl.acm.org/citation.cfm?id=1572759

Summary

This paper proposes a set of improvements to a previous corner finding algorithm titled shortstraw. In this work, the authors address the following shortcomings from the previous version of shortstraw:
1. Rigid resampling rate
2. The occurrence and consequences of a false corner. The authors address this by using a two staged corner finding stage with aggressive and relaxed threshold values in respective order.
3. The use of a dynamic threshold based on length of line segment to avoid missed corners
4. Sharp noise resulting from resampling: They device an elegant solution using the the behavior of angles around a point on an arch vs a corner to discern the difference between the two.


Discussion

Pros

Addresses much of the shortcomings of the shortstraw paper.
The work is well presented. Their solution to sharpnoise is very elegant

Cons.

It makes the shortstraw solution a little more complicated to implement.
I think their results should have focused more on the specific errors they were trying to address and not the overall performance metrics. Example, isolate cases where shortstaw falsely detects a corner for reason x from the improvement list. Then show how many of those are correctly resolved in the new algorithm.

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.