Summary
Protractor, by Yang Li, introduces a template based recognizer just like $1 recognizer, but with a twist: The angle of the vector from centroid to the sample point is used for error computation instead of distance. The protractor consumes less memory and time than $1 recognizer. The algorithm is stated to be suitable for mobile devices
Discussion
Angle might seem to be a better metric than position at first sight, but there is the fact that a stoke point on the same vector will give the same angle regardless of its position which may cause error in recognition.
Wednesday, September 22, 2010
Monday, September 20, 2010
Reading #5 $1 Recognizer
Summary
As the title suggests, a cheap way of gesture recognition is introduced in this paper. Due to it's simplicity, the algorithm can be implemented on light-weight interfaces such as browsers and mobile devices and actually is designed for that purpose in mind.
The recognizer has the following steps:
-Resample stroke points so you can have a more uniform distribution of them on the stroke.
-Reset the orientation of stroke by rotating it based on indicative angle; the angle between the first point and the centroid.
-scale&translate
-compute the total distance from each template and pick the template which minimizes the distance.
Discussion
I think this paper presents the most straightforward approach to sketch recognition. It is so straightforward that I could almost get the same type of answer if I asked my mum to describe a recognition algorithm. But I guess someone has to publish that paper and it should be there in the literature regardless of its complexity. But it works.
As the title suggests, a cheap way of gesture recognition is introduced in this paper. Due to it's simplicity, the algorithm can be implemented on light-weight interfaces such as browsers and mobile devices and actually is designed for that purpose in mind.
The recognizer has the following steps:
-Resample stroke points so you can have a more uniform distribution of them on the stroke.
-Reset the orientation of stroke by rotating it based on indicative angle; the angle between the first point and the centroid.
-scale&translate
-compute the total distance from each template and pick the template which minimizes the distance.
Discussion
I think this paper presents the most straightforward approach to sketch recognition. It is so straightforward that I could almost get the same type of answer if I asked my mum to describe a recognition algorithm. But I guess someone has to publish that paper and it should be there in the literature regardless of its complexity. But it works.
Monday, September 13, 2010
Reading #4 Ivan's Sketchpad
Summary
In this paper, Ivan Sutherland, one of the most influential names in CG, talks about an HCI device even before mouse. SketchPad is a pen based system used to draw and define geometric shapes and also apply constrains upon them.
The first part focuses on drawing the primitives and the overall design of the system. The second part explains the data structure of the system and the well defined format geometries are stored for other applications. The ring structure allowed efficient operations on the shapes.
The rest of the paper talks about how light pen is tracked and the shapes are drawn to the screen. It also talks about the constraints that can be placed on shapes.
Discussion
I think that this paper is a fundamental stone for CAD/CAM systems. It addresses main issues of CAD systems from user interface to data structures. Probably 60 years ago the material was very innovative. Today I feel like any decent computer scientist can unsurprisingly solve it.
In this paper, Ivan Sutherland, one of the most influential names in CG, talks about an HCI device even before mouse. SketchPad is a pen based system used to draw and define geometric shapes and also apply constrains upon them.
The first part focuses on drawing the primitives and the overall design of the system. The second part explains the data structure of the system and the well defined format geometries are stored for other applications. The ring structure allowed efficient operations on the shapes.
The rest of the paper talks about how light pen is tracked and the shapes are drawn to the screen. It also talks about the constraints that can be placed on shapes.
Discussion
I think that this paper is a fundamental stone for CAD/CAM systems. It addresses main issues of CAD systems from user interface to data structures. Probably 60 years ago the material was very innovative. Today I feel like any decent computer scientist can unsurprisingly solve it.
Wednesday, September 8, 2010
Reading #1 Gesture Recognition (Hammond)
The paper is pretty much gesture recognition GREC 101. First thing it stresses out is that each gesture must be a path of pen in a single stroke and drawn in the same manner each time to be properly recognized.
Each stroke is represented as a vector in a gesture recognition system. The paper introduces two gesture recognition methods:
Rubin's method, which is the most popular one, computes 13 features of the stroke vector and uses a trained linear classifier to recognize a gesture. Rubin's method is reported as %95 accurate.
Next comes up Long's gesture recognition, which extended Rubin's 13 feature to 22 (took 11 from Rubin's and added 11 more combinations).
The paper finally introduces Wobbrocks $1 Gesture Recognizer, an easy to implement method but slower than linear classifier at run time. But not that you can find it in a dollar store :P
Discussion:
My personal scientific (?) opinion is that this paper is a great 101 to the subject matter. It is an easy read and popular gesture recognizers are clearly outlined. Maybe, I'd expect to see some more on Long's recognizer and it's comparison to Rubin's.
Each stroke is represented as a
Rubin's method, which is the most popular one, computes 13 features of the stroke vector and uses a trained linear classifier to recognize a gesture. Rubin's method is reported as %95 accurate.
Next comes up Long's gesture recognition, which extended Rubin's 13 feature to 22 (took 11 from Rubin's and added 11 more combinations).
The paper finally introduces Wobbrocks $1 Gesture Recognizer, an easy to implement method but slower than linear classifier at run time. But not that you can find it in a dollar store :P
Discussion:
My personal scientific (?) opinion is that this paper is a great 101 to the subject matter. It is an easy read and popular gesture recognizers are clearly outlined. Maybe, I'd expect to see some more on Long's recognizer and it's comparison to Rubin's.
Monday, September 6, 2010
Me, Myself, etc
Contact: mozgurgonen@yahoo.com
Standing: 1st year PhD w/masters.
Why 624: My research is on sketch based modeling & 2.5D sketching.
Can Bring: I have done fair amount of work with graph theory; topology construction/manipulation.
In 10 Years: In front of the computer.
Biggest advancement in CS: Machine Learning will advance. Not skynet, but smarter software that customizes itself based on user needs.
Favorite Undergrad Course: Data Structures.
Favorite Movie: Le Fabuloux Destin d'Amelie Poulain. Because watching it always makes you feel better.
Time Travel: Larry Page & Sergey Brin, when they launched Google in a garage. It'd be fun to ask them where they saw themselves in ten years.
Interesting Fact: I thought of a Facebook like college-networking site when I was in undergrad, but I was discouraged by some friends who claimed that there were gazillions of similar websites :/
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