A digital display device field of vision testing system

Abstract

The present system is designed for home-based, self-administered visual field testing without the need for expert involvement, utilising common computing devices. The system adapts to differences in v

Description

A Digital Display Device Field of Vision Testing SystemField of the Invention

[0001] The invention is a system designed to assess and monitor visual field thresholds using common digital display devices enabling accurate testing and even self-testing. The system is designed to replace traditional specialised bowl-type visual field-testing machines whilst offering accurate vision field testing and progression monitoring using a wide range of digital display consumer electronic devices.Background of the Invention

[0002] Glaucoma, a leading cause of blindness globally, is characterised by the degeneration of the optic nerve, often associated with cupping of the optic disc and elevated intraocular pressure (IOP). Glaucoma typically damages vision starting in the peripheral regions. Thus, visual field (VF) tests, covering a wide area (e.g., 30 degrees), are standard for diagnosing glaucoma. These tests, known as perimetry or automated perimetry, use machines with a bowl-shaped surface projecting varying intensity light spots. The patient focuses on a central spot and responds to peripheral light spots, with the machine measuring retinal sensitivity and creating a graphical representation of deviations from normal values, aiding in diagnosing and monitoring glaucoma.

[0003] Alternative digital display-based methods for eye testing have been proposed including US20230165460A1 (Shousha, 2023-06-01 ) which uses a wearable device which displays stimuli at various field locations of the visual field and use spatial information to select appropriate display locations to collect feedback based on the eye's response using sensors, thereby assisting in diagnosing ocular anomalies.

[0004] US20130155376A1 (Huang et aL, 2013-06-20) involves a video game designed to map a test subject's peripheral vision. The game includes a moving visual fixation point confirmed by the subject's action and requires the subject to locate a briefly presented visual stimulus. The game runs on a platform comprising a video display, user input device, and video camera to map the subject's visual perception thresholds, comparable with age-stratified normative data.

[0005] However, there is a pressing need for home-based vision testing that can be self-administered without professional supervision on conventional computing devices. However, variations in display characteristics, such as screen resolution and brightness, as well as improper use and variable environmental factors, can render vision testing on these devices relatively inaccurate.Summary of the Disclosure

[0006] The described field of vision testing system is designed for home-based, selfadministered visual field testing using common digital display devices. In embodiments, it employs calibration steps to accommodate different screen sizes and brightness levels, as well as methods to ensure accurate testing, including stabilising viewing distance, detecting improper use, and accounting for digital display curvature.

[0007] Similar to standard perimetry, the test subject focuses on a fixation point and responds to stimuli displayed at peripheral locations.

[0008] The system may account for screen size variability by displaying calibration markers and offset adjustment controls to adjust the distance between them to match a fixed physical object. This allows the system to calculate a screen size scaling factor according to the distance and subsequently scale the screen size accordingly. This procedure can be performed accurately by non-specialist home users on conventional computer devices.

[0009] To account for screen brightness variability, the system may display a first stimulus at a brightness below known visual threshold (lowest contrast level which can just be detectable by human eye) and a second stimulus at a brightness just above the threshold, calculating screen brightness based on the frequency of responses to these stimuli. The system determines the screen brightness by analysing the number of responses received for each stimulus.

[0010] The system may ensures proper viewing distance through viewing distance calibration, which involves positioning the digital display a set distance from the user’s face and analysing image data obtained from a camera to set a reference calibrated facial metric. Real-time viewing distance monitoring is achieved by continually analysing image data to determine a real-time facial metric and comparingit to the calibrated metric to determine the real-time viewing distance. The system can warn the user if the viewing distance deviates from the calibrated distance.

[0011] For smaller screens, the system may adjust the position of the fixation point to the four corners of the screen to enable testing of viewing distances greater than from the centre. The system may also reduce threshold variability by applying a scaling factor to spot size to account for the tangent effect of digital displays, particularly at peripheral locations.

[0012] Gaze stability may be is monitored by using a front-facing camera to capture images of the eye and analyse pixel intensity of key features to assess gaze stability, ensuring reliable visual field testing. The system may also verifies the occlusion of the non-tested eye by capturing images of the eyes and ensuring the correct eye is occluded, with warning messages provided if the incorrect eye is occluded.

[0013] In embodiments, the system translates a stimulus position from flat screen geometry to curved screen geometry and detect background environmental brightness by analysing general and localised brightness within image data obtained from the camera.

[0014] For vision field testing, the system may employ Bayesian probability prediction to perform a binary search for the threshold contrast at each location, using a population probability density function (PDF) that includes individuals with normal eyes and those with disease. This allows for fast and accurate estimation of thresholds based on the subject's response matrix. Neighbourhood logic checks for errors in responses and recovers from potential corruptions in threshold logic, refining the prediction of initial seeding intensity for each test location based on neighbouring location endpoints. Furthermore, the system may employ adaptive responses by adjusting the speed of the test "wait-window" based on the average speed of response to stimuli for each individual subject. If a subject responds quickly, the software reduces the interval between stimuli, and if they respond slowly, it increases the interval. The algorithm dynamically adjusts the timing based on a running average of past responses.

[0015] Overall, the present system allows for visual field testing to be performed conveniently and efficiently on commonly available devices, providing accurate results while addressing technical considerations associated with the transition from curved bowl surfaces to digital displays.

[0016] Other aspects of the invention are also disclosed.Brief Description of the Drawings

[0017] Notwithstanding any other forms which may fall within the scope of the present invention, preferred embodiments of the disclosure will now be described, by way of example only, with reference to the accompanying drawings in which:

[0018] Figure 1 shows the key components and functionality of a field of vision testing system, which includes a personal computing device with a digital display used for displaying a field of vision testing user interface.

[0019] Figure 2 illustrates the processing carried out by the screen size calibration controller.

[0020] Figure 3 shows a screen size calibration user interface presented by the screen size calibration controller. The interface includes a fixation point and stimuli that are scaled according to the calculated screen size scaling factor.

[0021] Figure 4 demonstrates the processing performed by the screen brightness calibration controller. The figure shows three scenarios: calibrated screen brightness detection, excessive brightness detection, and too dark brightness detection.

[0022] Figure 5 depicts the three screen brightness detections: calibrated screen brightness detection, excessive brightness detection, and too dark brightness detection.

[0023] Figure 6 illustrates the processing conducted by the gaze stability detection controller. It shows the analysis of eye image data to detect gaze stability.

[0024] Figure 7 shows the operation of the gaze stability detection controller, detecting image properties across the eye region to assess gaze stability.

[0025] Figure 8 represents the processing carried out by the eye occlusion detection controller.

[0026] Figure 9 shows the operation of the eye occlusion detection controller, detecting an occluded eye region and a non-occluded eye region in the facial image data.

[0027] Figure 10 displays the processing performed by the viewing distance calibration controller.

[0028] Figures 11 A - D illustrates the exemplary image processing conducted by the viewing distance calibration controller, including the detection of facial width and boundary analysis.

[0029] Figure 12 illustrates mapping of flatscreen geometry to curved screen geometry.

[0030] Figure 13 shows processing for bright spot detection.Description of Embodiments

[0031] Figure 1 shows some key components 101 and functionality 102 of a field of vision testing system 100, which comprises a personal computing device (such as a tablet PC) having a digital display 103 configured to display a field of vision testing user interface thereon. In accordance with a preferred embodiment, the present system 100 employs a Web server architecture wherein the personal computing device is in operable communication with a Web server via the Internet and the personal computing device has a web browser application 104 configured to send HTTP requests to the Web server and to render webpages served by the Web server in response thereto. The personal computing device comprises a processor for processing digital data which is in operable communication with a memory device, the memory device configured to store digital data including computer program code instructions. In use, the processor fetches these computer program code instructions and associated data from the memory device for interpretation and execution of the functionality herein. The computer program code instructions may be logically divided into a plurality of computer program code instruction controllers 105 as will be described in further detail below. The computer program code instruction controllers may comprise a stimulus testing controller which is configured to perform an assessment of visual field function 106 by displaying a fixation point and peripheralstimuli on the digital display 103 and recording responses to the peripheral stimuli. In embodiments, the system 100 may comprise a front-facing camera 107 wherein computer vision analysis 108 is performed on image data obtained by the camera 107 for various purposes as will be described in further detail below. The controllers 105 may comprise a screen size calibration controller 109 which calculates a screen size scaling factor to ensure that the actual distance between the stimuli and the fixation point is uniform across different types of digital displays irrespective of their screen size and / or screen resolution.

[0032] Figure 2 shows processing 1 10 by the screen size calibration controller and Figure 3 shows a screen size calibration user interface displayed by the controller. At step 1 1 1 , the controller 109 is configured to display calibration markers 1 15 (in this case calibration lines) and offset adjustment controls 1 16 (in this case decrement and increment buttons) which are controllable to adjust the distance 1 17 between the markers 1 15. The user is instructed to place a physical object of set size, such as a ruler 1 18, between the markers 1 15 and to use the offset adjustment controls 1 16 to increase or decrease the display distance 1 17 between the markers 1 15 until the distance between the markers 1 15 matches a set length of the ruler 1 18, such as 5 cm. The system 100 calculates the screen size scaling factor according to the distance between the markers 1 15 and thereafter scales the screen size according to the screen size scaling factor to ensure that the actual distance between the stimuli and the fixation point is uniform irrespective of screen size and / or screen resolution variability of different types of digital displays 103. At step 1 13, the controller 109 may calculate the distance between the markers 1 15 as pixels per unit length to take into account screen resolution.

[0033] Figure 3c shows the field of vision testing user interface 1 19 having the fixation point 120 and stimuli 121 which are scaled from a small scale 122 to a larger scale 123 according to the calculated screen size scaling factor. It should be noted that the scaling factor may be calibrated both across the X axis and the Y axis of the digital display 103 to account for differences in X and Y screen resolution accordingly. With reference to Figure 1 , the controllers 105 may further comprise a screen brightnesscalibration controller 124 configured to calibrate the brightness of the digital display 103 to account for any screen brightness variability between different types of personal computing devices. The screen brightness calibration controller 124 may display instructions to increase or decrease the brightness of the screen or may alternatively automatically adjust the brightness of the screen.

[0034] Figure 4 shows processing 125 by the screen brightness calibration controller 124 resulting in calibrated screen brightness detection 126, excessive brightness detection 127, and inadequate brightness detection 128. Specifically, the screen brightness calibration controller 124 is configured to display a less bright stimulus 129A at a brightness below a threshold and a brighter stimulus 129B at a brightness above the threshold. The stimuli 129 may take the form of greyscale dots displayed adjacently on the screen. The user is instructed to respond to the display of the stimuli 129A and 129B at step 130 depending on their visibility. For example, the user would respond to both stimuli 129A and 129B if both were visible, only to the brighter stimulus 129B if only the brighter stimulus 129B was visible, and not respond if neither stimulus 129 were visible.

[0035] Figure 4 shows detection of a high-frequency response 131 A to the less bright stimulus 129A (e.g., greater than 50%) and a low-frequency response 131 B (i.e., less than 50%) to the brighter stimulus 129B. Calibrated screen brightness 126 is detected when both responses 131 A and 131 B are equal as shown in Figure 5. Excessive screen brightness 127 is detected when a high-frequency response 131 A to the low brightness stimulus 129A is detected, and the user may be instructed to lower the brightness of the screen. Inadequate screen brightness (too dark) 128 is detected when a low-frequency response 131 B is detected for the brighter stimulus 129B. Similarly, the user may be instructed to increase the brightness of the screen.

[0036] With reference to Figure 1 , the controllers 105 may further comprise a viewing distance controller 133 configured for viewing distance calibration by analysing image data obtained from the camera to determine a reference calibrated facial metric when the digital display 103 is positioned a set distance from a user’s face. The user may be instructed to position the digital display 103 appropriately using an object of setlength, such as a 30 cm ruler. Then, during use, the viewing distance controller 133 may be configured for real-time viewing distance monitoring by continually monitoring image data obtained from the camera to determine a real-time facial metric and comparing the calibrated reference and real-time facial metrics to determine a realtime viewing distance. Viewing distance controller 133 may be configured to warn the user if the display 103 is too close or too far away. The viewing distance controller 133 may be configured to calculate the calibrated facial metric measured as a pixel width, which may represent facial width, distance between the eyes, nose width, and jawline width. Furthermore, the viewing distance controller 133 may be configured for employing at least one of boundary analysis, contrast differential analysis, and colour differential analysis to determine the calibrated facial metric.

[0037] Figure 10 shows processing 135 by the viewing distance controller 133 and Figures 1 1 A - D shows exemplary image processing by the viewing distance controller 133, which may be individualised to each user’s facial features to allow for small or large faces to be used by the viewing distance controller 133. At step 137, the viewing distance controller 133 is configured to capture an image 143 of the user’s face as shown in Figure 1 1 A and, at step 138, perform computer vision analysis 108 thereon. The computer vision analysis 108 may comprise detecting a facial width 144 as shown in Figure 1 1 B. The facial width 144 may be calculated in pixels. The facial width 144 may comprise detection of the width of a visually detected facial boundary 145. The boundary 145 may be determined using boundary detection analysis, including intensity and colour differential analysis.

[0038] Intensity differential analysis detects variations in pixel intensity to detect edges. For instance, in the facial image, the boundary 145 between the face and the background often exhibits significant changes in brightness. By analysing these intensity gradients, the viewing distance controller 133 can effectively outline the facial boundary 145. Colouring differential analysis assesses differences in colour between adjacent pixels are assessed. In a facial image, the skin tone may contrast with the background or other facial features, such as the eyes or hair. By evaluatingthese colour variations, the viewing distance controller 133 can accurately identify the edges of the face.

[0039] The computer vision analysis 108 may employ edge detection algorithms, such as the Canny edge detector. This method uses a multi-stage process, including gradient calculation, non-maximum suppression, and edge tracking by hysteresis, to produce a precise edge map of the image. Additionally, boundary detection by the viewing distance controller 133 can be enhanced with machine learning approaches. Convolutional neural networks (CNNs) can be trained to recognise and delineate boundaries by learning from annotated datasets.

[0040] In alternative embodiments, the computer vision analysis 108 is configured to detect the width between the outer edges of both eyes. The computer vision analysis 108 may detect the width between the eyes in a facial image using Haar Cascades, which are pre-trained classifiers that detect specific facial features based on edge or line detection. This approach involves using a series of stages, each containing a set of Haar-like features, to identify the eyes within the image. Once the eyes are detected, the distance between their centres can be measured in pixels by the viewing distance controller 133.

[0041] The computer vision analysis 108 may alternatively use convolutional neural networks (CNNs) specifically trained for facial landmark detection to accurately locate key points on the face, such as the corners of the eyes. For example, the facial landmark detection model might identify the coordinates of the inner and outer corners of each eye, allowing for precise calculation of the inter-eye distance. This approach benefits from the robustness and accuracy of deep learning models, especially when trained on large datasets of annotated facial images.

[0042] The computer vision analysis 108 may alternatively use a Histogram of Oriented Gradients (HOG) feature descriptor for eye detection which focuses on the structure of local gradients, which are indicative of edges and textures. By extracting HOG features from a facial image and applying a sliding window technique, the viewing distance controller 133 can detect the presence and location of eyes. Once identified, the width between the detected eye regions can be computed.

[0043] The viewing distance controller 133 may be calibrated at step 139 wherein the user is instructed to hold the digital display 103 a set distance from the face, such as 30 cm. During such calibration, the viewing distance controller 133 calculates a reference width 144 representing a calibrated distance, whereafter the real-time viewing distance is thereafter calculated at step 140 according to the real-time width 144 and the calibrated reference width 144. At step 141 , the viewing distance controller 133 is configured to determine if the actual viewing distance exceeds the reference distance by a threshold, such as 10%, and, if so, at step 142, the viewing distance controller 133 may be configured to display a warning to the user to hold the digital display 103 closer or further away as the case may be. Figure 1 1 C shows wherein the user is too far away from the digital display 103 and therefore the visually detected facial boundary 145 is smaller than the calibrated reference width 144 whereas Figure 1 1 E shows wherein the user is too close to the digital display 103 such that the visually detected facial boundary is wider than the calibrated width 144.

[0044] In embodiments, the stimulus testing controller is configured to translate eccentricities and angular locations of the stimuli 121 to equivalent Cartesian X-Y values on the digital display 103. Furthermore, the stimulus testing controller may be configured to apply a scaling factor to the display of stimuli 121 to account for a visible tangent effect of the display of the stimuli on the digital display. In other words, the stimuli 121 may be displayed larger and with elliptical distortion further away from the fixation point 120.

[0045] With reference to Figure 1 , the controllers 105 may further comprise a gaze stability detection controller 146 configured to detect gaze stability by analysing eye image data obtained by the camera 107. Figure 6 shows processing 134 by the gaze stability detection controller 146 and Figure 7 illustrates exemplary operation thereof. At step 148, the gaze stability detection controller 146 performs gaze detection on eye image data obtained from the eye region 152 of the user captured at step 149. At step 150, the gaze stability detection controller 146 detects gaze stability and, if detecting gaze instability, displays a warning message of unstable gaze to the user at step 151 .

[0046] Figure 7 shows that the gaze stability detection controller 146 may be configured to detect image properties 153 across the eye region 152. These image properties 153 typically represent changes in image brightness, with large brightness changes detected at the edge of the iris and / or pupil. Alternatively, the image properties 153 may be colour properties. As shown, the image properties 153 may comprise image properties 153A detected across an X axis of the eye region 152 and image properties 153B across a Y axis of the eye region

[0047] 152. Figure 7 illustrates a horizontal gaze change wherein the image properties 153A change across the X axis. The gaze stability detection controller 146 may further detect gaze changes vertically using the image properties 153B across the Y axis of the eye region 152.

[0048] With reference to Figure 1 , the controllers 105 may further comprise an eye occlusion detection controller 154 configured to detect eye occlusion by analysing facial image data 143 obtained from the camera 107. The eye occlusion detection controller 154 may be used by the system 100 to ensure that the non-tested eye is closed or occluded. Figure 8 shows processing 156 by the eye occlusion detection controller 154 and Figure 9 shows exemplary operation thereof. With reference to Figure 9, the facial image data 143 may have an occluded eye region 152A (which is occluded with a patch in this embodiment) and a non-occluded eye region 152B. At step 157, the eye occlusion detection controller 154 captures the eye regions 152 from the facial image data 143 captured by the camera 107. Image processing is performed thereon at step 158 wherein, at step 159, the eye occlusion detection controller 154 detects an occluded eye. Detection of the occluded eye may comprise detection of the colour of the eyepatch or alternatively absence of eye feature recognition, or alternatively high homogeneity of pixels within the detection area due to absence of eye feature. In further embodiments, detection of an occluded eye may comprise the eye recognition only recognising one eye within the facial image data 143. At step 159, the eye occlusion detection controller 154 detects if the eye being tested is occluded and, if so, at step 160, displays a warning message to the user.

[0049] In embodiments, the controllers may comprise a screen geometry translation controller which, with reference to Figure 12, maps a stimulus 121 display from a flat digital screen to a curved digital screen, specifically focusing on mapping point A on the flat screen to point B on the curved screen. The setup involves a viewer positioned at point C, with the viewing distance denoted as d, which is the distance from point C to the flat screen. The curved screen is represented as an arc with a radius R, originating from the centre of curvature. Two primary planes are shown: the plane of the flat digital screen where point A is located and the plane of the curved digital screen where point B is located. The mapping involves calculating the new coordinates on the curved screen based on the viewing geometry. The line of sight from point C intersects the flat screen at point A and the curved screen at point B. The equation y = -d / L1 x + d represents the line of sight and xA2 + (y - R)A2 = RA2 describes the curved screen. Solving these equations simultaneously gives the coordinates of point B on the curved screen.

[0050] In embodiments, the controllers may comprise a bright spot detection controller configured to implement the process 170 shown in Figure 13, which begins with the front-facing camera scanning an image of a background scene at step 171. The system 100 then checks if any pixel's brightness exceeds a predetermined threshold level at step 172. If such a pixel is detected, the system 100 identifies the beginning of a cluster of bright pixels at step 173. This cluster is then expanded at step 174 by including adjacent pixels that also exceed the brightness threshold until the entire cluster is identified at step 175. The system 100 then assesses whether the cluster exceeds a specified size at decision 176. If the cluster is larger than the specified size, it is deemed a bright spot at step 177, and a warning message is displayed to the user. If the cluster does not exceed the specified size, the system continues scanning at step 178 for other potential bright spots.

[0051] The foregoing description, for purposes of explanation, used specific nomenclature to provide a thorough understanding of the invention. However, it will be apparent to one skilled in the art that specific details are not required in order to practise the invention. Thus, the foregoing descriptions of specific embodiments ofthe invention are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed as obviously many modifications and variations are possible in view of the above details. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, thereby enabling others skilled in the art to best utilize the invention and various embodiments with various modifications as are suited to the particular use contemplated. It is intended that the following claims and their equivalents define the scope of the invention.

Claims

Claims1 . A field of vision testing system comprising a digital display and configured for recording responses to stimuli displayed on the digital display, wherein the system is configured for screen size scaling comprising: displaying calibration markers and offset adjustment controls configured to adjust a distance therebetween; calculating a screen size scaling factor according to the distance; and scaling a screen size according to the screen size scaling factor.

2. The system as claimed in claim 1 , wherein the system is configured to calculate screen pixels per unit length according to the distance.

3. The system as claimed in claim 2, wherein the system is configured for calculating a screen size scaling factor for both X and Y axes of the digital display and scaling the screen size along the X and Y axes according to the screen size scaling factors respectively.

4. The system as claimed in claim 1 , wherein the system is configured for screen brightness calibration comprising: displaying a first stimulus at a brightness below a visual threshold and a second stimulus at a brightness above the threshold; and calculating a screen brightness according to responses to the stimuli.

5. The system as claimed in claim 4, wherein the screen brightness is classified as calibrated, inadequate and excessive.

6. The system as claimed in claim 4, wherein excessive screen brightness is detected by frequency of responses to the first stimulus exceeding a threshold.

7. The system as claimed in claim 4, wherein inadequate screen brightness is detected by frequency of responses to the second stimulus under a threshold.

8. The system as claimed in claim 4, wherein calibrated screen brightness is detected by approximately equal responses to both stimuli.

9. The system as claimed in claim 1 , wherein the system further comprises a camera and wherein the system is configured for: viewing distance calibration comprising: positioning the digital display a set distance from a user’s face; analysing image data obtained from the camera to determine a calibrated facial metric; real-time viewing distance monitoring comprising: continually monitoring image data obtained from the camera to determine a real-time facial metric; comparing the calibrated and real-time facial metrics to determine a real-time viewing distance.

10. The system as claimed in claim 9, wherein the calibrated facial metric is measured in pixel width.1 1 . The system as claimed in claim 9, wherein the calibrated facial metric comprises at least one of facial width, distance between the eyes, nose width and jawline width.

12. The system as claimed in claim 1 1 , wherein system is configured for configured for employing at least one of boundary analysis, contrast differential analysis and colour differential analysis to determine the calibrated facial metric.

13. The system as claimed in claim 1 , wherein the stimulus testing controller is configured to adjust a fixation point display location on the digital display.

14. The system as claimed in claim 13, wherein the stimulus testing controller is configured to adjust the fixation point display location depending on a size of the digital display.

15. The system as claimed in claim 14, wherein the stimulus testing controller is configured to position the fixation point display location at peripheral or corner regions of the digital display.

16. The system as claimed in claim 1 , wherein the system is configured for displaying a fixation point and scaling geometry of the stimuli proportionately according to their display distance from the fixation point.

17. The system as claimed in claim 16, wherein the geometry is stimuli are elliptical distortion.

18. The system as claimed in claim 17, wherein stimuli are displayed with greater elliptical distortion further away from the fixation point.

19. The system as claimed in claim 1 , wherein the system further comprises a camera and wherein the system is further configured to detect a gaze stability by analysing image data of a user’s eye obtained from the camera.

20. The system as claimed in claim 19, wherein the system is configured to detect image properties across at least one of an X and Y axis of an eye region.21 . The system as claimed in claim 20, wherein the image properties are at least one of brightness levels, contrast levels and colour values.

22. The system as claimed in claim 20, wherein the system is configured to detect changes in the image properties across the X axis of an eye region to detect gaze instability.

23. The system as claimed in claim 22, wherein the system is further configured to detect changes in the image properties across the Y axis of an eye region to detect gaze instability.

24. The system as claimed in claim 1 , wherein the system further comprises a camera and wherein the system is further configured to detect eye occlusion by analysing facial image data obtained from the camera.

25. The system as claimed in claim 24, wherein the system employs eye image recognition and recognition of only one eye.

26. The system as claimed in claim 1 , wherein the system is configured to translate a stimulus position from flatscreen geometry to curved screen geometry.

27. The system as claimed in claim 26, wherein the system is configured to calculate line of sight intersections of the flatscreen geometry and the curved screen geometry.

28. The system as claimed in claim 1 , wherein the system further comprises a front facing camera and wherein the system is further configured to detect bright spots within image data obtained from the camera.

29. The system as claimed in claim 28, wherein the system is configured to: scan an image of the background scene, determine if any pixel's brightness exceeds a predetermined threshold, if a bright pixel is detected, identify a start of a cluster of bright pixels; expand the cluster by including adjacent pixels exceeding the brightness threshold until; determining if the cluster exceeds a specified size.

30. The system as claimed in claim 29, wherein the system is further configured to display a warning message if the if the cluster exceeds the specified size.

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