Tuesday, December 14, 2010

#23 InkSeine

Summary
InkSeine is a note taking app for tablet-pcs, that uses a gesture based interface for functions as searching, linking, and gathering. The interface promotes linking&interaction between the existing notes.

Discussion
This is actually an HCI paper than, sketch regocnition, though sketch recognition could be thought of a subset of HCI. The system sounds neat, but I need to see it in action for a good evaluation.

Reading #22 Plushie

Comment:
Sam

Summary
Plushie is a system that allows non-experienced users to design their own plush toys using the Teddy system. The gesture based interface provide tools tailored for manipulating the mesh for sewing. The system can also run a physics simulation on the mesh to simulate the dynamics of the real toy.

Discussion
I think this is a cool way to commercialize Teddy; Teddy has a almost grandma friendly interface, and the models design using Teddy system looks like nothing but plush toys. I'd like to know how well it did in the market.

Reading #20 MathPad2

Comment:
Chris

Summary
The paper presents a prototype application for sketching math equations and diagrams. The application allows handwriting of regular math notation and free form diagrams. The relations between the two can be established implicitly by the application or by explicit user gestures. This process is called association. The application can also solve some complex equations.

Discussion
I this is a very important domain in sketch recognition. Writing equations+diagrams for papers using mark-up languages has always been painful for scholars. I would not care about the equation solver, but if this tool just worked fine in converting hand written equations & diagrams to a mark-up language, it would be a useful one.

Reading #21 Teddy

Summary
Teddy is pretty much the paper that started all about the sketch based modeling. The idea is that given a closed free-from curve, finding its medial axis and placing evenly distributed circles on the medial axis perpendicular to the projection plane. These circles are then combine together by triangulation and a mesh is generated. Using the same interface user can modify the mesh, create handles or holes.

Discussion
I actually implemented a fair amount of ideas from this paper for my own research. The time it was published, it was like revolutionary approach to modeling. However it could not find real life applications besides being a digital toy.

Monday, December 13, 2010

Reading #18 Spatial Recognition Text & Graphics

Summary

This paper is another one using relation graphs like #16, but it uses the graph to build spatial relationships between strokes. After building the graph, they use classifier to classify the known subgraphs.

Discussion

It has been interesting to me again, since it used graphs, but I did not quite get how their classifier worked. If someone can comment on that, I'd be happy.

Reading #17 Distinguishing Text vs Graphics

Comment: sampath

Summary

This is another text distinguishing paper. It uses a feature set that includes gaps between strokes, their relation to each other and some characteristic features for classification, and uses about 9 of them.

Discussion
The classifier they use sounds like an overkill to me. The entropy paper was a lot simpler and still had similar accuracy.

Reading #16 Graph Based Symbol Recognizer

Comment:
Johnatan.

Summary

As the name suggest, this paper recognizes symbols by building a relational graph and matching the graph to the existing graph templates.

Graph isomorphisim is an np-hard problem in general. This paper uses 4 methods to find the closest match; Stochastic match, Greedy, Sort and Error Driven Matching.

The method scored about %90 accuracy tested on about 20 shapes.

Discussion
This idea is particularly interesting to me since I have been using graphs for my recognition assignments. And actually our final project was almost entirely based on this idea. Graph isomorphisim is an hard problem, but you can reduce the complexity by benefiting from geometry data as we&they did.