Comments:
Sam-bo
Summary:
Tahuti is a dual-view sketch recognition environment. It shows users both their original strokes and the interpreted UML view of them. Users are able to sketch just as they would on paper and have the system create the UML structures that would correspond to their sketches. Tahuti uses a multi-layer framework to process, select, recognize, and identify strokes. The paper provides some nice information as to how each step is performed and what algorithms are used.
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 paper was written in 2002. Since that time, a number of different systems have been designed that aide users in creating UMLs. I am not sure that a sketch-based approach to this task is relevant or efficient anymore, but at the time it seems like it was an excellent idea. Limiting user frustration is a must!
Full Blog Completion Status Achieved!
Monday, December 13, 2010
Reading #29: Scratch Input
Comments:
Sam
Summary:
Scratch Input is an acoustic-based gesture recognizer. It uses a modified stethoscope with a single microphone embedded into it in order to capture the propagation of sound waves through a solid, flat surface. Scratches have a high frequency, and thus frequency thresholds are employed to eliminate almost all noise from the system. Gestures are distinguished mainly by the number of amplitude peaks in their signal. People slow down when approaching corners in a drawing, and thus the lowest points in a signal correspond to "corners" in a gesture. Strokes can thus be segmented as the peaks between these corners. The single input sensor used was unable to differentiate between gestures that contain the same number of strokes, but has an accuracy of 90% for the gestures tested. The hardware device designed by authors was extremely affordable, thus allowing for Scratch Input to be applied to a variety of large surfaces as needed by potential users.
Discussion:
Good ol' scratch input! Our Sound Board project was based heavily off of the ideas in this paper. I would love to see the authors return to this work and employ multiple sensors. That would essentially do what Drew, George, and I tried to do with our 3rd and Final projects.
Sam
Summary:
Scratch Input is an acoustic-based gesture recognizer. It uses a modified stethoscope with a single microphone embedded into it in order to capture the propagation of sound waves through a solid, flat surface. Scratches have a high frequency, and thus frequency thresholds are employed to eliminate almost all noise from the system. Gestures are distinguished mainly by the number of amplitude peaks in their signal. People slow down when approaching corners in a drawing, and thus the lowest points in a signal correspond to "corners" in a gesture. Strokes can thus be segmented as the peaks between these corners. The single input sensor used was unable to differentiate between gestures that contain the same number of strokes, but has an accuracy of 90% for the gestures tested. The hardware device designed by authors was extremely affordable, thus allowing for Scratch Input to be applied to a variety of large surfaces as needed by potential users.
Discussion:
Good ol' scratch input! Our Sound Board project was based heavily off of the ideas in this paper. I would love to see the authors return to this work and employ multiple sensors. That would essentially do what Drew, George, and I tried to do with our 3rd and Final projects.
Reading #27: K-Sketch: A “Kinetic” Sketch Pad for Novice Animators
Comments:
Francisco the Awesome Millionaire with the Diamond Suit
Summary:
K-Sketch is a 2D animation sketching system designed for novice users. The authors conducted interviews with both animators and non-animators in order to come up with a range of tasks that could be supported by K-Sketch. They then implemented a nice set of features that allow users to quickly modify their basic sketches so that they can carry out simple animations.
Discussion:
K-Sketch reminds me of a sketch-based Prezi tool. It includes lots of features, some of which are are mapped strangely, and allows users to create some pretty cool stuff given a little bit of time and some patience. It seems like the system would really allow users to express themselves, which is the best thing that could be hoped for.
Francisco the Awesome Millionaire with the Diamond Suit
Summary:
K-Sketch is a 2D animation sketching system designed for novice users. The authors conducted interviews with both animators and non-animators in order to come up with a range of tasks that could be supported by K-Sketch. They then implemented a nice set of features that allow users to quickly modify their basic sketches so that they can carry out simple animations.
In a laboratory experiment that compared K-Sketch to a more formal animation tool (PowerPoint), participants worked three times faster, needed half the learning time, and had significantly lower cognitive load with K-Sketch.That sums it up!
Discussion:
K-Sketch reminds me of a sketch-based Prezi tool. It includes lots of features, some of which are are mapped strangely, and allows users to create some pretty cool stuff given a little bit of time and some patience. It seems like the system would really allow users to express themselves, which is the best thing that could be hoped for.
Reading #26: Picturephone: A Game for Sketch Data Capture
Comments:
Marty
Summary:
Picturephone is a sketching game used to gather labeled data in a way that is fun (entertaining) for users. Picturephone works in a similar manner to that of the game Telephone, wherein players repeat phrases to each other in a linear fashion and see how the phrase evolves with each iteration. In Picturephone the players alternate between drawing a sketch and describing it. An additional player is then assigned the task of judging how similar the sketches are. In this way, the authors manage to get labeled data and a relevance/accuracy metric without having to do any of the work themselves.
Discussion:
Picturephone was discussed in reading #24. I liked it then and I like it now. Again, these types of sketching games seem like a good way to gather labeled data without having user repetitively draw the same shape over and over and over.
Marty
Summary:
Picturephone is a sketching game used to gather labeled data in a way that is fun (entertaining) for users. Picturephone works in a similar manner to that of the game Telephone, wherein players repeat phrases to each other in a linear fashion and see how the phrase evolves with each iteration. In Picturephone the players alternate between drawing a sketch and describing it. An additional player is then assigned the task of judging how similar the sketches are. In this way, the authors manage to get labeled data and a relevance/accuracy metric without having to do any of the work themselves.
Discussion:
Picturephone was discussed in reading #24. I liked it then and I like it now. Again, these types of sketching games seem like a good way to gather labeled data without having user repetitively draw the same shape over and over and over.
Reading #25: A descriptor for large scale image retrieval based on sketched feature lines
Comments:
Paco
Summary:
So you need to find an image online... do you describe it in words? What if you describe the wrong parts of the image because those are what you deem important? How do you quantify an image's importance? You could draw the image, but then why do you NEED it if you can draw?!
Ok, so obviously sketching to search for images would be cool. So cool in fact that the authors of this paper developed a system that does it. Their system is designed to query beastly databases containing millions of images. Actual images and the user's input sketch are preprocessed the same way which allows for matching based on similar descriptors.
Database image descriptors are cached in memory, and clusters are created based on similar colors. Searches take up to 3.5 seconds.
Discussion:
Awesome! Like Paco and I were talking about, this idea could be used to teach both users and the system words in different languages. If you draw simple items such as a tree or a cat, then you could also provide the written description (word) in your native language. Once you select a result, the system could then "learn" that your word describes that image and be able to employ cross-language searches in the future.
Paco
Summary:
So you need to find an image online... do you describe it in words? What if you describe the wrong parts of the image because those are what you deem important? How do you quantify an image's importance? You could draw the image, but then why do you NEED it if you can draw?!
Ok, so obviously sketching to search for images would be cool. So cool in fact that the authors of this paper developed a system that does it. Their system is designed to query beastly databases containing millions of images. Actual images and the user's input sketch are preprocessed the same way which allows for matching based on similar descriptors.
Database image descriptors are cached in memory, and clusters are created based on similar colors. Searches take up to 3.5 seconds.
Discussion:
Awesome! Like Paco and I were talking about, this idea could be used to teach both users and the system words in different languages. If you draw simple items such as a tree or a cat, then you could also provide the written description (word) in your native language. Once you select a result, the system could then "learn" that your word describes that image and be able to employ cross-language searches in the future.
Reading #24: Games For Sketch Data Collection
Comments:
Kim!
Summary:
The authors of this paper are interested in allowing users to freely move between sketching domains rather than be restricted to a certain one. This allows for a more natural sketching session akin to the use of pen-and-paper. In order to gather data on sketches and user-provided descriptions, the authors implemented a multiplayer sketching game.
The two online games created for data gathering are called Picturephone and Stellasketch. With Picturephone, players switch between describing a scene and drawing it. The next user interprets either the drawing as a new description or the description as a new drawing. Players then rate the various drawings to denote similarity (the more similar the better). The Stellasketch game is similar to Pictionary. A single user is given a topic and begins to draw it. The other players privately label the sketch with their guesses at various stages in its design process. Because users enjoyed playing the game, the authors were able to gather labeled sketches in the background.
Discussion:
I like this idea. You can hide data gathering techniques in games that people enjoy playing. We should implement something like this into Sousa studies because sometimes the redundancy of providing examples is tiring. Make users label their own stuff! Reduce your workload!
Kim!
Summary:
The authors of this paper are interested in allowing users to freely move between sketching domains rather than be restricted to a certain one. This allows for a more natural sketching session akin to the use of pen-and-paper. In order to gather data on sketches and user-provided descriptions, the authors implemented a multiplayer sketching game.
The two online games created for data gathering are called Picturephone and Stellasketch. With Picturephone, players switch between describing a scene and drawing it. The next user interprets either the drawing as a new description or the description as a new drawing. Players then rate the various drawings to denote similarity (the more similar the better). The Stellasketch game is similar to Pictionary. A single user is given a topic and begins to draw it. The other players privately label the sketch with their guesses at various stages in its design process. Because users enjoyed playing the game, the authors were able to gather labeled sketches in the background.
Discussion:
I like this idea. You can hide data gathering techniques in games that people enjoy playing. We should implement something like this into Sousa studies because sometimes the redundancy of providing examples is tiring. Make users label their own stuff! Reduce your workload!
Reading #23: InkSeine: In Situ Search for Active Note Taking
Comments:
SAM
Summary:
active note taking - capturing and extending creative ideas, reflecting on a topic, or sketching designs.
InkSeine is a fluid interface designed to allow users to engage in active note taking. It employs a handwriting recognizer in order to allow users to add a new depth to their notes with the incorporation of searches that can serve as extensions to their selected note or information feed. It also uses gestures such as lasso to trigger actions such as searching for the encircled, hand-written phrase. Sketch recognition techniques are used to aide users in their sensemaking tasks, and are done so intuitively. The authors took the time to conduct initial user studies with lo-fidelity prototypes in order to maximize usability and focus on potential user scenarios and tasks. Context-based searches minimize cognitive overhead and, based on the authors' formative studies, lead to happy users.
Discussion:
This paper seems like something you would read in Dr. Kerne's class. It is an excellent example of iterative design, user interface concerns, and affordances and mappings. Don Norman is probably using InkSeine right now trying to figure out how some poorly designed door opens. I also like the author's use of popup windows that do not require users to navigate away from their current tasks just to view initial results. Good design and good use of sketching.
SAM
Summary:
active note taking - capturing and extending creative ideas, reflecting on a topic, or sketching designs.
InkSeine is a fluid interface designed to allow users to engage in active note taking. It employs a handwriting recognizer in order to allow users to add a new depth to their notes with the incorporation of searches that can serve as extensions to their selected note or information feed. It also uses gestures such as lasso to trigger actions such as searching for the encircled, hand-written phrase. Sketch recognition techniques are used to aide users in their sensemaking tasks, and are done so intuitively. The authors took the time to conduct initial user studies with lo-fidelity prototypes in order to maximize usability and focus on potential user scenarios and tasks. Context-based searches minimize cognitive overhead and, based on the authors' formative studies, lead to happy users.
Discussion:
This paper seems like something you would read in Dr. Kerne's class. It is an excellent example of iterative design, user interface concerns, and affordances and mappings. Don Norman is probably using InkSeine right now trying to figure out how some poorly designed door opens. I also like the author's use of popup windows that do not require users to navigate away from their current tasks just to view initial results. Good design and good use of sketching.
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