Reading #21: Teddy: A Sketching Interface for 3D Freeform Design (Igarashi)

Comment:

Chris

Summary:

The paper presents a sketching interface for quickly and easily designing freeform models from 2D sketch to 3D models. The user draws several 2D freeform strokes interactively on the screen and the system automatically constructs 3D polygonal surfaces.The system can supports several operations for uer to create 3D models, including the operation to construct a 3D polygonal surface from a 2D silhouette drawn by the user. The result shows that the system can be accomplished the task at real time. The reaming part of this paper describes the algorithm for implementing the system.

Discussion:

Cool Idea! In fact, building 3D objects is very hard problem for novice user, including me. The nice contribution of this paper is, to provide multiple operations to control the process of modeling objects, and see the 3D modeling in real time so that encourage the user to modify it real time as well. The work has great potential for animation task , as well as eduationg people to draw 3D simple objects. I like the paper and I hope I can use the system to produce some nice 3D pictures.

Reading #20: MathPad2: A System for the Creation and Exploration of Mathematical Sketches (LaViola)

Comment:
Chris


Summary:
In this paper, the author describes the MathPad, a math algebra editing system, aims to provide easy use for users. The user can wrote the math equation on the screen and the system can automatically recognize the equation and solve them. The author also developed several useful gestures to edit or command the math equation that already wrote, including delete, scribble, tap, and etc. Besides recognize and solve the foundamental math equations for us, the system can also handle the matrix, including adding, multiplication, reverse,etc. And the system can also plot the funcion in the screen by using simple gesture. In all, Mathpad is very nice, and the best algebra editing system among all the sketch-based math system.




Discussion:
In fact, I read the paper long time ago. The system is very beautiful, and most of all, it is not easy to design and implement all these functionalities in one system. There are many difficulties when designing such system. The individual character recogniton is the first problem we should conquer, which itself is very hard problem. If the character set is large, the recogniton for individual character becomes very hard. In order to recognize the whole formula, the system needs formula parser, however, the parser need to handle with many difficulties and ambiguities in the math equaiton, like lower case, upper case, and etc. In all, Mathpad is awsome, but I still doubt about its accuracy, it should be not high if user does not draw carefully.

Reading #19: Diagram Structure Recognition by Bayesian Conditional Random Fields (Qi)

Comment:
Chris

Summary:
The paper uses bayesian conditional random fields to recognize sketched diagrams. Instead of recognize each element of digrams individually, they jointly analyzes all drawing elements in order to incorporate contextual cues. The classification uses the spatial and temporal information, and they have made a great assumption that classifying one object has impact one another object. The idea is very important when we utilizing the context informaiton into sketch recogniton. The result shows that their method can avoid overfitting problem and much better than maximum likehood and Maximum a posterior trained CRFs. The majority of this paper focused on mathematical detail of implementaiton.




Disccusion:
What a fantastic paper! The paper shows my initial idea about sketch recognition. I am always beliving that without context information of sketch, the recogniton is not feasible in most of cases, at least, does not obtain high accuracy. In order to maximize recognition accuracy, we must use the context information, also for the shape vs text task. They use baysian theory to incorpate this context information into their recogniton result. This is very very nice paper, and worth carefully reading it.

Reading #18: Spatial Recognition and Grouping of Text and Graphics (Shilman)

Comment:
Chris

Summary:
This paper shows a framework for simultaneous grouping and recognition of shapes and symbols in free-form ink diagrams. Their approach is completely spatial, that not require any ordering on the strokes. There framework works as follows:
1. Build a proximity graph.Each node corresponds to stroke, and edges are added when strokes are in close to one another.
2. Search through this graph and find the optimal groupings. They use cost function to control to find the optimal groupings.They uses dynamic programming and A* algorithm to make search. In this paper, they focused on A* search. Each state in search space corresponds to cost value. Due to this brute force search, they propose two optimiaztion approaches.
1) grouping is valid only if its vertices are connected in the neighborhood graph.
2) Restrict the size of each subset V in the graph to be less than constant k, which can greatly decrease the time complexity.
3. For the recogniton of each part, they use the Adaboost classifier which can be automatically learned from the training dataset.


The result shows that their method gains about 97% accuracy for their testing data set.


Discussion :
Fairly good paper. Instead of seperating the steps of segmentation and classifying each part, they simultaneously find the optimal grouping as well as recognition They use fairly general method A* to search through all the search space, which has great time complexity. They use another fairly general optimization approaches to control this searching. Even though the accuracy the reported is very high, there are some problems here. The threshod to control the build of proximity graph can be set unappropriately so that can miss good important groupings, even they threshod values works very good, we cannot avoid some missing groupings in pratice.

Reading #12. Constellation Models for Sketch Recognition. (Sharon)

Comment:
Chris

Summary:
This paper shows a system that adapts constelation or 'pictorial structur'model to the recognition of strokes in sketches of particular classes of objects. The model is designed to capture the structure of a particular class of object and is based on local features such as the shape or size of a stroke, and pairwise features, such as distance to other known parts. They uses the a probabilistic model from example sketches with know stroke labelings. The recogniton algorithm determines a maximum-likelihood labeling for an unlabelled sketch by serching through the space of possible label assignments using a multi-pss branch and bound algorithm. For searching, the current recognition process is largely top-down based


Discussion:
The paper seems interesting to me. Which is good paper for dealing with sketched picture. They use spatial information for recogniton, more specifically, use the spatial relathionship between each part. However, when then individual part is not correct, does the system can detect it?

Reading #11. LADDER, a sketching language for user interface developers. (Hammond)

Comment:
Chris

Summary:
The paper introduce ladder!, which is my advisor's thesis work! cool.. The paper deals with syntactic pattern recognition. Ladder is description language for shapes. Ladder hierarchcaly define shapes from low level to higher level. The most two important components of ladder is component section and contratint section. Each shape consists of several primitive strokes and some constraints. The ladder detaily describe how each shape is built by other shapes as well as constraints. The ladder is very descriptive and is very useful for describe complex shapes. After providing shapes description to ladder, the system can automatically generate recognizer to recognize these shapes. The result shows that the system works well in flow chart diagram and UML diagram recognition. In fact, after building the ladder grammer for each shape, the remaining task for system is to parse the grammer, which can be accomplished by efficient compiler.




Discussion:
Very nice paper. Beautiful syntatic approach for sketch recogniton. It maximally release the designer's task. However, inevitably, there are some problems or this system. The most important one is speed!, parsing grammer is not easy task, which generally be accomplished by brute force search which is exponential for the given input size. Even though the ladder implements some optimazation techiqnues. the system can still suffer from slow speed problem. In fact, this is typical problem for syntatic recognition. It is not hard to describe shapes by using ladder, however, it is hard to recognize it if the strokes containing in shapes is too large.

Reading #10. Graphical Input Through Machine Recognition of Sketches (Herot) Task(s)

Comment:
Chris

Summary:
The paper introduces HUNCH system, a hierarchy of inference programs for sketch recognition,which is very old system. HUNCH works by taking input data and running several layers of inference programs on top of that data, from basic shapes to 3D interface. The system cannot work for certain users but not for other people, which seems user dependent. But
the point that hierarchically recognize the sketch is important as well as returning multiple interpretations.


Discussion:
Nice but old paper. It wrote in 1976 35years ago!! wow! All the techinques mentioned here is already exists today. But we must adimit that this is very nice work at that time. This paper just gives me brief knowledge of how the techniques evoles.