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!
Tuesday, December 14, 2010
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...
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.
Reading #23: InkSeine: In Situ Search for Active Note Taking (Hinckley)
Comment:
Chris
Summary:
The paper introduce the fluid interface that encourage users to engage in active note taking. InkSeine is a Tablet PC application that supports active note taking by coupling a pen-and-ink interface with an in situ search facility that flows directly from a user’s ink notes InkSeine integrates four key concepts: it leverages preexisting ink to initiate a search; it provides tight coupling of search queries with application content; it persists search queries as first class objects that can be commingled with ink notes; and it enables a quick and flexible workflow where the user may freely interleave inking, searching, and gathering content. InkSeine offers these capabilities in an interface that is tailored to the unique demands of pen input, and that maintains the primacy of inking above all other tasks.The author also do the user studies so that maximize usability and focus on potential user scenarios.
Discussion:
Seems good,but I am not sure that I like the system. I guess that the system might not have fast speed.
Chris
Summary:
The paper introduce the fluid interface that encourage users to engage in active note taking. InkSeine is a Tablet PC application that supports active note taking by coupling a pen-and-ink interface with an in situ search facility that flows directly from a user’s ink notes InkSeine integrates four key concepts: it leverages preexisting ink to initiate a search; it provides tight coupling of search queries with application content; it persists search queries as first class objects that can be commingled with ink notes; and it enables a quick and flexible workflow where the user may freely interleave inking, searching, and gathering content. InkSeine offers these capabilities in an interface that is tailored to the unique demands of pen input, and that maintains the primacy of inking above all other tasks.The author also do the user studies so that maximize usability and focus on potential user scenarios.
Discussion:
Seems good,but I am not sure that I like the system. I guess that the system might not have fast speed.
Reading #22: Plushie: An Interactive Design System for Plush Toys (Mori)
Comment:
Chris
Summary:
The paper introduced the plushie, an interactive system that allows nonprofessional users to design their own original plush toys. The system provide different gestures for different editing behaviors. The system also provide feeback at real time that let people know how the 2D textured pieces looks like. The experiment results shows that the participants, which consists of kids, can draw flush toys without any difficulties and they also enjoy the system.
Discussion:
Honestly, I am not interested in this system, but in the HCI perspective, the system is very useful for aiding users to draw what they are hard to do before. The system seems more intesting for kids. I guess kids will be happy with the system and can draw very funny toys with the help of this system. In all, I am not so into the detail of system due to the fact that I am not interested in this system.
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.
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.
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.
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?
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.
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.
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.
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