Reading #30: Tahuti: A Geometrical Sketch Recognition System for UML Class Diagrams (Hammond)

Comment:
Chris

Summary:
The paper introduced Tahuti, a geometrical sketch recognition system for UML diagram. The system is dual-view sketch recogniton environment, which based on a multi-layer recognition framework which recognizes multi-stroke objets by their geometrical properties allowing users the freedom to draw naturally as they would on paper. The system can provide two views, interpreted version of stroks and original strokes. Users can choose to switch between them at any time. And users can draw and edit while viewing either their original strokes or the interpreted version of their strokes engendering user-autonomy in sketching. The result shows that uers preferred Tahuti to a paint programs and to Rational Ross.

Discussion:
The system looks nice. I actually watched the demo from youtube about this system. It works pretty good for editing UML diagram, user can drag and move the class components to whevere they want. The system give much freedom for people to draw. And the beautiful idea here is to encourage people to switch between different views. In all, nice system which deleveoped by my advisor!

Reading #29: Scratch Input Creating Large, Inexpensive, Unpowered and Mobile Finger Input Surfaces (Harrison)

Comment:

Chris

Summary:

The paper introduces a acoustic-based recognier that relies on the unique sound produced when a fingernail is dragged over the surface of a textured material such as wood, fabric,or wall paint. The recognizer they made here can recognize 6 different basic shapes by obtaining about 90% accuracy with less than five minutes of training and on wide variety of surfaces.For recognition, they actually use the amplitude of waveform and figure out the shape of waveform like traiangle, rectangle, and so on. The other contribution of this paper is to introduce several example applications that can use this technology, mainly for the mobile applications.

Discussion:

Awesome idea! They used sketching sound source, which can be very useful for sketch recognition. In fact, our projects idea come from this this paper. As far as I know, this

is the first paper that show the sketching sound can be used to variety of applications in real life,especailly for the mobile devices. This work could be extend to more complicate cases, which I am trying to do.

Reading #28: iCanDraw? – Using Sketch Recognition and Corrective Feedback to Assist a User in Drawing Human Faces (Dixon)

Comment:

Chris

Summary:

This paper shows the first system for using computer-aided instruction to assits a student in learning to draw human faces. This system uses face and sketch recognition to understand the reference photograph of a human model and a user's drawing of it. When users drawing, the system can give feedback step by step once user require it. Actually, there is matching between template image and user drawing. Face recogniton algorithm is applied to template image and get feature set for this template image. And by using these features, the system can give feedback to users at real time.

Comment:

This is one of paper that written by our lab. However, I didn't use the system before. But anyway, the idea behind this paper is excellent. Giving feedback at real time is one important advantage for tutoring/educational system.

Reading #27: K-sketch: A 'Kinetic' Sketch Pad for Novice Animators (Davis)

Comment:

Chris

Summary:

In this paper, they introduced the K-Sketch, a general purpose, informal,2D animation sketching system. At first, they do the field studies investigating the needs of animators and would-be animators helped us collect a library of usage scenarios for their tool. Then they design the set of operations for animation as well as some optimazation techniques they used in K-Sketch. Experiment shows that K-Sketch when comparing to formal animation tool(ppt), participants worked three times faster, needed half the learning time, and had significantly lower cognitive load with K-Sketch.

Discussion:

In fact, I am thinking about building such kind of system, and K-Sketch is what I am expecting. For most of novice users, using flash software for animation is not easy task, user need such system to fasten the process of making animation. After reading this paper, I cannot wait using that system to see how is it working!!

Reading #26: Picturephone: A Game for Sketch Data Capture (Johnson)

Comment:
Chris
Summary:
In this paper, the author introduce the PicturePhone, a game for sketch data capture. This game needs three participants, the first participant is required to draw sketch according to the discritpion. The game works as follows: Player A is given a text description, and they must make a drawing that captures that description as accurately as possible. Player B receives the drawing and endeavors to describe it. Player C is given Player B's description and draws it. An unrelated player D is asked to judge how closely Player A and C's drawing match, which assigns a score to players A,B and C. The purpose of this system is to collect sketch data for researchers.


Discussion:
This is a smart idea. The data collection is very important for sketch recogntion research. And data collection itself is not easy for researchers. This software gives us a new way for collecting data, while encourage people to participate in data collection. But the problem is, at least in my opinion, I will not be happy to play such game, which seems boring to me and waste of time, and I don't care about how much score I get...

eading #25: A Descriptor for Large Scale Image Retrieval Based on Sketched Feature Lines (Eitz)

Comment:

Chris

Summary:

The paper addresses the problem of large scale sketch based image retrieval. The main contribution is a sketch-based query system for image database containing millions of images.For the traditional search system, users can only provides word for searching. For searching for images, they also have to provide word to describe the image, which seems very hard in some cases. However, people could remember how the image looks like, and they can tell you by sketching them on the paper. The system is doing that! Using sketched image to search for real images in the image database. The result show that their system is superior to a variant of the MPEG-7 edge histogram descriptor in a quantitative evaluation.

Discussion:

Wow! Awesome! This is my favorite system that I've seen before. As far as I know, this likely be the future search engine. Combine sketch and word to search for image is very cool thing and can give much accurate images that people want to retrieve. And it is better to let the system can learn from the people's action like selecting picuture.I am very excited to see this system.

Reading #24: Games for Sketch Data Collection (Johnson)

Comment:

Chris

Summary:

This paper is very similar with what I read just before. The paper introduces several systems which aim to collect sketching data for research purpose. These data can be shared by researchers on the web. In this paper, they showed two system, Picturephone and Stellasketch two sketching games for collecting data about how people make and describe hand-made drawings. The first system is already described in the previous paper in detail. Stellasketch is a synchronous, multi-player sketching game similar to the parlor game Pictionary. One player is asked to make a drawing based on a secret clue. The other palyers see the drawing unfold as it is made and privately label the drawing. While Picturephone's descriptions are meant to be used to recreate a drawing.

Comment:

This paper seems more detail than the previous paper. As said in previous discussion section, I pretty much the idea behind this author. They make the collecting sketching data, a boring task for participant, more interesting, in addition, this might encourage some people to participant these games. However, we might doubt about the correctness of these sketch data.