Summary:
Lapides et al. designed and built a Tablet PC stand that can move vertically, allowing for a 3D drawing platform that switches the screen's view as the table is moved. The authors state that using the 3D Tractus will allow for a "direct mapping between physical and virtual spaces."
The frame of the 3D Tractus consists of aluminum bars and a table top, along with a counterweight that will balance the weight of the tablet and allow for the table top to slide up and down easier. The counterweight has to be tuned for each tablet's weight. A height sensor is built into the frame.
The drawing software for the system takes into account the height of the table when displaying a viewing angle to the user. The system uses line width as a depth cue, with farther lines thin and closer lines thick. An orthographic (cube) projection is used to demonstrate 3D depth, as well. Also, nothing of the sketch is displayed above the current tablet surface.
Discussion:
Although the idea of having a tactile way to sketch in 3D sounds appealing, the system could be implemented much better without a tactile, movable desk. Instead, having a z-axis button/wheel/control in the software will alleviate the issues with custom counterweights, a height constraint, awkward hand/arm positioning, and lack of mobility.
Also, the system is rather constrained with any large sketches since the user can move in the tablet's plane in infinite direction, but the vertical plane is limited to something like 40 centimeters.
Showing posts with label 3D inference. Show all posts
Showing posts with label 3D inference. Show all posts
Thursday, March 6, 2008
Monday, October 15, 2007
Graphical Input Through Machine Recognition of Sketches
Summary:
Herot's short paper gave a brief, but comprehensive, look at sketch interaction systems in the mid 70s.
The paper first looks at a general recognizer, HUNCH, that tries to see if accurate knowledge can be obtained without using a specific domain. The system takes data drawn on a large tablet with a special pencil, and the raw input data is recorded by the computer. The HUNCH system used another application, called STRAIT, that found corners in data by examining the user's pen speed. The system also used a process called latching to snap endpoints of close lines together. Unfortunately, the HUNCH system had problem with consistency between different users. Users drawing at different pen speeds produced different corners, and the latching technique sometime distorted an intended image, such as oversnapping lines in a cube. The system also handles overtraced lines by merging lines together, provides some 3D image inference through unexplained techniques, and can create floor maps by looking at boxed rooms and doorways.
Context is an important part of a sketch, and Herot recognizes this fact by mentioning how data interpretations should have context information. The context should be specified to the computer as to avoid issues of recognizing the domain. Herot briefly mentions a top-down processing for recognizing sketches with a context architecture.
Lastly, Herot mentions that user input is a key component of a sketch recognition system that should not be ignored. More complex interfaces need to be developed so that a user can interact with a program and correct mistakes, and corner finding algorithms need to be tuned for an individual user.
Discussion:
Although none of the topics mentioned in Herot are new to me, the fact that all of these issues were mentioned in a paper written in 1976 is surprising. For instance, I had been under the assumption that using pen speed to detect corners was a relatively new fad.
I also am very surprised that the system tried (and from the one example, succeeded) at incorporating 3D image analysis. I remember reading a paper about using edges and vertices to detect whether an image is 3D, but I cannot seem to recall the author involved, so it's hard for me to construct a timeline for that research.
Herot's short paper gave a brief, but comprehensive, look at sketch interaction systems in the mid 70s.
The paper first looks at a general recognizer, HUNCH, that tries to see if accurate knowledge can be obtained without using a specific domain. The system takes data drawn on a large tablet with a special pencil, and the raw input data is recorded by the computer. The HUNCH system used another application, called STRAIT, that found corners in data by examining the user's pen speed. The system also used a process called latching to snap endpoints of close lines together. Unfortunately, the HUNCH system had problem with consistency between different users. Users drawing at different pen speeds produced different corners, and the latching technique sometime distorted an intended image, such as oversnapping lines in a cube. The system also handles overtraced lines by merging lines together, provides some 3D image inference through unexplained techniques, and can create floor maps by looking at boxed rooms and doorways.
Context is an important part of a sketch, and Herot recognizes this fact by mentioning how data interpretations should have context information. The context should be specified to the computer as to avoid issues of recognizing the domain. Herot briefly mentions a top-down processing for recognizing sketches with a context architecture.
Lastly, Herot mentions that user input is a key component of a sketch recognition system that should not be ignored. More complex interfaces need to be developed so that a user can interact with a program and correct mistakes, and corner finding algorithms need to be tuned for an individual user.
Discussion:
Although none of the topics mentioned in Herot are new to me, the fact that all of these issues were mentioned in a paper written in 1976 is surprising. For instance, I had been under the assumption that using pen speed to detect corners was a relatively new fad.
I also am very surprised that the system tried (and from the one example, succeeded) at incorporating 3D image analysis. I remember reading a paper about using edges and vertices to detect whether an image is 3D, but I cannot seem to recall the author involved, so it's hard for me to construct a timeline for that research.
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