Effect of visual cues on line drawing performance
Tetsuyou Watanabe
School of Mechanical Engineering
Kanazawa University
Kanazawa, Japane-mail: te-watanabe@ieee.org
Abstract—This paper investigates how visual cues affect humanperformance at a line drawing task. Line drawing is a primitiveand fundamental task using tools such as pens, (soldering) irons,
and cutters. Many situations (for example, home, office, studio,
and workshop) require drawing lines with tools. Assistive tools
for improving performance might be especially useful forunskilled people. As a first step, we focus on visual cues asassistive tools and determine the kinds of visual cues that are
effective for good performance. We asked subjects to draw a line
on a displayed dotted line while the spacing and shape of the dots
is changed, and we then analyzed the data. The results indicatethat the cue that should be displayed for good performance
should change according to subjects’ performance: for good
performers the cue whose geometrical center is easy to detect,and for not-good performers the cues whose occupied area islarge. We also discussed the possibility of controlling
performance in line drawing, and presented a strategy fordisplaying visual cues for good line drawing performance.
Keywords-component; line drawing; performance control;
visual cue;I.

INTRODUCTION

A high level of quality in (hand-made) products andoperations requires highly developed skill. In the case ofdyeing a kimono, artists color a drawing on cloth with brushes.
This task requires precision in the position and force control ofthe brushes. Medical operations involving surgical cutting or
burning of organs with such instruments as scalpels also
require precise position and force control of the instruments inorder to avoid injuring normal tissues around the affected area.
Only a limited number of highly skilled experts can perform
such tasks, and only after long training. Even when training isprovided, the number of people who finally succeed inmastering the skills is small, so only a limited number ofpeople can avail of complex and difficult surgical operations
performed by experts. In the community of Japanese traditional
crafts, the aging of crafters has become a serious problem, andpreserving the technical tradition is a very important issue. One
solution might be to increase the number of candidates who can
become highly skilled.
Developing a skill support system would enable many
people to have complex surgical operations, as well as enabling
people who would like training to have a chance to become
experts even without much physical dexterity. Even retired
people would have the opportunity. The final goal of theresearch is to develop a system that augments peoples’ skills so
978-1-4799-0652-9/13/$31.00 ©2013 IEEE

that even unskilled people can perform at the level of skilled
people. As a first step, this paper focuses on the evaluation ofhuman performance.
In recent years, surgical operations with robots havebecome popular [1-3]. One of the most popular surgical robotic
systems is the da Vinci telemanipulation system (Intuitive
Surgical, Mountain View, CA). In telerobotic master–slave
systems, when the operator manipulates the master manipulator,
the manipulation information is transferred to the slave
manipulator, which contacts the real world. During the transfer,
we can remove unnecessary information, such as a surgeon’s
tremor. This effectively helps surgeons to operate safely. In
bilateral control systems, where force or position information at
the slave manipulator is also transferred to the mastermanipulator, we can add a (virtual) motion constraint at themaster manipulator to support operators so that the operators
can manipulate operations precisely [4]. Such systems can besaid to support the skill of the operators, but they are support
systems for indirect manipulation by robots. The aim of thispaper is direct manipulation using tools without robots acting
as intermediates between the operators and tools. Examples ofsystems that directly support the tool manipulation skill of theoperators are ICanDraw [5] and ShadowDraw [6]. ICanDraw
provides instructional procedures and corrective feedback sothat an operator can draw a reference human face accurately.
ShadowDraw assists the operator in drawing by shadow. If an
operator draws, the system infers what the operator is drawing
and provides a suggestive shadow of the inferred image. The
provided image is a blend of relevant images from a database.
These systems work effectively, but the reasons for theireffectiveness are unclear. Various analyses from different
viewpoints might be needed for understanding humanperformance and constructing more effective systems.
Focusing on sketching, several researchers have analyzed how
humans recognize an image. Koenderink et al. [7] studied cues
for recognition of shapes in pictures. Phillips et al. [8] studied
how people identify landmarks on object surfaces. Isenberg etal. [9] compared drawings by humans and computers. Cole etal. [10] analyzed lines drawn by artists and compared themwith lines derived by line drawing algorithms in computer
graphics. They showed that computer-generated lines candescribe lines drawn by artists with high accuracy. Eitz et al.
[11] studied how humans and computers recognize thecategory of sketches drawn by non-experts, and developed asketch recognition system. However, several problems still
remain open.

One of those problems is the effect of visual cues. Whichkind of displayed visual information affects humanperformance and how is unclear. This paper focuses on thisissue. A complex task can be regarded as a combination of
simple primitive tasks. One of the most primitive tasks is
drawing a line. Line drawings are used not only in sketching
and coloring but also in soldering, cutting, grinding, burning(with scalpels), and so on. Therefore, it is worthwhile toevaluate human performance in line drawing, as we do here.
We use a dotted line as a cue (visual displayed information).
By displaying dotted lines with varying shapes and dot
spacings, this paper determines the kinds of information that
improve performance and the kinds of information that degrade
performance.

nominal lines. In total there are six types of visual cues. The
dotted lines (visual cue) were displayed at 0° to the horizon forobserving the performance of fundamental operations.

The paper is organized as follows. In section II, we describe
the methodology for the experiment, with the results shown insection III. We discuss the results in section IV.
METHODOLOGY

A. Subjects
Ten Japanese volunteers (men and women, mean age
21.5 ± 1.6 years (from 19 to 23 years)) participated in theexperiment. They were all right-handed, and none of them had
difficulty with their hands, vision, or haptics. In order toremove any effects of cultural difference, only Japaneseparticipated. These experiments were approved by the Medical
Ethics Committee of Kanazawa University.

Fig. 1

Schematic view of the experimental setup

10pixelDoublespace

10pixelII.

400pixel

circle10pixel

10pixelsquaretriangle

10pixelB. Apparatus
Fig. 1 shows a schematic view of the experimental setup.
The operator draws lines or curves using a pen tablet (Wacom
Intuos5). The drawings are displayed on the monitor (HPZR2040w), along with visual cues for helping the operators.
The position and force of the pen can be measured through thetablet system.

10pixel(a) nominal
Fig. 2

C. Stimuli
Fig. 2 shows the visual information displayed to assist
operators. The subjects were asked to draw a line along thedotted line. Because human arms are constructed from revolutejoints and links, it is reasonable to expect that drawing straightlines will be more difficult than drawing curves. The first stepin Japanese textbooks for calligraphy is drawing straight lines.
For these reasons, drawing not a curve but a straight line wasselected. Methods such as connecting points and drawing lines
along dotted lines (which constructs letters) are popularly usedin Japan when teaching letters to children. In this regard, we
evaluated dotted lines as a means of assisting operators. Bychanging the shapes and spacings of the dots, we examined theeffects of different visual cues on performance. Circles,
rectangles, and triangles were chosen as the shapes of the dotsin order to observe the effects of isotropy, occupied area, andgeometrical center position of the shapes. We prepared two
kinds of spacing between dots to determine the effect of
spacing. The spacing for nominal lines was determined suchthat the space is almost the same as the width of one dot. The
spacing of wider lines is two times the space of that of the

TABLE I.
Symbol(b) wider

Displayed visual cues for helping operators
DEFINITION OF SYMBOLS EXPRESSING VISUAL CUES
Visual cue

●●●
■■■
▲▲▲
● ●
■ ■
▲ ▲

D. Procedure
First, to let the subjects familiarize themselves with thesystem so that they could draw anything normally, every
subject was asked to draw freely. This preliminary experiment
was continued until the subject reported being comfortablyfamiliar with the system. After the preliminary experiment,
every subject was asked to draw a line along the displayed
dotted line from left to right so that the drawn line could be as
close as possible to the dotted line. The presented dotted lines
are the six shown in Fig. 2 and TABLE I. We designated thetrials for the six kinds of visual cues as one trial cycle. Everysubject conducted one trial cycle four times. As the first cycle,
we displayed the visual cues in the order ●●●, ● ●, ■■■, ■ ■,▲▲▲, ▲ ▲, and subjects drew lines along the cues. Afterthe first trial cycle, the second trial cycle was conducted in thereverse order of that of the first trial. The third trial cycle wasconducted after the second trial cycle in the same order as thatof the first cycle. The order at the fourth cycle (after the third
cycle) was the same as that of the second cycle. In this way, we
took the effect of learning and practice (getting used to drawing
lines) into account. It is possible to feed back the magnitude offorces by the thickness of the drawn lines, but this paper
focuses on errors in positioning, so the thickness of the drawnlines was set to be constant such that only the position error
could be fed back.

Connect dots

drawn lines, we considered the following three evaluation
criteria.
The distance between the start point of the drawn
line and the extreme left point of the reference line(|prs – pds|): The positioning performance of placing
the pen on the desired point from free space can beobserved from this.

2.

The distance between the drawn and referencelines: How close to the reference/desired line theoperator can draw the line can be determined from this.

3.

The distance between the end point of the drawn
line and the extreme right point of the referenceline (|pre – pde|): This can reveal the performance of
inclining the pen as closely as possible to the desired
point.

Let pr = [prx, pry]T and pd = [pdx, pdy]T be the vectors
expressing the points on the reference and drawn lines,
respectively. We denote pry (pdy) at x = prx (pdx) by pry(x) (pdy(x)).
In regard to the second criterion, we calculated the difference
between the reference and drawn lines along the x (horizontal)
axis from max(prsx, pdsx) to min(prex, pdex) (see Fig. 4 ). Because
the unit of x is pixels, we calculated the difference at each pixel.
Fig. 31.

|pry (x) – pdy(x)| x ∈ max(prsx, pdsx), min(prex, pdex) Reference line

Then, the mean difference is expressed byCenter line

max(prsx , pdsx )min(prex , pdex )Reference line

Drawn linemin( pre x , pde x ) | p ( x) - p ( x) | ryx  max( prs x , pds x )



ry

and the standard deviation is expressed by
1

y p
rsx
prex

pdsx

difference

pdex

x

Fig. 4dm Comparison of the difference between the center lines for thereference and drawn lines2
 min( pre x , pde x )(| pry ( x) - pry ( x) | -d m ) 2   x max( prs x , pds x )d std  

min(
p
,
p
)
max(
p
,
p
)
1
re x
de x
rs x
ds x







The line drawn by Sub. 3 when the displayed visual cue was ● ●E. Data analysis
The reference line was the line constructed by connecting
the displayed dots (Fig. 3 ). We compared the reference linewith the drawn line and observed the performance of
positioning. Because the thickness of the lines has no
significance, we compared the difference between thecenterlines of the reference and drawn lines (Fig. 4 ).
As shown in Fig. 4 , we defined x- and y-axes. Let prs andpre be the position vectors of the extreme left and right (startand end) on the reference line, respectively. Let pds and pde be
the position vectors of the start and end points on the drawn
line. With regard to the differences between the reference and

The line drawn by Sub. 2 when the displayed visual cue was ■■■The line drawn by Sub. 4 when the displayed visual cue was ●●●Fig. 5

Several examples of lines drawn by subjects

Average of difference[pixel]

5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
Sub. 1 Sub. 2 Sub. 3 Sub. 4 Sub. 5 Sub. 6 Sub. 7 Sub. 8 Sub. 9 Sub. 10

Fig. 6

Average difference between the reference and drawn lines(corresponding to the second criterion: dm)

respect to every subject is shown in Fig. 6 . Note that adifference of 1 [pixel] on average means that the total
difference reaches about 400 [pixel], which corresponds to thelength of the reference line. It can be seen that the performancevaried with visual cues, suggesting the possibility ofcontrolling performance with visual cues. Hereafter, we will
discuss the results based on the performance level determinedby the average difference shown in Fig. 6 , since the average
difference is associated with total error for every subject. Fig. 7
and Fig. 8 show the average and standard deviation of thedifference between the reference and drawn lines for every
visual cue and every subject. The order of subjects is the orderof the performance level as determined by the results shown in
Fig. 6 . Arranging the results according to the performancelevel reveals several features. The details will be described in
the next section.
The results for the other evaluation criteria are shown in Fig.
9 and Fig. 10 . These figures respectively show the average
difference between the start points of the reference and drawnlines for every displayed visual cue and every subject, as wellas those for the end points. The order of subjects is the same as
in Fig. 7 and Fig. 8 .
The writing pressure (force) was not a target of evaluation,
but we measured it for reference. Fig. 11 shows the results forthe average pressure for every displayed visual cue with respect
to every subject.

Fig. 7 Average difference between the reference and drawn lines forevery displayed visual cue (corresponding to the second criterion: dm)

Fig. 9 Average difference between the start points of the reference anddrawn lines for every displayed visual cue (corresponding to the first
criterion)

Fig. 8 Standard deviation of the difference between the reference anddrawn lines for every displayed visual cue (corresponding to the second
criterion: dm)

III.

RESULTS

Examples of the lines drawn by subjects are shown in Fig.
5 . These are typical very good, good, and low results. First, we
focus on the results related to the second criterion because it is
the most significant owing to the possibility of controlling theperformance, for example, by changing a cue during drawing
lines. The average difference for all data (all visual cues) with

Fig. 10 Average difference between the end points of the reference anddrawn lines for every displayed visual cue (corresponding to the third
criterion)

Fig. 11 Average writing pressure for every displayed visual cue withrespect to every subjectIV.

DISCUSSIONFirst, we categorize the subjects into four groups by
applying K-means classifier (K = 4) to the average difference
for all data (Fig. 6 ). The obtained group (class) is considered tocorrespond to their performance level (very good, good,
medium, and low). The color of subject in Fig. 7 indicates thecategorized group. The area for the visual cue where theaverage (standard deviation of the) difference for subjects inevery group is the lowest is surrounded by a colored rectangle
in Fig. 7 (Fig. 8 ). Note that a dashed-lined rectangle shows thesecond best performance for subjects with low-level
performance, because the difference between the values for■■■ and ● ● was small.
The visual cue ● ● is most effective for reducing thedifference (obtaining good performance) for subjects with avery good performance level (see the area surrounded by a redrectangle). Taking the good performance at ●●● into account,the cue ● is considered to be very effective for good
performance, because it is easy to find the geometric center forthe cue ●. The reason the space should be wider may be thathaving more information than the subjects required
undermines/decreases their performance.
For subjects with good-level performance, the visual cue●●● was most effective for obtaining good performance (seethe area surrounded by an orange rectangle), perhaps for thesame reason that the cue ● is effective for the very good
performers. However, they might need more information thanthe very good performers, and the results for ●●● were better
than for ● ●. Another feature is low performance for ▲ ▲.
This is true of both very-good-level and good-level performers.
It is not easy to detect the geometrical center for the cue ▲. In
addition, the wider space might worsen the performance.
For subjects with medium-level performance, the visualcue ▲▲▲ was most effective for obtaining good performance(see the area surrounded by a black rectangle) from theviewpoint of average difference. From the viewpoint ofstandard deviation, ■ ■ was the best. The difference of thelowest area appeared for only the medium-level performer. As
can be seen from Fig. 7 , the performances for ●●● and ● ●were low. In particular, the performance for ●●● was unstableas can be seen from Fig. 8 . These are considered to not begood at targeting the geometrical center of the cue and mighttarget not a point for ● but an area for ▲ and ■. The horizontal

side of ▲ could be the base line for targeting, and the subjects
with medium-level performance are considered to target thearea above the base line even though the drawn lines are likely
to be close to the base line, high accuracy is not expected, andthe drawn line tends to be unstable (as can be seen from Fig. 8 ).
In fact, regarding the base line as the reference line, the average
difference from the reference line for the cue ▲ (▲▲▲ and▲ ▲) is smaller than that for the other cues. That▲ has only
one horizontal side might be an additional important factor andfacilitate the medium-level performers in finding the target area,
compared to the case of ■. The performance for ■ ■ can besaid to be the second best for the medium-level performancegroup (in this case, they showed the most stable performance,
as shown in Fig. 8 ). In this case, ■■■ was considered to bemuch more information than the subjects required. The cue ■occupies more space than the other cues, and the subjects may
think that the target (desired) area (point) is wider. This might
be the reason that the medium-level performers can show good
performance at ■ ■.
For subjects with a low-level performance, the visual cues■■■ and ● ● were most effective for obtaining good
performance (see the area surrounded by a magenta rectangle).
For ■■■, the target (desired) area (point) is large, i.e., a lot of
information can be displayed, which perhaps contributes togood performance. Non-low performance for ▲ ▲ is anotherinteresting feature, because the subjects with the other levels of
performance showed low performance for ▲ ▲. One reason
for high performance for ● ● and ▲ ▲ might be that the
subjects with low-level performance do not take account of thegeometric center of the cues but of the space occupied by thecues. (The target is not a point but an area.) Another feature is
low performance for ●●●, which might be related to thedifficulty of targeting a point.
The correlation between the average differences for all data
(see Fig. 6 ) and for ●●● (with respect to every subject) was
high, 0.90, and the groups categorized by applying the Kmeans classifier (K = 4) to the results for ●●● were very close
to the categorized group shown in Fig. 7 (only Sub. 7 wascategorized to a different group: good performance level).
Therefore, considering the possibility of performance control,
we could take the following approach:1.

Ask subjects to draw a line along the displayed cue ●●●in order to infer the performance level.

2.

Change the displayed visual cue according to the inferredperformance level. If the performance level is very good,show ● ●. If it is good, show ●●●. If it is medium, show▲▲▲. If it is low, show ■■■.

Next, we considered the positioning of the start points oflines (Fig. 9 ). Comparing Fig. 6 and Fig. 7 with Fig. 9 , thesubjects with very good and good performance levels(determined by the result in Fig. 6 ) show good performance.
However, the performance of subjects with other performancelevels does not always correspond to the performance of thepositioning of the start point. As discussed above, the subjects
with good performance tend to target the geometric center of acue, but the other subjects do not. This might be because theability to position does not always correspond to the ability todraw lines along the desired lines.

When placing the pen on the start point of a line from freespace, the placement is done for a very short time. Then it is
impossible to control the performance to be a good one bychanging the displayed visual cue during the placement.
Therefore, when considering which visual cue to display forgood performance, we should display the cue for which
subjects with any level can show good performance. If
considering the average and maximum difference, it isconsidered that ▲▲▲ should be displayed.
Next, we considered the positioning of the end points oflines (Fig. 10 ). Fig. 10 shows an interesting tendency: theperformance for small spaced visual cues ●●●, ■■■, and▲▲▲ is very good, compared to the performance for large
spaced visual cues ● ●, ■ ■, and ▲ ▲. The influence of ashape difference of cues was not obvious. This suggests that
small-spaced visual cues are effective for stopping to drawlines precisely. When comparing the results of average
difference and selecting one cue among the three cues, ▲▲▲is considered to be the best.
Last, we evaluate the writing pressure. Note that the
subjects were not asked to take forces into account, and force
information was not displayed and fed back. Fig. 11 shows
very interesting results; there is no obvious difference withrespect to the different visual cues for every subject. This
indicates that in the absence of force information, subjects caneasily maintain a constant force, perhaps because there is nomental pressure to keep the force constant and control the force
at a desired value. This might be an interesting feature ofhuman beings. In the da Vinci telemanipulation system [2],there is no force feedback. Nonetheless, the system works very
effectively for assisting and supporting the work of surgeons.
In this system, surgeons do not need to take force into accountand can concentrate on controlling the positions and poses ofsurgical instruments. The surgeons’ disregard of force could
unintentionally provide appropriate force control. Validating
the details is left to future work.
V.

CONCLUSION

We investigated the effect of visually displayed cues fordrawing lines. We conducted experiments in which subjects
were asked to draw a line along a displayed dotted line as close
as possible to the dotted line. The displayed dotted lines served
as visual cues for drawing. We investigated the effect ofchanges in the shape (circle, rectangle, and triangle) and space(narrow and wide) of the dotted line. We evaluated the resultsfrom three different viewpoints: positioning the start point of aline, the difference between the reference and drawn lines, andpositioning the end point of a line. We categorized the subjects
into four groups (very good, good, medium, and low),according to results about the difference between the reference
and drawn lines, and we analyzed the results. The results
suggested that the kind of visual cue effective for good
performance varies with the performance level, and visual cue
can be used as a control variable for controlling theperformance. The cue whose geometrical center is easy todetect should be displayed for very-good and good performers
while the cues whose occupied area is large should be
displayed for medium and low performers. We summarize ourstudy by constructing a method of displaying visual cues forgood performance:
1.
2.
3.

4.

Display ▲▲▲ for good positioning of the start point.
After drawing starts, display ●●● for inferring theperformance level.
Change the displayed visual cue according to theperformance level. If the performance level is very good,show ● ●. If it is good, show ●●●. If it is medium, show▲▲▲. If it is low, show ■■■.
Around the end point of a line, display ▲▲▲.

Future work will involve 1) increasing the sample size andenhancing the validity of the results, 2) constructing a systemthat automatically infers users’ performance levels and displays
appropriate visual cues, and 3) investigating performance atother line and curve drawing tasks.
ACKNOWLEDGMENT
The authors would like to thank Mr. Shuji Nagasaki formaking experimental setup and conducting experiments. This
work was supported by JSPS KAKENHI Grant Number
24650092 and The Telecommunications Advancement
Foundation.
REFERENCES[1]

C. Gutt, T. Oniu, A. Mehrabi, A. Kashfi, P. Schemmer, and M. Büchler,“Robot-assisted abdominal surgery,” British journal of surgery, vol. 91,
no. 11, pp. 1390–1397, 2004.
[2] S. Jacobs and V. Fal, “Pearls and pitfalls: Lessons learned in endoscopic
robotic surgery the da vinci experience,” The Heart Surgery Forum,
vol. 4, no. 4, pp. 307–310, 2001.
[3] D. D. Thiel and H. N. Winfield, “Robotics in urology: past, present, andfuture,” Journal of Endourology, vol. 22, no. 4, pp. 825–830, 2008.
[4] Y. Matsumoto, S. Katsura, and K. Ohnishi, “Dexterous manipulation inconstrained bilateral teleoperation using controlled supporting point,”
Industrial Electronics, IEEE Transactions on, vol. 54, no. 2, pp. 1113–
1121, 2007.
[5] D. Dixon, M. Prasad, and T. Hammond, “icandraw: using sketch
recognition and corrective feedback to assist a user in drawing humanfaces,” in Proceedings of the 28th international conference on Human
factors in computing systems. ACM, 2010, pp. 897–906.
[6] Y. J. Lee, C. L. Zitnick, and M. F. Cohen, “Shadowdraw: real-time user
guidance for freehand drawing,” in ACM Transactions on Graphics(TOG), vol. 30, no. 4. ACM, 2011, p. 27.
[7] J. J. Koenderink, A. J. van Doorn, C. Christou, and J. S. Lappin, “Shapeconstancy in pictorial relief,” in Object Representation in Computer
Vision II. Springer, 1996, pp. 149–164.
[8] F. Phillips, J. T. Todd, J. J. Koenderink, and A. M. Kappers, “Perceptual
representation of visible surfaces,” Perception & psychophysics, vol. 65,
no. 5, pp. 747–762, 2003.
[9] T. Isenberg, P. Neumann, S. Carpendale, M. C. Sousa, and J. A. Jorge,“Non-photorealistic rendering in context: an observational study,” inProceedings of the 4th international symposium on Non-photorealistic
animation and rendering. ACM, 2006, pp. 115–126.
[10] F. Cole, A. Golovinskiy, A. Limpaecher, H. S. Barros, A. Finkelstein,
T. Funkhouser, and S. Rusinkiewicz, “Where do people draw lines? ”
ACM Transactions on Graphics (Proc. SIGGRAPH), vol. 27, no. 3, 2008.
[11] B. M. Asl, S. K. Setarehdan, and M. Mohebbi, “ Support vector
machine-based arrhythmia classification using reduced features of heart
rate variability signal.” Artif Intell Med, vol. 44, no. 1, pp. 51–64, 2008.