A New Method for Assessing the Exploratory Field of View (EFOV)
Enkelejda Tafaj
, Sebastian Hempel
, Martin Heister
, Kathrin Aehling
, Janko Dietzsch
Frank Schaeffel
, Wolfgang Rosenstiel
and Ulrich Schiefer
Wilhelm-Schickard Institute, Computer Engineering Department, University of T
ubingen, T
ubingen, Germany
Centre for Ophthalmology, Institute for Ophthalmic Research, University of T
ubingen, T
ubingen, Germany
Exploratory Field of View, Visual Exploration, Visual Field, Ophthalmology, Eye Movements.
Intact visual functioning is a crucial prerequisite for driving safely. Visual function tests usually include the
assessment of the visual acuity and binocular visual field. Based on these results, persons suffering from
some types of visual field defects that affect the central 20 degree region are prohibited from driving, although
they may have developed patterns of eye and head movements that allow them to compensate for their visual
impairment. We propose a new method to assess the exploratory field of view (EFOV), i.e. the field of view
of a subject when eye movements are allowed. With EFOV testing we aim at capturing the visual exploration
capability of a subject and thus understand the real impact of visual field defects on activities of daily living
and potential compensatory strategies.
Visual information accounts for up to 90% of driv-
ing related-inputs (Taylor, 1982). The assessment of
the visual acuity and visual field are therefore impor-
tant elements of ability tests in traffic ophthalmology.
According to current recommendations, subjects suf-
fering from binocular visual field defects affecting the
central 20 degree region are prohibited from driving.
Visual field testing, i.e. perimetry, consists of measur-
ing the sensitivity of visual perception as a function of
location in the visual field (Schiefer et al., 2008). The
stimuli are projected onto a homogenous curved back-
ground. In kinetic perimetry, a stimulus with constant
luminance is moved from blind areas almost perpen-
dicularly towards the assumed visual field defect bor-
der. The position at which the presented stimulus is
detected, represets the border and/or the outer limit of
the visual field. Static perimetry is mainly performed
automatically by computer-driven stimulus presenta-
tion. The size and location of a stimulus is kept con-
stant while its luminance varies, usually in a stepwise
up- and-down manner. Subjects indicate stimulus per-
ception by pressing a response button. A missing re-
sponse to a stimulus projection is interpreted as a fail-
ure to seeing it (Schiefer et al., 2008).
Although perimetry is a highly standardized psy-
chophysical method, it is yet rather artificial. In ev-
eryday but safety critical activities such as driving a
car, subjects are usually neither confronted with
small, rather dim light stimuli on an homogeneous
background nor do they have to refrain from eye and
head movements. Instead, conspicuous objects within
the visual field induce a shift of the visual attention,
eliciting eye and head movements towards the object
of interest. These types of movements can also help
to - at least partially - compensate for existing visual
defects. Several studies, e.g. (Martin et al., 2007),
(Hardiess et al., 2010), (Pambakian et al., 2000), (Ri-
ley et al., 2007), have been conducted to explore the
viewing behavior of persons with advanced visual
field defects such as homonymous, where half of the
visual field in both eyes is affected. These studies
confirmed that persons who have developed a good
exploration capability are able, by performing effi-
cient eye and head movements towards their visual
field defect, to obtain information from the impaired
part of the visual field. Present ability testing does
address ocular motility, however only with regard to
disclose double vision during smooth pursuit instead
of unveiling insufficient saccadic eye movements to-
wards objects of interest.
In this paper we introduce a new method to assess
the exploratory field of view (EFOV), i.e. the field of
view of a person when eye movements are allowed.
During EFOV testing, the subject is encouraged to
move his eyes towards the presented stimulus in or-
der to fixate it. EFOV testing can capture the visual
Tafaj E., Hempel S., Heister M., Aehling K., Dietzsch J., Schaeffel F., Rosenstiel W. and Schiefer U..
A New Method for Assessing the Exploratory Field of View (EFOV).
DOI: 10.5220/0004190600050011
In Proceedings of the International Conference on Health Informatics (HEALTHINF-2013), pages 5-11
ISBN: 978-989-8565-37-2
2013 SCITEPRESS (Science and Technology Publications, Lda.)
exploration capability of a subject and thus reveal the
real impact of a visual field defect. Implementation
details and exemplary results of the assessment of the
exploratory field of view will be presented in the fol-
lowing sections.
The Exploratory Field Of View (EFOV) Test is im-
plemented for usage with cupola perimeters such as
the Octopus 900 perimeter (HAAG-STREIT Inc., Ko-
eniz, Switzerland) depicted in Figure 1. The perimet-
ric system itself consists of an examination unit, Oc-
topus 900 cupola, and a control unit, Figure 1. The
control unit is a notebook computer or a PC that com-
municates with the examination element via an Ether-
net link. For fixation stability, the Octopus perimeters
are equipped with a camera directed to the subject’s
eye. The eye is illuminated with infrared LEDs and
then captured by a CMOS camera at an image resolu-
tion of 320 × 240px and a sampling rate of 20Hz.
Figure 1: The EFOV test on an Octopus 900 perimeter.
The EFOV test is implemented as an add-on to the
Octopus control software. In contrast to usual peri-
metric examinations, where the subjects’ head and
eyes are fixated, during the assessment of EFOV the
subject is allowed to move his eyes towards the pre-
sented stimulus and fixate it. A stimulus is considered
as perceived if it is fixated by the subject. Therefore,
no explicit subject’s response (e.g. by pressing a but-
ton) is needed. The examination procedure consists
of the following steps:
Similar to conventional perimetric examinations,
first the examination set-up is configured. This in-
cludes the choice of the grid of stimuli that will be
presented sequentially, as well as an initial param-
eter setting, e.g. the definition of a time windows
defining the stimulus presentation time or for cap-
turing the user’s response.
After the general configuration of the examina-
tion, a calibration routine is started. During cali-
bration, a mapping between the eye position in the
camera image and the gaze position in the scene,
i.e. on the perimeter surface, is calculated. The
algorithms involved in this examination step will
be discussed in the next section.
Once the calibration procedure is successful, the
examination can start. A grid of stimuli is loaded
and the stimuli are presented sequentially in ran-
dom order. For each stimulus the subject is en-
couraged to perform eye movements towards the
stimulus location in order to fixate the target. The
software captures the visual search behavior of the
subject and the fixation (if any occurred).
Finally, the testing results are visualized.
Figure 2 depicts the main components of the soft-
ware: a communication component that encapsulates
several routines for real-time communication with the
Octopus perimeter hardware and software, an easy
to use GUI for the examination control, several al-
gorithms for the detection of the pupil, for calibra-
tion and gaze mapping, and a collection of routines
for reading, writing and visualization purposes. The
software is developed in C++ and uses the Microsoft
Foundation Classes
Graphical User Interface: The graphical user in-
terface of EFOV is simple, intuitive and aggregates
following information:
Video signal from the cupola perimeter, Figure 3
left upper part. The result of the pupil detection is
denoted by a red circle. This image can be used
both for fixation control and for online validation
of the result of the pupil detection algorithms.
Both presented stimulus and gaze position of the
subject are visualized in a polar coordinate grid,
Figure 3 right upper part. The depicted red line
visualizes the eye position over the last 5 seconds.
The graph at the bottom part of the GUI depicts
the pupil size during the examination. When no
pupil is found, e.g. due to blinks, the pupil size is
zero. This is represented by lows in the chart. So
far, the pupillographic information in only used
Figure 2: Components of the EFOV software.
for monitoring the eye position. In the future we
will use this information to monitor the vigilance
of the subjects during testing.
Furthermore the GUI provides several possibilities to
configure an examination, e.g. subject data, the set-
up of the stimulus grid, the size and duration of the
stimuli or the response time window.
Figure 3: The Graphical User Interface of EFOV. The left
upper part depicts the video signal from the cupola perime-
ter with the detected pupil (red circle). The presented stim-
ulus (black circle) and the gaze position of the subject over
the last 5 seconds are presented in a polar coordinate system
at the right upper part. The graph at the bottom depicts the
pupil size during the examination.
Read/Write and Visualization. A visualization ex-
ample of EFOV testing is presented in Figure 4(b).
The black dots indicate the presented stimuli (here the
stimuli grid consists of 72 stimuli locations in a po-
lar arrangement within the central 30
field of view).
The red dots represent the location of the subject’s
fixations. Note that when a stimulus is presented, the
subject is asked to search for the presented stimulus
and then fixate it. The presented stimulus (black dot)
and its corresponding fixation point (red dot) are con-
nected by a red line. The longer the line, the greater
is the mismatch between presented stimulus and loca-
tion of the subject’s fixation. Thus the length of the
line represents both the exploration quality and indi-
cates an overshoot or undershoot of the exploratory
2.1 Algorithms
To be able to detect a fixation during a video se-
quence, first the gaze position of the subject at each
time step, i.e. video frame, has to be determined. The
gaze position is the position of the pupil midpoint in
the scene, i.e. cupola surface. For the detection of the
pupil and gaze position we perform image processing
of the video signal captured by the camera interface
in the Octopus perimeters. Furthermore, changes of
pupil size are monitored.
Pupil Detection. Real-time performance is a major
prerequisite to the image processing algorithms. We
use both global and local image features to extract
the pupil. The basic idea behind it is simple: since
the pupil represents an extended circular object with
dark pixels, we have to search for and find such pixel
groups. First, global image properties, such as the av-
erage image brightness b
, are calculated. Then, for
performance reasons, every second pixel is processed.
Its gray level value is compared to a threshold gray
level th that varies with the average image brightness.
This threshold is defined as
th = α b
The variable α is set empirically and can be adjusted
by the user until the pupil detection is satisfactory.
Since other regions in the image, such as eye lashes,
may also contain dark pixel groups, for each detected
group of dark pixels, it is necessary to calculate the
brightness of its neighboring pixels. Only those pix-
els that are surrounded by dark pixels within a user-
defined neighborhood area will be considered as can-
didates for the pupil. The pixel group that corre-
sponds to the pupil is found by shape matching as the
pixels corresponding to the pupil should form a cir-
cular object. Finally, for the identified pupil region,
the area and radius is calculated. We track the posi-
tion of the pupil center, which moves linearly with the
direction of gaze.
Gaze Mapping and Fixation Detection. To be able
to calculate the gaze position in the scene (i.e. on
perimeter surface), we need to provide a mapping be-
tween the position of the eye within the camera image
and the coordinates on the perimeter surface. The co-
ordinates of the eye position are defined by the coor-
dinates of the pupil center. The mapping is calculated
during a calibration routine, as usual in eye-tracking
applications. We use a 3 × 3 calibration grid as pre-
sented by (Li et al., 2005).
The scene points
= (x
) are given in polar
coordinates and can be configured at the beginning
of an examination. We define following default val-
ues for the coordinates x
{−20,0,20}. The re-
sulting nine points from the combination of these co-
ordinates are presented during the calibration routine
sequentially, where each point is presented for 5 sec-
onds (corresponding to 100 frames at the sampling
frequency of the camera). The subject is asked to fix-
ate each presented calibration point. During stimulus
presentation the eye position ~e
= (x
) in the im-
age is calculated using the algorithms for the pupil
detection described above. When the eye position is
stable for a time period f
, a fixation is assumed. In
order to achieve best mapping precision, during the
calibration procedure we expect long fixations f
1000ms (corresponding to 20 video frames). Thus,
the standard deviation f
of the eye position in the im-
age data is computed for the last 20 frames. When the
standard deviation respects an empirically determined
threshold th
= 4px that considers the inaccuracy of
the eye tracker, f
< th
a fixation is assumed. If a
fixation cannot be recognized (e.g. due to an impaired
cooperation) the missed stimulus is presented again.
The mapping of the eye position in the image to
the gaze position in the scene, - perimeter surface - we
use a first-order linear mapping (Li et al., 2005). For
each correspondence between
and ~e
, two equations
are generated that constrain the following mapping:
= a
+ a
+ a
= a
+ a
+ a
where a
and a
are undetermined coefficients of
the linear mapping. This linear formulation results
in six coefficients that need to be determined. Given
the nine point correspondences from the calibration
and the resulting 18 constraint equations, the coeffi-
cients can be solved using Single Value Decomposi-
tion (Hartley and Zisserman, 2000).
In a further step, for each presented stimulus
during the EFOV test, we have to find out whether
the stimulus was fixated by the subject. Generally,
when a presented stimulus is fixated, the subject’s
gaze oscillates around the stimulus location forming
a fixation cluster. A fixation is assumed if the gaze
is kept around the stimulus location for at least 300
ms (Liversedge et al., 2011). At a sampling rate of
20 Hz, as it is the case in the built-in cameras of the
Octopus perimeter, 300ms correspond to 6 frames (or
gaze points). After the presentation of a stimulus, our
algorithm searches for clusters of points in at least
6 sequential video frame. This parameter is config-
urable and can easily be adapted to other sampling
rates. If a fixation cluster is detected, we calculate
the cluster centroid that represents the location of
the fixation. As described above, for each stimulus
location (Figure 4(b) black dots) we calculate the
corresponding fixation location (Figure 4(b) red dots).
2.2 Modeling Fixation Data with the
Generalized Pareto Distribution
We observed that an exact match between the location
of the presented stimulus and the corresponding fixa-
tion is given very rarely. Instead, for a given stimulus
location, the distribution of the distances between the
stimulus location and the fixations of different sub-
jects corresponds to a Pareto distribution. The ques-
tion is: up to which distance between fixation and
stimulus d
can a stimulus be considered as per-
ceived (seen)?
We used the Generalized Pareto Distribution
(GPD) to model the distribution of distances between
fixation and stimulus and implemented the model us-
ing Matlab (MATLAB, 2012). The probability den-
sity function of GPD is given by the following Equa-
tion 1 (Kotz and Nadarajah, 2000), (Embrechts et al.,
y = f (d|k, σ, θ) = (
)(1 + k
(d θ)
where d is the distance between stimulus location
and fixation location, k 6= 0 is the shape parameter,
σ the scale parameter and θ the threshold parameter.
The threshold value d
was obtained from the 95%
quantile of fixations of healthy (control) subjects, see
Section 3.
EFOV was validated in a pilot study with 80 sub-
jects, 40 patients with binocular visual field defects
and 40 ophthalmologically healthy (control) subjects.
The aim of the study was to investigate the prediction
capability of EFOV for everyday living conspicuous
objects within the central 30
of the visual field. Fur-
thermore, the driving performance of the subjects was
assessed in an on-road study using a dual-brake vehi-
cle. The scope of the study was much broader and
involved the investigation of visual scanning behavior
and its impact on the driving performance. The oph-
thalmological interpretation of the results of the study
is beyond the scope of this paper and will be presented
in a separate article. In the following we will focus on
some exemplary results of the assessment of the ex-
tended field of view to show the viability of the soft-
To determine the threshold d
, i.e. the maximal
distance between a perceived stimulus and the corre-
sponding fixation, we fitted the GPD model presented
in Equation 1 to the fixation data collected from the
control subjects. From the 95% quantile we obtained
the threshold value d
= 10. Thus, a stimulus is
considered as perceived, if the distance between its
location and the location of the corresponding fixa-
tion does not exceed 10
Figure 4(a) shows the binocular visual field of a
subject with a homonymous visual field defect that
was tested with semi-automated kinetic perimetry.
The red line represents the boundary of the intact vi-
sual field. This subject is suffering from right-sided
hemianopsia, therefore he has no perception within
the right hemifield. Based on this result, the sub-
ject was banned from driving. Figure 4(b) depicts the
EFOV testing result. The gray area represents the vi-
sual field defect from 4(a). The black dots represent
the locations of the presented stimuli, while the lo-
cations of the fixation clusters are represented by red
dots. As mentioned above, the location of a stimu-
lus and its corresponding fixation are connected by a
Figure 4: The performance of a subject with good ex-
ploration capability. The upper figure shows the result of
a standard semi-automated kinetic perimetric examination,
where the red line represents the boundary of the intact vi-
sual field. The subject can perceive only stimuli that are
presented within the left hemifield. The lower figure shows
the result of the exploratory field of view testing. The black
dots represent the locations of the presented stimuli. The
red dots are the locations of the subject’s fixation. When eye
movements are allowed, this subject can obviously compen-
sate for his visual field defect.
red line. Implicitly, the line length represents the ex-
ploration capability of a subject. The longer the line,
the greater is the mismatch between presented stimu-
lus and fixation location. If the distance between the
location of a presented stimulus and the location of
the fixation exceeds 10
, the stimulus is considered
as not seen. Such ’failed-to-see’ stimuli and missing
fixations (i.e. when no fixation was detected within
a given response time window) are represented by
red crosses. As we can see from the distances be-
tween the stimuli locations and the locations of the
fixations clusters, this patient has developed a good
visual search strategy that enables him to compensate
for his visual field defect.
Another example for a successful visual search
strategy is shown in Figure 5. This subject can fully
compensate for his visual field defect and is able to
perceive all stimuli presented in the area of his visual
field defect. Interestingly, both subjects passed the
on-road driving assessment.
Figure 5: The EFOV result of a subject with a successful
visual search strategy.
Figure 6(a) presents the visual field of another
subject suffering from left sided homonymous hemi-
anopsia. The lengths of the mismatch lines within the
right hemifield and the missed stimuli indicate poor
exploration capability. This subject failed the on-road
driving task.
In this paper we have presented a software for the as-
sessment of the exploratory field of view using a con-
ventional cupola perimeter. Pilot studies conducted
so far have shown that this approach allows the as-
sessment of exploratory capabilities, which may be
more relevant than the extent and location of the vi-
sual field defect. In contrast to standard perimetric
examinations, the assessment of EFOV promises to
reveal the the real impact of visual field defects on ac-
tivities of daily living, such as driving. Yet, broader
evaluation of the method is needed.
Due to its modular structure, the EFOV software
can easily be extended by further modules. Our future
work will be threefold. As many subjects report loss
of vigilance during visual field examinations, lead-
ing thus to an increase in the frequency of missed
Figure 6: The performance of a subject with left sided hemi-
anopsia. The upper figure shows the result of a standard
semi-automated kinetic perimetric examination. The lower
figure shows the result of the EFOV assessment. In contrast
to the results of the subjects in Figures 4(b) and 5, this sub-
ject could perceive only few of the stimuli within the visual
defect area. This indicates poor exploration capability.
targets, we will develop a module for the monitor-
ing of vigilance by using the pupillographic informa-
tion (Henson and Emuh, 2010). Furthermore we plan
the improvement of the calculation of fixation clus-
ters using a Bayesian online clustering method (Tafaj
et al., 2012). Up to now EFOV was developed for
usage with Octopus perimeters. To make it available
for a broader set of applications, we plan to integrate
the testing method with existing vision analysis tools,
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