Reading #30

Comments:
Ozgur

Summary:

The paper introduced Tahuti, a geometrical sketch recognition system, which can create UML diagrams. Proposed system uses a multi-layer framework for sketch recognition. The stages of the multi-layer recognition framework are: 1) Preprocessing 2) Selection 3) Recognition 4) Identification.  Through user studies, the authors discovered that Tahuti's interpreted view was deemed to be easier to drawn in and easier to edit in than comparative systems.

Discussion:

This is a new area of application of sketch recognition. UML diagrams contains nearly all the straight lines. This makes the processing much easier. The system allows people to drag and move. This is a great idea of application.

Reading #29

Comment:
Wen zhe

Summary:

This paper provides an system that takes acoustic-based input,  as the name say, scratch. The user proposed to use a modified stethoscope through solid materials, a mic is attached to the surface. It is particularly good to amplifying sound and detecting high frequency noises. The author claim an average accuracy of 89.5%.

Discussion:

In my opinion, the major handicap of this idea is how to eliminate the noise. Compared wit sketch, scratch is much more noisy. And some systems even consider scratch itself as kind of noise. Maybe the stethoscope mic is the key to make this idea work. Well, I am not sure about the stethoscope. Make it plays all the magic in this paper.

Reading #28

Comments:
Ozgur

Summary:

This paper provides an evaluate system about how well people draw the face. From image side, face recognition is applied to model features from the desired human face. From the user side, a sketch recognition is applied to measure how similar it is to the features from human face of image. More over, the system can guide user step by step to draw a more accurate face.

Discussion:

This is a good way of combining computer vision technique with sketch technique. Feature extraction from face image is not very hard, the real hard part is how you can come up with this interesting application.

Reading #27

Comments:
Jianjie

Summary:

This paper proposed an animation tool K-Sketch that can help novice to create animations. This is a pen-based system that requires the user to give timing and spatial information.  This paper has adopted a novel optimization algorithm that makes the whole system simultaneously fast. K-Sketch currently supports all ten desired animation

operations: Translate, Scale, Rotate, Set Timing, Move Relative, Appear, Disappear, Trace, Copy Motion, and Orient to Path.

Discussion:

I have been wondering how sketch can be efficiently used for creating animation, and then here comes this paper. I really want to see how it works, the data from paper can not convince me how it really feels like.

Reading #26

Comments:
Jonathon

Summary:

This paper proposed a sketch-based game for collecting data on how people make and describe sketches. Actually Picture-phone has been talked in Reading 24. This paper has given a detailed description as well as the implementation of the system.

The system has three  mode:

Draw: Text description is given and players are asked to draw according to it.

Describe: Inverse as Draw, sketch is given, and players need to described.

Rate: Player needs to judge how well the drawings matches.

Discussion:

Not much to say about this paper, since it is the same one as in Reading #24. But more detailed. Again, this is really an interesting way of collecting datas.

Reading #25

Comments:
Jinjie

Summary:

This paper proposed a method of retrieving image with combined text information and sketch information. The whole is based on a descriptor, which is constructed by both color image and sketch, where the sketch actually provides the edges of the desired images. An edge histogram descriptor in a cell will be stored for each image. It takes up to 3 seconds to search an image among 1.5 million images.

Discussion:

The major contribution of this paper is the idea of using  sketch information combined with text. We have all experienced using text description to find a picture. It does not work well. This is really a very interesting idea of using sketch.

Reading #24

Comments:
Sampath

Summary:

This paper provides a game system where players interact from drawing. The system collects the raw sketch input and associate it with text information. Two game systems have been described in the paper, Picture-phone and Stella-sketch. While Picture-phone supports people to play at their own rate, a game of Stella-sketch  requires several people to play at the same rate.

Discussion:

In general, This paper has presented Picture-phone and Stella-sketch, two sketching games for collecting data about how people make and describe hand-made drawings. It is very interesting idea, since the game system will of course attract more people to collect data.

Reading #23

Comments:
Ozgur

Summary:

This paper has proposed InkSeine, which is a TabletPC that supports active note taking by coupling a pen-and-ink, this application that offers fast interactions for users to search, gather, and link across multiple documents. InkSeine interface is tailored to the unique demands of pen input, and that maintains the primacy of inking above all other tasks.

Discussion:

This paper is a perfect example to show the use of a text recognizer. However, I doubt the accuracy of this idea. Since different user has different habits of  organizing documents, will this system cover all the cases?

Reading #22

Comments:
Wenzhe

Summary:

This is another paper on generate 3D model from 2D sketch. The major difference between this one and previous reading is Plushie can not only generate 3D meshes, but also the texture attached to it, which makes the final 3D model more real. The user interactively draws free-form strokes on the canvas as gestures and the system will handle all the rest operations. Even the system is tested on children,  they can generate new plush toy.

Discussion:

It is definitely amazing to see this application. I am so surprised to see children who totally have on professional training of generating 3D models or even 3D spatial thinking. It is a great successes to even the most inexperience user.

Reading #21

Comment:
Sampath

Summary:

This paper designed a sketching interface for quickly and easily designing freeform models from 2D freeform strokes interactively on the screen, and the system will automatically constructs plausible 3D polygonal surfaces. In general, the user will be asked to draw a 2D sketch which contains the silhouette, indicates what can be seen from that angle. Then a 3D mesh will be generated in real time.

Discussion:

To create 3D model is tedious and hard. Actually I am really surprised to find they can create 3D polygons. It would be easy to just create a certain view from an angle. But to get a 3D polygons, this is really a great contribution.

Reading #20

Comments:
Longfei

Summary:

This paper has built a system Mathpad2 for users to efficiently do mathematical operations. The system will recognize sketch math notations or equations, then give the calculated results. Besides the sketch that user can write to the screen, the system also provide some useful gestures.MathPad also provide set of computational functions to choose from, as well as different colors to help to recognize. Current Mathpad2 can calculate simple equations, but failed to the complicated ones.

Discussion:

As a specific software, the author does not give a lot of details about the algorithm.  However, this paper has given a very good application of sketch recognition, and even prompted to a product.

Reading #19

Comments:
Jianjie

Summary:

The paper proposed the method of using Bayesian conditional random fields to recognize sketched. The conditional random files will not only take the current stroke but also the neighboring strokes. So the recognition is also done using contextual information.

Discussion:

The main contribution of this paper lies in the idea of using context information. It is reasonable to assume that all the strokes are related, since human tends to draw things related together. The math is fancy, and complicated to me.

Reading #18

Comments:
Ozgur

Summary:
In this paper, the author presents a framework for simultaneous grouping and recognition of shapes and symbols in free-form ink diagram. This paper has also adopted the graph method to recognize shapes. One advantage of this method is it does not require the order how user draw the strokes. For every symbol, a graph is build and then separated into smaller sub graph. The recognition is done between each sub graph.

Discussion:
A matching between whole graph is hard, however, if this matching could be applied to smaller sub graph, it is much easier. That is really the smartness of this paper.

Reading #17

Comments:
Drew

Summary:
In this paper the author proposed an algorithm to ditinguish text. The features extracted are gaps between strokes time data, their relation to each other and some characteristic features for classification. Then they use a HMM for recognition.

Discussion:
To ditinguish shape from text is a hard problem. I think the author has made the problem even harder by using HMM. However, I have found limitation since HMM asks for a fixed order, which means users have to draw in the same order.

Reading #16

Comments:
Yue

Summary:
In this paper, the author proposed a graph-based recognizer. Each sketch symbol is represented by attributes relational graph, where vertices represent a primitive, and each edge represents a relation between primitives.
For every input, the author did a graph marching to find the each corresponding node between two graphs. This is well-known NP -complete problem. However, author gives some approximate the best solution. The author defines six different marching score metric in this paper as well as its weight value, which get from empirical study. Result shows that about 93% accuracy.

Discussion:
The key idea of this paper is the graph matching problem that the author tried to solve. The accuracy depends on how well the approximate isomorphism algorithm. More improvement can be made here.

Reading #15

Comments:
 Yue

Summary:
This paper has proposed an image-based method, which follows vision ideas. In this method, the sketch is compared with a 48 by 48 bitmap image. This algorithm is similarity transformation- invariant, and fast. It needs to mention about the rotation. They first transform the symbol image into polar coordinates and mapped into [-pi, pi] range. In polar coordinate they can easily calculate the rotation angle and again transformed into screen coordinate to continue their recognitin.  The whole system is instances-based, so it is easy to add/remove already-defined class.

Dicussion:
It is a clever idea to convert the sketch into an image, so that all the algorithm related to vision ca be applied. However, I am pretty surprised about the speed. Since I assume the convertion from sketch to image might be slow, even though they can solve the rotation efficiently.

Reading #14

Comments:
Ozgur

Summary:

This paper used an entropy idea that can distinguish text as high entropy from non-text information as low entropy. Entropy represents uncertainty measurement, small and complex shapes usually have larger entropy. Strokes are translated into a string of characters representing the angle that stroke is pointed. An entropy model has been introduced that can store the degree of curvature and measure the density. They have achieved a 92% recognition rate.

Discussion:

The idea of entropy is very interesting. However, I doubt the assumption that text has big entropy, since complex shape can also result in big entropy.

Reading #13

Comments:
Ozgur

Summary
This paper used features from texts such as curvature, speed, intersection, etc to distinguish the shape described by text. Totally 46 features have been chosen and grouped in to 7 categories. The experiment was tested on 26 participants, and they use a statistical partition to find 8 of these features 

are found useful to split text strokes and build a decision tree.

Discussion:
This paper has broaden our horizon, that sketch recognition may not just be points and lines. Since it is the human who is drawing, it is reasonable that human can give other information like text.