Reading #7. Sketch Based Interfaces: Early Processing for Sketch Understanding

Comments on Others:

Chris Aikens

Summary
           The paper discusses a novel approach of developing an interface which feels as natural as a paper but smart enough to understand the user’s intension and identifying sketches to a geometric shape. More specifically the work is intended to provide natural sketching and have the sketches understood. Also the authors state their intension of this work by giving user a system with unrestricted capabilities, such as drawing a rectangle clock-wise or counter clock-wise or drawing it with multiple strokes and still which is able to identify by the computer, like how people perceive things in the real world, the geometric shape rather how it was drawn.  Also note that the authors specified their domain as mechanical engineering design which dives added difficulty and complexity into sketching in this domain. Therefore the paper states that preprocessing steps like finding corners, fitting both lines and curves particularly important.
           The system design consists of approximation, beautification and basic recognition in the preprocessing stage. According to the paper, approximation fits pixels to lines and curves, Beautification modifies the output from approximation to make it visually more appealing, and finally Basic Recognition do the interpretation of the strokes, like sequence of 4 lines as a rectangle or a square. Subsequent recognition into complex structures handles by some other system in the design. The approximation consist of vertex detection, which is based on Average based filtering to find extrema corresponding to vertices while avoiding those due to noise. 

Discussion
           This paper particular grabs my interests due to its nature of regarding sketches as graphic objects and doing preprocessing before recognition to break them into basic objects (lines, curves etc). I consider this technique a sort of best way of tackling sketch recognition problems (just my personal opinion).
            The paper discusses vertex detection by the average based filtering which is on curvature graph and speed graph thresholds. I’m just wondering is there any other sophisticated ways to find the vertexes than this particular technique? In my experience, examining pixel distribution (neighboring pixels) is a hard problem; I’m interested in knowing something simple (Dr. Hammond mentioned tools to do this, am I missing something here?). Also I’m skeptical about using just the mean of the curvature data or speed data, a single higher order noise signal may ruin the whole detection process.   Also the Beautification is based on adjusting the slopes, but may be thickening the line and then skeltoning it would be a better choice, because slope adjustments may weaken the intended stroke appearance.
              In my personal opinion, the paper is lacking the required recognition design as a description which makes it more abstract to readers when it comes to the so called “higher level recognizer”.     

Find the paper here.