figure 6. Consistency of artists’ lines. (a) five superimposed
drawings by different artists, each in a different color, showing that
artists’ lines tend to lie near each other. (b) a histogram of pairwise
closest distances between pixels for all 48 prompts. approximately
75% of the distances are less than 1 mm.
(b) 0 10 20 30 mm
pixels are within 1mm of a drawn pixel in all other drawings for that prompt.
3. 2. Do known CG lines describe artists’ lines?
A natural question to ask is how well currently known line-drawing algorithms can describe the human artists’ lines.
In our analysis, we consider the following line-drawing
algorithms: image intensity edges, 2 geometric ridges and
valleys (as defined by Ohtake et al. 17), suggestive contours, 7
and apparent ridges. 13 For the object space methods (ridges
and valleys, suggestive contours, and apparent ridges), we
always include the exterior boundary and interior occluding contours in the generated drawing. For Canny edge
detection, we always include the exterior but not the interior contours, since they are not necessarily image intensity edges.
Quantifying comparisons between drawings. In order to
compare an artist’s drawing and a computer-generated
drawing quantitatively, we use the standard information retrieval statistics of precision and recall (PR). Here, precision
is defined as the fraction of pixels in the CG drawing that
are near any pixel of the human drawing. Recall is defined
as the fraction of pixels in the human drawing that are near
any line of the CG drawing. We define “near” by choosing a
distance threshold—we use 1 mm.
As an example, consider comparing the set of five
human drawings shown in Figure 6a with the lines generated by the apparent ridges algorithm (Figure 7). The output of the apparent ridges algorithm is not only a set of
lines but also a “strength” value at each line point. In general, we expect stronger lines to be more important and
thus more likely to match the artists’ lines. We thus generate a series of binary apparent ridges images, each consisting of all points with strength above a given threshold.
The PR of each drawing compared with this set of images
is shown as a dotted pink line in Figure 7. As the strength
figure 7. Precision and recall example. Left: apparent ridges are
compared with five artist drawings. solid line (highlighted) is the
average PR for the set of drawings. Black dots indicate occluding
contours only. Right: an example drawing (black) with apparent
ridges overlayed (red, widened by 1 mm on each side). the PR for this
example is circled (80% P, 88% R).
0.2 0.8 1
threshold is lowered more lines are produced, typically
causing recall to increase and precision to go down, yielding a sloping line in the PR graph. For completeness, we
allow the PR plot to extend to P = 1.0, R = 0.0 (defined as
a blank image) and directly downward to P = 0.0 from the
highest recall obtained by the algorithm. Since each PR
curve is defined for P = [0, 1], we can compute an average
curve by combining points along lines of fixed precision.
The PR values for occluding contours alone are plotted as
black dots and are not averaged.
comparing cG lines in combination. In order to combine
line definitions fairly, we use computer-generated drawings
with a fixed 80% precision. We then classify each pixel in each
human drawing by the nearby CG lines. Pixels that lie near
a single-line definition are considered to be explained only
by that definition, while pixels that lie near multiple definitions are considered explained by all the nearby definitions.
To visualize the results, we create bar charts that partition the lines into object space definitions (blue), image
intensity edges (green), or both (brown). Looking at the
results in Figure 8a, we find that the large majority of lines
are described by both image intensity edges (Canny edges)
and an object space definition. Of the remainder, slightly
more lines are explained by the combined object space
approaches than by image edges alone.
Lines that are explained only by image edges account for
at most 5% of all classified lines at 80% precision. We can also
break down the human lines by intuitive categories, such as
exterior and interior occluding contours and everything else
(Figure 8b). Across all model groups, exterior contours alone
account for between 35% and 50% of all classified pixels.
Interior occluding contours account for between 10% and
20% of all classified pixels, while all other definitions make
can cG lines characterize artists’ tendencies? Given a way
of describing an artist’s drawing in terms of CG line types,
it is possible to investigate whether those descriptions can
characterize the similarities and difference between artists’
styles or tendencies. For example, it may be possible to characterize whether certain artists tend to draw certain geometric features (e.g., ridges) more than other artists do. In such
cases, the CG line definitions provide a vocabulary to discuss