Highlighting Techniques for Real Entities in Augmented Reality
Sebastian Fuchs, Mario Sigel and Ralf D
Hochschule RheinMain, Wiesbaden, Germany
Highlighting, Augmented Reality, Particle Systems, AR User Interfaces.
One fundamental task in Augmented Reality is to highlight specific real world entities or small areas in the
user‘s view. For this purpose, a total of 9 novel highlighting techniques were designed and implemented.
For comparison, they are complemented by two conventional techniques often used in practice. This paper
presents the techniques and evaluates them based on a user study in which 23 participants were asked questions
concerning the degree of attention as well as disturbances caused by the highlighting techniques. The user
study revealed that particle systems are a successful tool to effectively draw attention to a highlighted entity
without causing annoying distractions.
In Augmented Reality (AR), it is a fundamental task
to highlight an entity in the user’s environment. For
instance, highlighting can help a user to follow as-
sembly instructions, highlighting can draw the user’s
attention to a specific object in a learning application,
or highlighting can be used to reference entities in
a collaborative setting. In principle, there are two
different kinds of entities that can be highlighted in
AR: virtual entities and real entities. Usually, virtual
entities are easier to highlight as the AR system has
not only the information available about their position
and orientation, their geometry, and their appearance
but can also manipulate them quite effortlessly
(Milliron et al., 2002). Highlighting real entities
is more challenging and the paper focuses on this task.
In practice, highlighting by changing the color
of an entity or drawing a frame around it are
commonplace; examples can be found in (M
et al., 2012) and (Li et al., 2013). While these
highlighting techniques are simple to implement,
they have several drawbacks. First, a frame might
occlude important information. Second, changing
the appearance of an object might result in a loss of
information. Third, some highlighting techniques
might only be applicable to virtual objects resulting
in inconsistencies as virtual and real objects are
highlighted differently. For example, it might not be
feasible to change the color of a real entity as con-
vincingly as the color of a virtual entity. Moreover,
it is desirable to have a larger variety of highlighting
techniques available. This expands the design space
and increases the chances that a designer can find a
more suitable highlighting technique for a specific
use case. For instance, it might be beneficial to use
different highlighting techniques for highlighting
humans or avatars as opposed to objects. Different
highlighting techniques can also be used to group
entities or to express different levels of urgency
or importance. This is our motivation to conceive
additional highlighting methods. The advances in
hardware (e.g., increasing graphics processing power
even on handheld devices used for AR or availability
of high-resolution cameras and depth sensors) as well
as in computer vision algorithms (e.g., for object
segmentation and finding contours of real entities in
a video image) provide a larger basis for exploring
novel highlighting techniques.
The main contribution of this paper is the pre-
sentation of nine novel techniques for highlighting
real entities in AR and the results of their evaluation.
These methods do not only use color or enclosure
as visual variables to cater to the user’s pre-attentive
perception (Ware, 2004). They rely on the use of
particle systems (Reeves, 1983), distortion, and the
segmentation of the object’s contour. Comparing
them to traditional techniques, a user study has
been conducted with the highlighting techniques
introduced in this paper. Criteria for the evaluation
are the degree of the user’s attention that is achievable
as well as the extent of possible distractions that are
Fuchs, S., Sigel, M. and Dörner, R.
Highlighting Techniques for Real Entities in Augmented Reality.
DOI: 10.5220/0005674002570268
In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 1: GRAPP, pages 259-270
ISBN: 978-989-758-175-5
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
caused by the highlighting techniques and can be
perceived annoying by the users.
The paper is organized as follows. In section
2, we present related approaches regarding AR and
highlighting. Section 3 describes the basic scenario
including hardware set-up and software components
used to implement and test several highlighting tech-
niques. After that, in section 4, the highlighting tech-
niques conceived are presented. A user study, that
we conducted, including an evaluation is presented in
section 5. Finally, a conclusion (section 6) is given to
summarize the contribution of this paper.
Different research concerning recognition and mark-
ing of small areas or entities in the user’s physical
environment has been conducted in recent years.
Guiding techniques are used to navigate the user
towards the vicinity of the target. Both Buchmann
et al. (Buchmann et al., 2008) and Li et al. (Li
et al., 2014) tested and compared different directional
AR-interfaces based on projected marks, e.g., an
arrow or a compass, in the user’s view. While
Buchmann et al. and Li et al. conducted their tests
in controlled indoor environments, Schwerdtfeger
et al. (Schwerdtfeger et al., 2009) developed an
application for the logistics system of a warehouse.
The user, wearing an HMD, is guided to the targeted
product by an overlaid curved tunnel. A square frame
at the end of the tunnel highlights the label under
the location of the product. One disadvantage of
the described approaches so far may be that further
elements were added to the user’s view that possibly
cause a distractive effect on the user’s perception of
the environment.
Instead of guiding the user to a specific location,
the targeted entity can be highlighted itself. For
instance, applications are conceivable that support
users when searching for a specific item in a shelf.
Li et al. (Li et al., 2013) developed an assistant tool
for a supermarket scenario. Thereby, the user’s visual
search processes in front of a shelf is enhanced by
highlighting targeted products when seen through the
display of a tablet device. Red circles and spotlights
(dark overlay in the surrounding of the product) were
employed as highlighting techniques. A similar ap-
proach was pursued by L
ochtefeld et al. (L
et al., 2010) who used a mobile camera projector to
highlight products in a supermarket shelf that fit the
user’s preferences. In their work, Li et al. as well as
ochtefeld et al. focus on highlighting as a tool for
guidance in a special scenario. For highlighting, the
authors chose commonplace techniques (circles and
spotlights). The question arises if it is possible to
improve the user’s guiding process by choosing more
advanced highlighting techniques. This is one key
issue to be discussed in our work.
Most related work is particularly suitable to
highlight virtual entities. Kim and Varshney (Kim
and Varshney, 2008) created a new highlighting tech-
nique altering virtual models to direct the observer’s
attention to a targeted point. By altering the geometry
of an entity beside the targeted region, attention can
be attracted. Though it is an interesting approach for
highlighting, the method is only applicable to virtual
entities based on the fact that a 3D mesh is necessary
to alter their shapes.
The research group Veas et al. (Veas et al., 2011)
focused on research in the field of directing attention
without distracting the observer. Furthermore, one
objective of the research was to create a saliency
technique that can be modulated in the rate of user’s
attention. For every frame of a video, the researchers
changed the contrast, lightness, and color of different
areas of the frame in a different way to have the
highest attention salience inside a designated focus
region. Using this technique for highlighting, it is
possible to control the degree of the user’s attention
precisely. One drawback may be that it is necessary
to alter most parts of the image presenting the user’s
view which can lead to a loss of quality in the user’s
perception of the environment.
The approaches described above either deal with
highlighting entities in a specific use case or with
the development of a single novel highlighting tech-
nique. We did not find any research dealing with the
comparison of different highlighting techniques for
real entities. Gacem et al. created a design space
for guidance techniques which, besides highlighting
techniques, also contains tactile and auditory guid-
ance techniques (Gacem et al., 2014). The approaches
presented in this section used techniques based on
static visualizations. Highlighting techniques that are
partially based on movement, such as particle flow,
have not been explored in depth yet. Here, the addi-
tion of a movement to the highlighting effect makes
the entity itself as well as the background temporar-
ily visible that might be a step towards providing a
solution to the problem of occlusion.
GRAPP 2016 - International Conference on Computer Graphics Theory and Applications
This section describes the basic requirements for our
highlighting techniques that are presented in the next
section. Moreover, we give an example how we met
the requirements by giving some realization details
on the prototype implementation of the highlighting
techniques that was also used in the evaluation. In
principle, AR applications use sensor data to align
virtual objects in the real environment. This data
will be also used for highlighting purposes. Our
techniques require a video based sensor. Ideally, the
video data is complemented by the data from a depth
sensor. The data collected from the real environment
needs to be suitable to estimate the contour of a
targeted entity. Therefore, sensor data, such as the
video stream of a camera, needs to be analyzed.
Consequently, the area of a video stream, which
belongs to a designated entity, has to be identified.
For this, a segmentation algorithm that finds those
pixels in a single frame of the video stream is required
that contains the contour of the designated entity.
The segmentation algorithm should provide the exact
contour of an entity as a homogenous area. Moreover,
the runtime of the algorithm should meet real-time
constraints. A depth camera can be used as it already
is able to identify 3D-entities as comparatively
homogenous areas. Once the contour is determined,
graphics libraries such as OpenGL can be utilized
to implement the proposed highlighting effects.
Concerning the hardware set-up, one can distinguish
between video-see-through or direct-see-through AR
systems (T
onnis, 2010). Not all highlighting tech-
niques proposed are suitable for direct-see-through
as image of the real object is distorted. This is not a
problem when using video-see-through AR systems.
In our user study, we employ video-see-through
using a notebook screen (with a screen size of 15.6”)
to display the highlighted entities. A specific num-
ber of arbitrary entities is placed in front of a Mi-
crosoft Kinect-V1-sensor which provides a stream of
depth images of the scene to a notebook. In order
to get an RGB-video-stream with a resolution higher
than the images delivered by the camera built in the
Kinect, a HD-webcam was assembled on the Kinect.
When conducting user studys, the coordinates of the
targeted entities are determined by the administrators
in an initial cycle of the experimental set-up. Af-
ter that, the determined coordinates are delivered to a
pipeline for highlighting a designated entity. Thereby,
the depth image stream of the Kinect-sensor is ana-
lyzed, the contour of the entity is determined, and the
entity is highlighted in the user’s view. Segmentation
methods mostly are based on two principles (Adams
and Bischof, 1994). The first principle contains meth-
ods that use edge detection, whereas the second one
summarizes areas with similar features. We have im-
plemented a segmentation algorithm that combines
both principles. Moreover, for real-time usage we
accelerated the segmentation process by limiting the
segmentation to one entity instead of the segmenta-
tion of the whole image. Figure 1 shows an example
of the depth image stream of the Kinect-sensor (1a)
and the result of the segmentation process (1b).
(a) (b)
Figure 1: The contour (b) is determined using the depth
image stream (a).
Based on the work of Mishra et al. (Mishra et al.,
2009), we use the focal point of an entity to segment
the image and estimate its contour around the focal
point. The transformation into polar coordinates en-
ables the fast estimation of the contour based on the
fact that line-by-line iteration can be used to analyze
the depth image.
A total of 9 novel highlighting techniques were
designed and implemented, with 4 of them using
particle systems to highlight a certain entity. Particle
systems are widely used, e.g., for visualizations of
surfaces (Su and Hart, 2005) and phenomena like
clouds, smoke, and fire (Reeves, 1983). Additionally,
for comparison purposes, two more conventional
techniques already described in related literature
have been considered in the following. Although
we are focusing on video-see-through, some of the
techniques are suitable for direct-see-through as
well. Table 1 shows an overview of all highlighting
techniques where each of them is classified concern-
ing the use of particle systems, the suitability for
direct-see-through, and if one technique is considered
In the following, each of the highlighting tech-
niques, that are shown in table 1, is presented in a
separate subsection.
Highlighting Techniques for Real Entities in Augmented Reality
Table 1: Overview of all designed highlighting techniques
assigned to a category. Part-Sys is an abbreviation for us-
ing particle systems, Direct-ST is an abbreviation for suit-
able for direct-see-through, and Conv. is an abbreviation for
conventional technique.
Name Part-Sys Direct-ST Conv.
Red Frame
Blinking Aura
Black Aura
Red Overlay
Color Contrast
4.1 Fire
The idea of the highlighting technique described in
this section we call it Fire is to place upwelling
particles along the contour of the entity to be high-
lighted. These particles should be designed in a
way that the highlighted entity looks like it burns at
its contour. Therefore, the particles should have a
fire-typical color (with a color range from red to light
orange) and change their color as well as their size
over time as observable in real fire (see Figure 5a).
At each edge point of the contour of the high-
lighted entity and at specified intervals between the
edges, a particle system is placed which contains a
certain number of particles at one point of time. For
our user study, we experimented with different num-
bers of particles and choose the value of 50. A parti-
cle that is just emitted from a point on the contour is
given a darker color at the beginning than at the end of
its lifetime. As it moves upwards, its color is getting
brighter. Furthermore, its size is getting smaller. Af-
ter a certain time, each particle disappears and a new
one is emitted in its place.
4.2 Emitter
An entity highlighted with the technique described in
this section should look like it sends out particles in
every direction creating the effect of an emitter. These
particles have a certain lifetime, a certain color and
are getting smaller over time to announce their end of
lifetime. In our user study, we chose a light blue color
which differentiates the particles of the background
in an indoor environment. A particle system is set
at each point of the contour of the highlighted entity.
The starting point of the particles is moved a few pix-
els inside the shape of an entity so that the emitted
particles overlap the entity at the beginning of their
lifetime. Figure 2a shows an illustration of an entity
(grey shape) highlighted by the Emitter technique.
(a) (b)
Figure 2: Illustration (a) and screenshot (b) of the Emitter
The movement direction of each particle is defined
by a vector between the focal point of the highlighted
entity (red dot in the middle of the entity in Figure 2a)
and one point of its contour. In Figure 2a the move-
ment directions are symbolized as red arrows. The
particles are emitted from the point where a red arrow
starts. The maximum number of particles each sys-
tem contains should be set in way that the highlighted
entity is still visible and the highlighting effect is not
too subtle. In our prototype, we set this value to 20.
4.3 Garlands
The highlighting techniques Fire and Emitter use
particle systems that are set along the contour of the
highlighted entity. Another idea is to place particles
over the shape of an entity and in this way create a
kind of garlands that wrap around the entity. A total
of three particle systems is set along the left part of
the contour (see the green dots in Figure 3a), each
emitting a certain number of particles at one point
of time with no diffusion. The maximum number of
particles a system contains should be set to a value
which on the one hand causes an eye-catching effect
but on the other hand covers only a small part of
the highlighted entity. For the prototype, we chose 20.
The sources of the particle systems have a con-
tinuous distance (d in Figure 3a) between them. The
value for the distance depends on the height of the
entity. The movement direction of the particles (red
arrows) have a certain angle α to the horizontal line
(dashed lines) which we set to a value of α = 10
order to achieve a soft rise of the particles. The life-
time of the particles of one particle system ends when
the particles reach the right part of the contour (blue
GRAPP 2016 - International Conference on Computer Graphics Theory and Applications
(a) (b)
Figure 3: Illustration (a) and screenshot (b) of the Garlands
dots). The size of the the particles changes over time.
After being emitted, one particle‘s size is increasing
until it reaches the middle of its way to the right part
of the contour (middle of its red arrow), then its size
is decreasing again to its end of lifetime. In this way,
the particles look like they follow an orbit around the
highlighted entity.
4.4 Hotplate
With the intention to create a more subtle way to
highlight a certain entity, we designed a highlighting
technique that is applied only to parts of the entity.
The idea of this technique is to place something like
a hotplate at the bottom of the entity. This hotplate
should emit particles that move upwards (y-direction
in our case) and let the entity look like it is flowed
around with blue flames comparably to standing in
burning gas (see Figure 5h).
Other than Fire and Emitter, Hotplate is using par-
ticle systems emitting particles only along the bottom
line of the highlighted entity. The bottom line is de-
fined by a line through two points p
= (x
, y
and p
= (x
, y
, where x
is the minimum x coordinate of the contour, x
maximum x coordinate of the contour, and y
minimum y coordinate of the contour. The value
shifts the bottom line downwards to reduce the
overlap of emitted particles with the bottom part of
the shape of the entity. In our work, we focus on real
entities that can usually be assumed to have a certain
base area to stand on the ground. The width of the
targeted entity defines the bottom line. At each point
of the bottom line a particle system is set containing
a certain number of particles at one point of time. We
set this number to a value of 50 which led to satisfying
results concerning the recognizability. We intended to
create the effect that particles at the edges of the bot-
tom reach a higher position than in the middle. There-
fore, the speed of the particles along the bottom line
is calculated with a parabola (see Equation 1). The
parameter speed
was assigned empirically.
speed(x) = x
· speed
The angular point of the parabola is set at the
middle of the bottom line. The particles emitted by
the particle system that is set at the point (x, y
on the bottom line move with a speed of the
value speed(x). A higher value for speed
in a greater y-dimension for the Hotplate effect be-
cause the lifetime for the particles is set to a constant
value. The emitted particles follow the shape of the
given parabola. Furthermore, the size of each emitted
particle is decreasing over lifetime.
4.5 Red Frame
Compared to the created highlighting techniques so
far, a more conventional approach to highlight an en-
tity is simply enframing it with a colored border. The
technique that we call Red Frame highlights an entity
by surrounding it with a red frame (see Figure 5d).
This frame encloses the outmost parts of the entity’s
4.6 Blinking Aura
One approach to highlight entities is to create the
visual impression of an aura surrounding a targeted
entity (see Figure 5b). For this purpose, a glowing
circle around an entity, that does not overlay the
entity itself, was designed. Moreover, the color of
the aura as well as the intensity changes over time to
increase the highlighting effect.
The color video stream displayed in the user’s
view is altered for a pixel at position p = (x, y)
on the radius r = kp p
f ocus
k, that is the Euclidean
distance to the focal point p
f ocus
of the targeted en-
tity. The aura overlays the image at its pixels in the
area between r
and r
. Thereby, the borders of
the aura overlay the underlying color image with a
low intensity value whereas the intensity of the over-
lay for a pixel is increasing when the radius is close to
, a value between r
and r
. So the param-
eter r
marks the line of points where the over-
lay covers the image completely. For one pixel, the
coefficient c (see Equation 2) represents the ratio be-
tween the color value of the video stream image and
the color of the overlay dependent on the current ra-
dius r.
c(r) =
if r
> r r
if r
> r r
1 else
Highlighting Techniques for Real Entities in Augmented Reality
The quadratic term in Equation 2 creates the ef-
fect of a smoother glow around the highlighted ob-
ject. Based on the value c, for every pixel in an im-
age the resulting RGB-values are computed including
both the aura and the color image as well. Equation 3
shows the calculation of the color values for a pixel at
position p. The resulting RGB-value is a linear com-
bination of the RGB-value in the color image of the
video stream and the color of the overlay.
= c ·
+ (1 c) ·
The dimension of the aura is adapted to fit the size
of the target entity. So the glow is scaled by adapt-
ing the parameter r
, r
, and r
based on the
dimension of the entity. The described highlighting
technique creates an aura that surrounds the targeted
entity in a comparably static way. Due to the fact that
alteration over time is a way to draw someone’s atten-
tion, we expanded the calculation to create a blinking
effect with a periodic change between two colors as
well as the intensity of the aura.
4.7 Black Aura
We created a highlighting technique using a black
aura to help the observer to distinguish between the
targeted entity and the background. Thereby, the en-
tity itself is displayed unmodified. In contrast, the sur-
rounding pixels are overlaid by an aura that decreases
its intensity when the distance to the focal point of
the targeted entity increases (see Figure 5f). While
implementing a similar concept to the one described
in section 4.6, a major difference is that the contour
of the entity is highlighted with this technique. Due
to the fact that the sensor data is not completely ex-
act caused by noise, a distracting movement of the
estimated contour of the entity is visible. Thus, we
decided to use a Gaussian Blur Filter to stabilize the
contour data.
4.8 Red Overlay
This highlighting technique overlays an entity with a
red semi-transparent color. The overlay imitates the
shape of the entity so that it does not transcend its
contour (see Figure 5c). We classified this technique
into the conventional ones. It is based on the tech-
nique presented in (M
oller et al., 2012).
4.9 Color Contrast
According to the Gestalt principle of figure-ground
articulation, one approach for a highlighting tech-
nique is to emphasize the contrast between an entity
and the background. For this purpose, we imple-
mented a technique which alters pixels in the image
that do not belong to the shape of the targeted entity.
More precisely, the background of a targeted entity is
transformed to a gray-scale image while the targeted
entity is put in the foreground by displaying its
shape in color. Figure 5e illustrates the highlighting
Although this technique showed satisfying results
regarding the direction of the observer’s attention to
the targeted entity in early tests, the disadvantage
in highlighting entities, whose shape contains only
marginal color information, is obvious. In case the
R-, G-, and B-values of the pixels inside the shape
of the targeted entity are almost identical for each
pixel, the described highlighting technique results
not in an improvement regarding the direction of the
observer’s attention.
To create a highlighting technique working with
any kinds of colored entities, we ensured a minimum
color contrast between the background and the tar-
geted entity by adding color information to almost
gray entities. For this purpose, the RGB-color infor-
mation is transformed into HSV-values. When the sat-
uration s is going below a minimum threshold s
for a specific pixel, the color information is altered.
Moreover, one goal is to avoid big leaps in the gra-
dient of the color of the targeted entity. So the al-
teration of the color information of a pixel is not im-
plemented as abrupt leap at the threshold. Instead,
the alteration is applied relative to the distance to the
threshold. Equation 4 shows the calculation of the co-
efficient c
. Based on c
, the saturation of a pixel is set
to a linear combination of the actual saturation value
and a maximum value s
(see Equation 5).
s = s
· c
+ s · (1 c
) (5)
The increase of the saturation of a pixel is not suffi-
cient to create a smooth highlighting because of the
fact that small differences in the Hue-value h result
in distracting artifacts of the targeted entity. So an
adaption of the Hue-value is necessary as well. For
this purpose, the average Hue-value h
of the en-
tity’s color is used as shown in Equation 6.
h = h
· c
+ h · (1 c
) (6)
GRAPP 2016 - International Conference on Computer Graphics Theory and Applications
4.10 Distortion
Similar to the highlighting technique described in
section 4.6, the technique described here displays a
circle around a targeted entity. Instead of an overlay,
this technique distorts the image of the entity at pixels
near to the surrounding circle. More precisely, in the
area of the highlighting effect, the surroundings of the
entity are reflected (see Figure 5g). To implement the
Distortion technique, the coefficient c is calculated
as described in Equation 2 based on the parameters
, r
, and r
. Dependent on the value
1 c, the distance between the actual pixel and the
point, whose color information is reflected from, is
calculated. Thus, a higher distortion for a specific
pixel is equal to the reflection of a point in a higher
Equation 7 shows the calculation of the reflected
point for a specific pixel of the image. Thereby,
represents the maximum distance between the
position of an actual pixel and the reflected point.
The distance is weighted by the value 1 c.
f ocus
x x
f ocus
y y
f ocus
· (1 + s) (7)
with s = s
· (1 c)
Because the calculation for the coordinates of
a reflected point in the image results in floating
point numbers, the actual displayed RGB-values are
taken by the four surrounding neighbors of the re-
flected point using bi-linear interpolation to generate
a smooth highlighting effect without artifacts.
4.11 Magnifier
One approach to highlight a targeted entity and
direct the observer’s attention to it, is to implement a
magnifier that is laid over the entity. An enlargement
of the targeted entity as well as of the whole video
stream would not result in any highlighting effect,
when displaying just a region of the original video
stream. Instead, only the area of the targeted entity
is enlarged creating the effect of a magnifying lens
aligned to the focal point of the targeted entity (see
Figure 4b). As a result, the pixel at the focal point
of the entity is not altered due to the fact that the
virtual lens is aligned to it. A point of the targeted
entity is projected in a farer distance to the focal
point based on the current distance to the focal point.
The higher the current distance to the focal point
amounts, the higher results the difference between
the current point and the projected point. Figure 4a
illustrates this issue.
(a) (b)
Figure 4: Illustration (a) and screenshot (b) of the Magnifier
The Magnifier effect is created by projecting
points based on the distance to the focal point simi-
lar to the approach of section 4.10. Equation 7 shows
the calculation for the projection of a point. In this
case, the parameter s, that determines the degree of
the magnification, is calculated by Equation 8. The
value of s = 0.3 creates the effect of a magnified
entity without occupying to much of the user’s view.
The transition between projection and unchanged dis-
play of a pixel (dependent on the radius r to the focal
point) takes place at the distance r
from the focal
point. Due to this transition, there is a distortion of the
image at the circle with radius r
around the focal
s(r) =
0.3 if r < r
0 else
In order to evaluate each of the highlighting
techniques described in section 4 concerning recog-
nizability, disturbances, and overall impression, a
user study was conducted with 23 participants. The
youngest participant was 16 years old, whereas the
oldest participant was 72 years old. The average
age over all participants was about 38.3 years. A
total of 9 participants were female. Moreover, we
were interested in the question if people, who are
used to design and handle different applications
including diverse kinds of interaction methods, assess
the designed techniques differently than people
with basic PC knowledge. So the user’s knowledge
concerning IT or PC handling was recorded as well.
We classified the user’s knowledge into two levels,
that are “IT Knowledge” (in short “IT”), which
means the user has profound skills in IT handling and
programming, and “PC Knowledge” (“PC”), which
means the user has basic skills to handle a computer.
Highlighting Techniques for Real Entities in Augmented Reality
Figure 5: Overview of highlighting techniques: (a) Fire, (b) Blinking Aura, (c) Red Overlay, (d) Red Frame, (e) Color
Contrast, (f) Black Aura, (g) Distortion, and (h) Hotplate.
Concerning the IT or basic PC knowledge, a total of
9 participants were computer science students who
rated themselves “IT”, whereas 14 of the participants
rated themselves “PC”. The average age in the “IT”
group was about 24.3 years and the average age in
the “PC” group was about 47.3 years. Except for
one participant, a classification of all participants into
“young” (that we defined with an age 30 years)
and “old” (age > 30 years) people is identical with
the classification into “IT” and “PC”.
For the user study, five real entities were arranged
on a table. The test subject observed the scene using
video-see-through. The application randomly picked
out one entity of the scene and highlighted it ran-
domly choosing one technique from the set of high-
lighting techniques described in section 4 that were
not used in the test so far. Thus, the application chose
each of the 11 highlighting techniques exactly once.
The test users had to click the mouse on the entity
that they think is highlighted. The time from showing
a highlighted entity to the user’s click was measured.
After the click, the object remained highlighted on the
screen and the test user was asked a total of ve ques-
tions concerning the current highlighting technique.
For each question, we asked the test user to rank the
correspondent criterion on a scale from 1 to 5. The
questions were:
1. How well or respectively how fast did you recog-
nize the highlighted entity? (scale from 1 [very
bad / slow] to 5 [very well / fast])
2. Imagine you wear some glasses for Augmented
Reality in which certain entities are permanently
highlighted in the way you see here. How strong
would you feel disturbed? (scale from 1 [ab-
solutely not disturbed] to 5 [very strongly dis-
3. Are there any disturbances in the (visual) percep-
tion of the background? (scale from 1 [no distur-
bances] to 5 [very strong disturbances])
4. Are there any disturbances in the (visual) percep-
tion of the entity itself? (scale from 1 [no distur-
bances] to 5 [very strong disturbances])
5. How well is the contour of the highlighted entity
visible? (scale from 1 [not visible] to 5 [clearly
For question 2, we ensured that all participants
knew about AR and according devices by giving them
a clear explanation about the principle concept. If
one test user ranked the questions 3 and 4 other than
1, we additionally asked the user to explain what kind
of disturbances he or she perceived. After a test user
ranked all of the 5 questions concerning the current
highlighting technique the application had picked,
he or she could hit the space key and continue with
the next randomly chosen entity highlighted with
another technique. To avoid that the user recognized
a highlighted entity due to a change of the previous
highlighted entity to the entity to be highlighted
next, a black screen was replacing the video stream
for about two seconds before the application chose
GRAPP 2016 - International Conference on Computer Graphics Theory and Applications
and showed the next entity. At the end of the test,
we asked the user to rank each of the highlighting
techniques according to his or her overall impression
on a scale from 1 (“I do not like it at all”) to 5 (“I like
it really much”). We assume that the rankings for the
questions are normal distributed based on the fact that
100% of the values are in an interval of 3· σ (standard
deviation) around the average for each question and
each highlighting technique. For all 6 questions (5
during the test and one after it) a one-way ANOVA
analysis between two groups of participants (“IT”
and “PC”) was conducted.
Figure 6 shows the average rankings of the first
question. The test users stated they could recognize
those entites best or respectively fastest that were
highlighted with Fire (with an average value of
4.96), whereas entities highlighted with Red Overlay
and Blinking Aura were ranked worst recognizable
(3.43 and 3.52). There was a statistically significant
difference between the “IT”- and “PC”-group of
participants for the ranking of Emitter as determined
by one-way ANOVA (F(1,21) = 6.18, p = 0.021),
whereat the “PC”-group ranked higher (average of
4.79) than the “IT”-group (4.22). The p-values of
the techniques Garlands, Hotplate and Blinking
Aura are low as well (between 0.059 and 0.078),
but not statistically significant low, due to the fact
that the average ranking of the “PC”-group is about
0.6 higher than the ranking of the “IT”-group. The
results show that the “PC”-group rates particle effects
higher in question 1 than the second group.
Figure 6: Average rankings of question #1 (including con-
fidence intervals with confidence level γ = 0.95): Entities
highlighted with Fire were recognized best.
The average rankings of the second question
are illustrated in Figure 7. The chart shows that
Distortion would be the most disturbing highlight-
ing technique when permanently shown in some
Augmented Reality glasses (with an average value
of 3.91). The test users rated Red Overlay the least
disturbing highlighting technique (2.35). The ranking
of the two groups show no significant differences
(p-values >= 0.364) except for the Color Contrast
technique. Most members of the “IT”-group did
not notice a gray scale background as a disturbance.
Consequently, there is a low p- value (F(1,21) =
10.99, p = 0.003) based on an average value of 2.5
for the “PC”-group and an average value of 4.2 for
the “IT”-group.
Figure 7: Average rankings of question #2 (including con-
fidence intervals with confidence level γ = 0.95): The most
disturbing highlighting technique was Distortion, the least
disturbing one was Red Overlay.
Figure 8 illustrates the average rankings of the
third question. The highlighting techniques with the
strongest disturbances in the visual perception of the
background were Distortion (3.70) and Magnifier
(3.52). Most users, who ranked Distortion and Mag-
nifier a high value in the third question, mentioned
the distortion that both highlighting techniques pro-
voke. They stated that the surroundings of the entity,
especially the entities beside the targeted entity, were
not clearly visible. Another disturbance was the
change in color at the borders of the distortion ring or
respectively the magnifier. Most of the participants
ranked Black Aura high concerning distractions of
the background because of the darkening aura that
covers the surrounding area of an entity and so
partially blocking the observer’s view. Some of the
participants stated that the aura near to the targeted
entity was blurred. The Color Contrast technique is
ranked high in this question based on the fact that
a transformation of the background to a gray scale
image is clearly a disturbance of the original image.
As mentioned before, it is statistically significant that
most members of the “PC”-group did not mention
the gray image as a disturbance which results in
different values for the average (“PC”: 1.71, “IT”:
3.44, F(1,21) = 11.84, p = 0.002). In addition to
Color Contrast, Red Frame also shows statistically
significant higher values for the “IT”-group (“PC”:
1.0, “IT”: 1.6, F(1,21) = 8.52, p = 0.008). This may
be caused by the fact that “IT”-members stated a
Highlighting Techniques for Real Entities in Augmented Reality
more critical evaluation mentioning the overlay of
Red Frame as a distraction.
Figure 8: Average rankings of question #3 (including con-
fidence intervals with confidence level γ = 0.95): A high-
lighting technique without any disturbances was Garlands,
whereas Distortion was the one with the most disturbances.
As depicted in Figure 9, which shows the av-
erage rankings of the fourth question (concerning
distractions of the targeted entity itself), Black Aura
was identified as the technique that created the most
distracting effects in a targeted entity itself based
on the fact that irritating movement of the contour
is visible mostly caused by unstable sensor data.
The change of the shape of an entity and the partial
darkening of the entity caused by the Black Aura
effect was mentioned by most of the participants. On
the contrary, Blinking Aura and Distortion caused no
visible distortion for the entity itself. We observed
high ratings in question 4 for the Fire effect. One
explanation could be that the Fire effect makes it
comparably difficult to clearly observe the contour of
the targeted entity. The techniques Red Overlay and
Red Frame show high values in this question as well,
which could be attributed to the fact that Red Overlay
alters the color of the entity, whereas Red Frame
covers parts of the contour and shape. One result
of question 4 and question 5 is that an observer’s
ability to recognize the highlighted entity is mainly
depending on the visibility of the contour. Indeed,
question 4 and question 5 show almost the same
result except for the inversed order caused by the way
of questioning. With reference to the “IT” and “PC”
group, there is one statistically significant value for
the Magnifier technique in question 4 as some of the
“IT” members mentioned a reduced accuracy of the
contour of an entity (F(1,21) = 5.52, p = 0.029). The
contour is less clear when displaying the entity in a
higher resolution.
The average rankings of the overall impressions
of each highlighting technique are shown in Figure
10. The test users liked Hotplate most (3.70), fol-
Figure 9: Average rankings of question #4 (including con-
fidence intervals with confidence level γ = 0.95): The high-
lighting technique Blinking Aura did not provoke any dis-
turbances, whereas entities highlighted with Black Aura led
to the most disturbances.
lowed by Emitter (3.48). The highlighting techniques
Red Frame and Distortion obtained the lowest as-
sessments (2.09 and 2.39). There are no statistically
significant differences between the “IT” and the “PC”
Figure 10: Average rankings according to the overall im-
pression (including confidence intervals with confidence
level γ = 0.95).
The chart in Figure 11 presents the measurements
of the time needed between the display of a highlight-
ing technique and the user’s click response. Mostly,
the median values according to the highlighting
techniques are in a small range from 2.2 seconds up
to 3.8 seconds. Particle systems are identified as a
technique might achieve a higher degree of attention
based on median values under 3 seconds. This
observation conforms with the results of question
1 which evaluates a short recognition degree for
particle systems. Blinking Aura (3.8 sec.) and Color
Contrast (3.7 sec.) emerged as techniques that need a
comparably higher amount of time to be recognized
by a user. Furthermore, a test with a color-blind
person, who stated this highlighting technique as
almost invisible, indicates that Color Contrast is
GRAPP 2016 - International Conference on Computer Graphics Theory and Applications
not barrier-free. In contrast, the other highlighting
techniques could easily be recognized by this person.
Figure 11: Measurements of recognition time. The recogni-
tion time for Color Contrast has a very high deviation due
to the fact that one color-blind participant recognized the
highlighted entity after more than 70 seconds.
To compare the conventional techniques Red
Frame and Red Overlay with the designed techniques
using novel approaches, we conducted paired t-tests
(two-tailed, confidence level γ = 0.95) for the 6
questions where conventional and novel techniques
are the related groups of the tests. We assume a
normal distribution based on the fact that 100% of the
values regarding the two groups are in an interval of
3 · σ (standard deviation) around the average value for
each question. As there are p-values 0.039 for all
6 tests, there are statistically significant differences
between conventional and novel techniques.
For all highlighting techniques, the average frame
rate in frames per second (fps) was measured, based
on a 5 minute measurement for each highlighting
technique. Figure 12 shows the results of the mea-
surements. The highlighting techniques that use par-
ticle systems have the best frame rates (with values of
12.6 fps and 12.4 fps), followed by the aura effects
(10.8 fps and 10.4 fps). Color Contrast shows the
lowest frame rate (5.0 fps) due to the fact that a time-
consuming color transformation to HSV color space
was used. This shows that the proposed highlighting
techniques might have a considerable impact on the
frame rate.
We designed and implemented a total of 9 novel
highlighting techniques that can not only be applied
to virtual entities but to real entities as well. Thereby,
we focused on techniques that are using moving
particles (Fire, Emitter, Garlands, and Hotplate),
Figure 12: Measurements of the average frame rates in
frames per second (fps) of all highlighting techniques.
auras (Blinking Aura and Black Aura), distortions
(Distortion and Magnifier), and an alteration of
the color (Color Contrast) to highlight a certain
entity. Additionally, for comparison purposes, we
implemented two more conventional techniques (Red
Frame and Red Overlay). We conducted a user study
with 23 participants evaluating the quality of each
highlighting technique concerning recognizability,
disturbances, and overall impression. Moreover,
we examined the question if there are statistically
significant differences in the perception of the high-
lighting techniques for users with much experience
concerning IT handling and those with few.
The results of the user study show that Hotplate
obtained positive assessments. It emerged as a tech-
nique combining useful attributes for highlighting an
entity in the user’s view. In fact, Hotplate was ranked
highest in overall impression with an average value
of 3.7 and identified as a technique recognized fast
(proven by click time measurement). Furthermore,
it caused only little disturbances of the background
(question 3) as well as the entity itself (question 4/5).
Moreover, based on the user study, particle systems
in general can be named as a successful tool to ef-
fectively draw attention to a specific entity or an area
in the user’s view without creating annoying distrac-
tions. Though more conventional highlighting tech-
niques like Red Frame and Red Overlay show good
recognition times as well, they obtained low assess-
ments concerning the overall impression. The dif-
ferences between conventional and novel techniques
proved to be statistically significant. Thus, the high-
lighting techniques presented in this paper can be
considered a useful addition to the highlighting tech-
niques already described in the related literature. A
designer intending to use them needs to tweak sev-
eral parameters associated with each technique (e.g.
colors used or velocity of movement). Also, the im-
pact of these highlighting techniques on performance
Highlighting Techniques for Real Entities in Augmented Reality
have to be considered and optimizations that aim to
increase performance need to be examined. The latter
two issues are starting points for future work.
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