Digital Artifacts of Daniel

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The way to assess someone’s abilities with the arts is not through a multiple-choice test and so on.  Subjects must be invovled in actually artistic creation (painting, composing, etc.), more specifically, actual projects that expose them to their artistic abilities and allow them to reflect on them, then they are assessed.  The idea is that since many have not be exposed to art, they must first be allowed to play in the space for a little while before being given a problem to solve that determines their ability.

Aspects of the assessment: (1) production, (2) perception, (3) reflection.  (1): Students must be drawing, as this is central.  Not merely copying, but also works of their own composition.  (2 and 3): The student must learn to perceive, or see, better to make “finer discriminations”, and then step back from the latter two states and say, “What am I doing? How can I make it better? Am I successful?”

Gave example of instructing the student to create alterations on an existing piece: Take “Twinkle, twinkle” and sing it happy, sad, 3/4 time, and so on.  What validity is there to encouraging derivatives of a work and how do I grade it?

Another example: Showing derivatives of the Mona Lisa and letting the child find the differences, entering a state of reflection. Also has them copy signatures or other people’s work (perception->reflection->production).  Another activity is having them build a portfolio that serves as a databse of their progress.

Discussed his theory on multiple intelligences. Good read.

Summary/Discussion:

Allows a user to draw on a picture using different stroke tools and then combines that input with image analysis of the picture to produce a tonal drawing in real-time.  Most work presented is on rendering techniques and mimicing pencil lead and smuging.  More of a light and shadows thing than perspective, edges, and spaces since the use is just tracing.

Comments Made Elsewhere:

1. Andrew’s Blog

Summary:

Very short paper. Using speed and curvature, go through and find every possible corner (unless too close together, and also move the one with the smallest curvature).� Next, assuming that the corners of small stoke segments are false positives, attempt to merge them with their neighbors, only doing so if the error is less.

Discussion:

Considering my discussion in the last blog post, here’s a solution for corner finding that doesn’t appear to require resampling.� It looks to be a very fast solution, too.� Seems like they could have implemented Kim&Kim’s local convexity as another heurisic of the initial fit.

And there are still lots of glitches, like all of my categories disappeared and some posts have odd characters in them now.

Comments Made Elsewhere:

1. Manoj’s Blog

Summary:

Paper summaries research on discovering the “region of support” (number of neighbors) to look at when determining curvature on the on-the-fly and only using angle space.

Things to note: resampling will undoubtedly cause smoothing of the input curve, so determining a good size for the k-neighbors is necessary.

As a first step, they initially define the curvature as the direction change at each point with k = 1 (only one neighbor). Next, they define “local convexity” where all neighboring points with the same direction (sign) as point(i) are used as the region of support.� Works well for some situations, but came make features indistinguishable (where to segment?).� Added “local monotonicity” where you only observe the neighboring subsets on the left and right as long as they are monotonically decreasing (meaning keep decreasing).� Next, find the local maximum or minimum (depending on if curve is positive or negative) to find the segmentation point.

Performed evaluation test with volunteers and then tested their algorithm in parts (direction only, with local convexity, only, etc.) and then against two other accepted algorithms.

Discussion:

Again, another paper that doesn’t make use of time so their implementation is independent of input medium (tablet PC or actual paper) and the direction of the scanning (can start from the front or back). They do use speed for calculating the threshold to use when determining the segmentation points.

I was actually thinking up a solution similar to theirs (but theirs is obviously better).� I did not realize the issue of determining the optimal number (k) of nearest numbers to use, but am supportive of using a sliding window at drawing time for calculating the segmentation points there and then.

I like that they do use their segmentation algorithm for going to the next step of defining features for shape recognition on page 5.� This algorithm appears to be capable of complex line segmentation.

How could this be done without resampling?� Is it possible in determining curve segmentation?� I want to develop a vector-based approach that incorporates speed and doesn’t require resampling.