Patent Publication Number: US-2018033133-A1

Title: Surface Analysis Systems and Methods of Identifying Visible Surfaces Using the Same

Description:
TECHNICAL FIELD 
     Embodiments described herein generally relate to surface analysis systems and, more specifically, methods and systems for identifying one or more visible surfaces positioned in an observation environment, such as a vehicle. 
     BACKGROUND 
     When designing a product, it may be useful for a designer to know which surfaces of the product, for example, a vehicle, will be visible to a consumer, such that the aesthetic design of these surfaces may be prioritized. 
     Accordingly, a need exists for systems and methods for identifying one or more visible surfaces of a product, for example, a vehicle, and determining the likelihood of observation of each of these visible surfaces. 
     SUMMARY 
     In one embodiment, a surface analysis system includes a sensor for generating data regarding a location of an object, one or more processors communicatively coupled to the sensor, one or more memory modules communicatively coupled to the one or more processors, and machine readable instructions stored in the one or more memory modules that cause the surface analysis system to perform at least the following when executed by the one or more processors: measure a plurality of head locations of a head of an observer within an observation environment during an observation period using the sensor. The one or more surfaces are positioned in the observation environment. Further, the machine readable instructions cause the surface analysis system to identify one or more visible surfaces of the one or more surfaces positioned in the observation environment based on the plurality of head locations measured during the observation period. Moreover, the one or more visible surfaces include at least one of the one or more surfaces positioned in the observation environment and the one or more visible surfaces are positioned unobstructed from at least one head location of the observer measured during the observation period. 
     In another embodiment, a method of identifying visible surfaces within an observation environment includes measuring, using a sensor configured to generate data regarding a location of an object, a plurality of head locations of a head of an observer within an observation environment during an observation period. The one or more surfaces are positioned in the observation environment. The method further includes identifying one or more visible surfaces of the one or more surfaces positioned in the observation environment based on the plurality of head locations measured during the observation period. Moreover, the one or more visible surfaces include at least one of the one or more surfaces positioned in the observation environment and the one or more visible surfaces are positioned unobstructed from at least one head location of the observer measured during the observation period. 
     In yet another embodiment a surface analysis system includes a sensor for generating data regarding an orientation of an object, one or more processors communicatively coupled to the sensor, one or more memory modules communicatively coupled to the one or more processors, and machine readable instructions stored in the one or more memory modules that cause the surface analysis system to perform at least the following when executed by the one or more processors: measure a plurality of head orientations of a head of an observer within an observation environment during an observation period using the sensor. Each head orientation of the plurality of head orientations corresponds with a field of view extending from the head of the observer into the observation environment and one or more surfaces are positioned in the observation environment. Further, the machine readable instructions cause the surface analysis system to identify one or more visible surfaces of the one or more surfaces positioned in the observation environment based on the plurality of head orientations measured during the observation period. The one or more visible surfaces include at least one of the one or more surfaces positioned in the observation environment and the one or more visible surfaces are within a field of view corresponding with at least one head orientation of the observer measured during the observation period. 
     These and additional features provided by the embodiments of the present disclosure will be more fully understood in view of the following detailed description, in conjunction with the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments set forth in the drawings are illustrative and exemplary in nature and not intended to limit the disclosure. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals and in which: 
         FIG. 1  schematically depicts an surface analysis system, according to one or more embodiments shown and described herein; 
         FIG. 2  depicts an example observation environment comprising a vehicle, according to one or more embodiments shown and described herein; 
         FIG. 3A  schematically depicts a top view of an observer located in an example observation environment including one or more surfaces, according to one or more embodiments shown and described herein; 
         FIG. 3B  schematically depicts a side view of the observer located in the example observation environment of  FIG. 3A , according to one or more embodiments shown and described herein; 
         FIG. 4A  schematically depicts a top view of a head location probability cloud corresponding to a location of an observer&#39;s head in an observation environment, according to one or more embodiments shown and described herein; 
         FIG. 4B  schematically depicts a side view of the head location probability cloud corresponding to the location of the observer&#39;s head in the observation environment of  FIG. 4A , according to one or more embodiments shown and described herein; 
         FIG. 5A  schematically depicts a top view of a visibility polygon corresponding to a location of an observer&#39;s head in an observation environment, according to one or more embodiments shown and described herein; 
         FIG. 5B  schematically depicts a side view of the visibility polygon corresponding to the location of the observer&#39;s head in the observation environment of  FIG. 5A , according to one or more embodiments shown and described herein; 
         FIG. 6A  schematically depicts a top view of a surface observation probability map corresponding to one or more surfaces of an example observation environment, according to one or more embodiments shown and described herein; 
         FIG. 6B  schematically depicts a side view of the surface observation probability map corresponding to the one or more surfaces of the example observation environment of  FIG. 6A , according to one or more embodiments shown and described herein; 
         FIG. 7A  schematically depicts a top view of an observer located in another example observation environment including one or more surfaces, according to one or more embodiments shown and described herein; 
         FIG. 7B  schematically depicts a side view of the observer located in the example observation environment of  FIG. 7A , according to one or more embodiments shown and described herein; and 
         FIG. 8  depicts a flow diagram of a method of identifying one or more visible surfaces in an observation environment using the surface analysis system, according to one or more embodiments shown and described herein. 
     
    
    
     DETAILED DESCRIPTION 
     The embodiments disclosed herein include a surface analysis system for identifying visible surfaces of parts positioned in an observation environment, such as visible surfaces of parts of a vehicle, by observing an observer positioned in the observation environment. The surface analysis system may measure a plurality of head locations of the observer during one or more observation periods using one or more sensors. Further, the surface analysis system may identify the one or more visible surface by determining which surfaces are positioned unobstructed from at least one of these head locations. The one or more sensors may be image sensors, proximity sensors, and/or motion capture sensors and may interact with one or more motion trackers located on the observer to determine the head location of the head of the observer. In some embodiments, the surface analysis system may also determine the surface observation probability of each of the surfaces positioned in the observation environment. By identifying visible surfaces and surface observation probabilities, the surface analysis system allows a designer and manufacturer of the parts to prioritize and improve the design, manufacture, and assembly of these highly visible parts and part surfaces. The surface analysis system and will now be described in more detail herein with specific reference to the corresponding drawings. 
     Referring now to  FIG. 1 , an embodiment of a surface analysis system  100  is schematically depicted. The surface analysis system  100  includes one or more processors  102 . Each of the one or more processors  102  may be any device capable of executing machine readable instructions. Accordingly, each of the one or more processors  102  may be a controller, an integrated circuit, a microchip, a computer, or any other computing device. For example, the one or more processors  102  may be processors of a computing device  105 . The one or more processors  102  are coupled to a communication path  104  that provides signal interconnectivity between various components of the surface analysis system  100 . Accordingly, the communication path  104  may communicatively couple any number of processors  102  with one another, and allow the components coupled to the communication path  104  to operate in a distributed computing environment. As used herein, the term “communicatively coupled” means that coupled components are capable of exchanging data signals with one another such as, for example, electrical signals via conductive medium, electromagnetic signals via air, optical signals via optical waveguides, and the like. 
     Accordingly, the communication path  104  may be formed from any medium that is capable of transmitting a signal such as, for example, conductive wires, conductive traces, optical waveguides, or the like. In some embodiments, the communication path  104  may facilitate the transmission of wireless signals, such as WiFi, Bluetooth, and the like. Moreover, the communication path  104  may be formed from a combination of mediums capable of transmitting signals. In one embodiment, the communication path  104  comprises a combination of conductive traces, conductive wires, connectors, and buses that cooperate to permit the transmission of electrical data signals to components such as processors, memories, sensors (e.g., sensors  112  described herein), input devices, output devices, and communication devices. Accordingly, the communication path  104  may comprise a vehicle bus, such as for example a LIN bus, a CAN bus, a VAN bus, and the like. Additionally, it is noted that the term “signal” means a waveform (e.g., electrical, optical, magnetic, mechanical or electromagnetic), such as DC, AC, sinusoidal-wave, triangular-wave, square-wave, vibration, and the like, capable of traveling through a medium. 
     Moreover, the surface analysis system  100  includes one or more memory modules  106  coupled to the communication path  104 . The memory modules  106  may be one or more memory modules of the computing device  105 . Further, the one or more memory modules  106  may comprise RAM, ROM, flash memories, hard drives, or any device capable of storing machine readable instructions such that the machine readable instructions can be accessed by the one or more processors  102 . The machine readable instructions may comprise logic or algorithm(s) written in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL) such as, for example, machine language that may be directly executed by the processor, or assembly language, object-oriented programming (OOP), scripting languages, microcode, etc., that may be compiled or assembled into machine readable instructions and stored on the one or more memory modules  106 . Alternatively, the machine readable instructions may be written in a hardware description language (HDL), such as logic implemented via either a field-programmable gate array (FPGA) configuration or an application-specific integrated circuit (ASIC), or their equivalents. Accordingly, the methods described herein may be implemented in any conventional computer programming language, as pre-programmed hardware elements, or as a combination of hardware and software components. 
     Referring still to  FIG. 1 , the surface analysis system  100  comprises a display  108  for providing visual output such as, visual depictions of sensor data, probability clouds ( FIGS. 4A and 4B ), visibility polygons ( FIGS. 5A and 5B ), surface observation probability maps ( FIGS. 6A and 6B ), or the like. The display  108  is coupled to the communication path  104 . Accordingly, the communication path  104  communicatively couples the display  108  to other components of the surface analysis system  100 . The display  108  may include any medium capable of transmitting an optical output such as, for example, a cathode ray tube, light emitting diodes, a liquid crystal display, a plasma display, or the like. In some embodiments, the display  108  may comprise a display of the computing device  105 . Moreover, the display  108  may be a touchscreen that, in addition to providing optical information, detects the presence and location of a tactile input upon a surface of or adjacent to the display. Accordingly, each display may receive mechanical input directly upon the optical output provided by the display. 
     Referring now to  FIGS. 1 and 2 , the surface analysis system  100  further comprises one or more sensors  112 , for example, one or more of an image sensor  114 , a proximity sensor  116 , and/or a motion capture sensor  118 . In operation, each of the one or more sensors  112  may be configured to generate data regarding a location (e.g., a spatial location) and, in some embodiments, an orientation of an object, for example, a head  122  of an observer  120  positioned in an observation environment  130  ( FIGS. 2A, 2B, 6A, and 6B ). In some embodiments, the surface analysis system  100  may further comprise one or more tracking markers  115  configured to be worn by the observer  120 . In operation, the one or more tracking markers  115  may interact with the one or more sensors  112  to generate data regarding a location and/or orientation of the observer  120  (e.g., the head  122  of the observer  120 ). 
     As depicted in  FIG. 1 , the image sensor  114  is coupled to the communication path  104  such that the communication path  104  communicatively couples the image sensor  114  to other components of the surface analysis system  100 . The image sensor  114  may comprise any imaging device configured to capture image data of the observation environment  130  and the observer  120  positioned in the observation environment  130 . The image data may digitally represent at least a portion of the observation environment  130  or the observer  120 , for example, the head  122  of the observer  120 . In operation, the image sensor  114  may interact with the one or more tracking markers  115  when the one or more tracking markers  115  are worn by the observer  120 , to determine the location of the observer  120  (e.g., the spatial location of the head  122  of the observer  120 ) and, in some embodiments, the orientation of the head  122  of the observer  120  (e.g., a pointing direction of a face  124  of the observer  120 ). 
     The image sensor  114  may comprise any sensor operable to capture image data, such as, without limitation, a charged-coupled device image sensors or complementary metal-oxide-semiconductor sensors capable of detecting optical radiation having wavelengths in the visual spectrum, for example. The image sensor  114  may be configured to detect optical radiation in wavelengths outside of the visual spectrum, such as wavelengths within the infrared spectrum. In some embodiments, two or more image sensors  114  are provided to generate stereo image data capable of capturing depth information. Moreover, in some embodiments, the image sensor  114  may comprise a camera, which may be any device having an array of sensing devices (e.g., pixels) capable of detecting radiation in an ultraviolet wavelength band, a visible light wavelength band, or an infrared wavelength band. 
     Still referring to  FIG. 1 , the proximity sensor  116  is communicatively coupled to the communication path  104  such that the communication path  104  communicatively couples the proximity sensor  116  to other components of the surface analysis system  100 . The proximity sensor  116  may be any device capable of outputting a proximity signal indicative of a proximity of an object to the proximity sensor  116 . In some embodiments, the proximity sensor  116  may include a laser scanner, a capacitive displacement sensor, a Doppler effect sensor, an eddy-current sensor, an ultrasonic sensor, a magnetic sensor, an optical sensor, a radar sensor, a sonar sensor, or the like. Some embodiments may not include the proximity sensor  116 . In operation, the proximity signal may be used to determine the location of the observer  120  and in some embodiments, the orientation of the observer  120 . For example, the proximity sensor  116  may interact with the one or more tracking markers  115  when the one or more tracking markers  115  are worn by the observer  120 , to determine the location of the observer  120  (e.g., the spatial location of the head  122  of the observer  120 ) and, in some embodiments, the orientation of the head  122  of the observer  120  (e.g., the pointing direction of the face  124  of the observer  120 ). 
     Further, the motion capture sensor  118  is communicatively coupled to the communication path  104  such that the communication path  104  communicatively couples the motion capture sensor  118  to other components of the surface analysis system  100 . The motion capture sensor  118  comprises one or more sensors that are wearable by the observer  120  and are configured to measure the spatial location and/or the orientation of the observer  120 . For example, the motion capture sensor  118  may comprise an inertial sensor having an inertial measurement unit (IMU). For example, the IMU may include a gyroscope, a magnetometer, and an accelerometer. Further, the motion capture sensor  118  may comprise one or more RF sensors configured to transmit an RF signal regarding the spatial location and/or orientation of the head  122  of the observer  120 . Moreover, the motion capture sensor  118  may comprise one or more magnetic sensors configured to transmit a magnetic signal regarding the spatial location and/or orientation of the head  122  of the observer  120 . 
     Referring now to  FIG. 2 , the one or more sensors  112  and/or one or more tracking markers  115  may be coupled to a wearable device  140  configured to be worn by the observer  120 , for example, eyeglasses  142 , headwear  144 , or any other wearable device configured to monitor the position and/or orientation of the head  122  of the observer  120 . Further, the one or more tracking markers  115  may be directly coupled to the observer  120 , for example, using an adhesive or a fastening mechanism. As a non-limiting example, the one or more sensors  112 , for example, image sensors  114  and/or proximity sensors  116  may be positioned in the observation environment  130  apart from the observer  120  and the one or more tracking markers  115  may be positioned on the head  122  of the observer  120  using the wearable device  140  or by directly coupling the one or more tracking markers  115  to the head  122  of the observer  120 . As another non-limiting example, the motion capture sensors  118  may be coupled to the observer  120  and/or the wearable device  140  and may measure the location and/or orientation of the head of the observer  120  without use of additional sensors  112 . In operation, the sensors  112  may monitor the observer  120 , for example, by monitoring the tracking markers  115  and may generate sensor data regarding the location and or orientation of the head of the observer  120 . 
     Referring still to  FIG. 2 , an embodiment of the observation environment  130  comprising a vehicle  150  is depicted. The observation environment  130  (e.g., the vehicle  150 ) includes one or more component parts  132  each comprising one or more surfaces  134 . For example, the one or more component parts  132  may comprise one or more interior vehicle parts such as a seat  154 , a dashboard  158 , a steering wheel  152 , a central storage console  155 , one or more interior panels, a vehicle floor, or the like. Further, the one or more component parts  132  may comprise one or more exterior vehicle parts, for example, one or more doors, a hood, a wheel, a bumper, one or more exterior vehicle panels, or the like. Moreover, the one or more surfaces  134  may comprise surfaces of any vehicle part, for example, the above described vehicle parts. While the observation environment  130  is described herein as including the vehicle  150  and the one or more surfaces  134  are described as vehicle part surfaces, it should be understood that the surface analysis system  100  may analyze surfaces in any observation environment  130 . 
     As depicted in  FIG. 2 , the observer  120  may be positioned in the observation environment  130 , for example, in the vehicle  150 . In the embodiment depicted in  FIG. 2 , the observer  120  may be a driver  121  of the vehicle  150  or a passenger  123  in the vehicle  150 . In operation, the one or more sensors  112  monitor the observer  120  during one or more observation periods. An individual observation period may comprise any period of time. As a non-limiting example, the observation period may comprise between about 1 minute and about 120 minutes, for example 5 minutes, 15 minutes, 30 minutes, 60 minutes, 90 minutes, or the like. Further, when the observation environment  130  comprises the vehicle  150 , individual observation periods may comprise a period of time corresponding with operation of the vehicle  150  and the one or more sensors  112  may monitor the observer  120  while the observer  120  (e.g., the driver  121 ) is driving the vehicle  150  or while the observer  120  is riding as the passenger  123  of the vehicle  150 . 
     During the observation period, the one or more sensors  112  may measure one or more head locations  160  ( FIGS. 3A and 3B ) of the head  122  of the observer  120  and, in some embodiments, measure one or more head orientations  162  ( FIGS. 4A and 4B ) of the head  122  of the observer  120 . The one or more sensors  112  may output sensor data to the one or more processors  102  of the surface analysis system  100 , for example, head location data and/or head orientation data. Using the head location data and/or the head orientation data generated during the observation period, the one or more processors  102  of the surface analysis system  100  may identify which of the one or more surfaces  134  are visible to the observer  120  and determine the probability that an individual surface of the one or more surfaces  134  is visible from any one individual head location  160  and/or any one individual head orientation  162  of the observer  120 . Moreover, the surfaces  134  described in the embodiments and examples herein may be full surfaces, or segments (e.g., portions) of full surfaces. Thus, it should be understood that the surfaces  134  may refer to any surface segment of the one or more component parts  132 . Identifying the surfaces that are visible from at least one head location  160  or head orientation  162  and determining the likelihood of visibility of each surface  134  provides information for a manufacturer of the one or more component parts  132  (e.g., a manufacturer of the vehicle  150 ) regarding the likelihood that a user will see the component parts  132 . This information allows the manufacturer to prioritize manufacturing and installation resources to parts and surfaces that are highly visible, improving the visible quality of these parts. 
     Referring now to  FIGS. 3A-7B , example observation environments  130  are depicted to help illustrate the operation of the surface analysis system  100 .  FIG. 3A  schematically depicts a top view of the observer  120  and the plurality of component parts  132  each comprising at least one surface  134 , for example, a first surface  134   a , a second surface  134   b , and a third surface  134   c . Further,  FIG. 3A  depicts multiple head locations  160  of the observer  120  along an X-Y coordinate plane, for example, a first head location  160   a , a second head location  160   b , and a third head location  160   c .  FIG. 3B  schematically depicts a side view of the observer  120  and the plurality of component parts  132  each comprising at least one surface  134 , for example, a fourth surface  134   d , a fifth surface  134   e , and a sixth surface  134   f . Further,  FIG. 3B  depicts multiple head locations along a Y-Z coordinate plane, for example, a fourth head location  160   d  and a fifth head location  160   e.    
     In the embodiments of  FIGS. 3A and 3B , each surface  134  that is positioned unobstructed from at least one head location  160  measured during one or more observation periods is a visible surface and each surface that is positioned obstructed from each head location  160  measured during the observation period is an obstructed surface (e.g., a non-visible surface). Further, it should be understood that while a limited number of head locations  160  are depicted in  FIGS. 3A and 3B , any number of head locations  160  may be measured during the observation period or during multiple observation periods. 
     In the example embodiment depicted in  FIG. 3A , when the head  122  of the observer  120  is located in the first head location  160   a , the first surface  134   a  is visible while the second surface  134   b  and the third surface  134   c  are obstructed. In the second head location  160   b , the first surface  134   a  and the second surface  134   b  are visible and the third surface  134   c  is obstructed. Moreover, in the third head location  160   c , the first surface  134   a  is visible and the second surface  134   b  and the third surface  134   c  are obstructed. Thus, in the example embodiment depicted in  FIG. 3A , the first surface  134   a  and the second surface  134   b  are visible because they are each positioned unobstructed from at least one head location  160  and the third surface  134   c  is not visible because it is obstructed from each of the first, second, and third head locations  160   a - 160   c . In particular, an obstruction, such as one or more additional component parts, is positioned between the third surface  134   c  and each of the head locations  160 . 
     In the example embodiment depicted in  FIG. 3B , when the head  122  of the observer  120  is located in the fourth head location  160   d , the fourth surface  134   d  is visible while the fifth surface  134   e  and the sixth surface  134   f  are obstructed. Further, when the head  122  of the observer  120  is located in the fifth head location  160   e , the fourth surface  134   d  and the fifth surface  134   e  are visible while the sixth surface  134   f  is obstructed. Thus, in the example embodiment depicted in  FIG. 3B , the fourth surface  134   d  and the fifth surface  134   e  are visible because they are each positioned unobstructed from at least one head location  160  and the sixth surface  134   f  is not visible because it is obstructed from each of the fourth head location  160   d  and the fifth head location  160   e.    
     Referring still to  FIGS. 3A and 3B , the surface analysis system  100  may also determine a surface observation probability of the one or more surfaces  134  during the observation period. The surface observation probability is the probability that an individual surface  134  is visible to the observer  120  having any one individual head location  160  of the plurality of head locations  160  measured by the one or more sensors  112  during the observation period. In the example observation environment  130  depicted in  FIG. 3A , the first surface  134   a  has a higher surface observation probability than the second surface  134   b  because the first surface  134   a  is visible from each of the first, second, and third head locations  160   a - 160   c  and the second surface  134   b  is visible from the first and third head locations  160   a  and  160   c  but is not visible from the second head location  160   b . Thus, in this example, the first surface  134   a  comprises a surface observation probability of about 100% and the second surface  134   b  comprises a surface observation probability of about 66%. 
     Moreover, in the example observation environment  130  depicted in  FIG. 3B , the fourth surface  134   d  has a higher observation probability than the fifth surface  134   e  because the fourth surface  134   d  is visible from both the fourth head location  160   d  and the fifth head location  160   e  and the fifth surface  134   e  is visible from the fifth head location  160   e  but is not visible from the fourth head location  160   f . Thus, in this example, the fourth surface  134   d  comprises a surface observation probability of about 100% and the fifth surface  134   e  comprises a surface observation probability of about 50%. 
     In operation, the surface analysis system  100  may also determine a plurality of head location probabilities corresponding to the plurality of head locations  160  measured during the observation period. Each individual head location probability comprises a probability that the head  122  of the observer  120  is located in an individual spatial location within the observation environment  130  at a discrete observation point (e.g., moment) during the observation period based on the plurality of head locations  160  measured during the observation period. For example, each individual head location probability is the probability that at any one point during the observation period, the head  122  of the observer  120  will be located at the individual head location  160  corresponding with the individual head location probability. 
     Referring to  FIG. 3A , as a non-limiting example, if the one or more sensors  112  measure the head  122  of the observer  120  in the first head location  160   a  more often than the second head location  160   b , the first head location  160   a  would have a higher head location probability than the second head location  160   b . As another non-limiting example, the head location probability may be determined by first determining an average head location of the head  122  of the observer  120  during the observation period and then measuring the distance between the average head location of the head  122  of the observer  120  between both the first head location  160   a  and the second head location  160   b . If the average head location is closer to the first head location  160   a  than the second head location  160   b , than the first head location probability is greater than the second head location probability. 
     Referring now to  FIGS. 4A and 4B , the surface analysis system  100  may also generate a head location probability cloud  180  based on the plurality of head locations  160  measured during the observation period. The head location probability cloud  180  corresponds with the plurality of head location probabilities of the head  122  of the observer  120  and is a visual depiction of the head location probability. In particular, the head location probability cloud  180  is a visual depiction of the probability that the head  122  of the observer  120  will be positioned in a specific head location  160  at a discrete observation point (e.g., moment) during the observation period. The head location probability cloud  180  depicted in  FIGS. 4A and 4B  includes a high density region  182 , an intermediate density region  184 , and a low density region  186 . In some embodiments, the head location probability cloud  180  may be displayed on the display  108 . 
     The high density region  182  corresponds with locations within the observation environment  130  in which the head  122  of the observer  120  is most frequently measured during one or more observation periods. For example, when the observation environment  130  is the vehicle  150  and the observer  120  is the driver  121  of the vehicle  150  ( FIG. 2 ), the high density region  182  may be a region between the steering wheel  152  of the vehicle  150  and the headrest  156  of a seat  154  of the vehicle  150  ( FIG. 2 ). Further, the low density region  186  corresponds with locations within the observation environment  130  in which the head  122  of the observer  120  is least frequently measured during the one or more observation periods. For example, when the observation environment  130  is the vehicle  150  and the observer  120  is the driver  121  of the vehicle  150 , the low density region  186  may comprise a region below the steering wheel  152  ( FIG. 2 ). The intermediate density region  184  corresponds with locations within the observation environment  130  in which the head  122  of the observer  120  is more often located than in the low density region  186  and less often located than in the high density region  182 . Further, regions of the observation environment  130  that are not within the head location probability cloud  180  correspond with regions within the observation environment  130  where the head  122  is not located during the one or more observation periods. 
     Referring now to  FIGS. 5A-5B , the surface analysis system  100  may also generate a plurality of visibility polygons  190  corresponding with the one or more head locations  160  of the head  122  of the observer  120  measured during the observation period. An individual visibility polygon  190  corresponds with an individual head location  160  and includes a visible region  192  (shaded in  FIGS. 5A and 5B ) and an obstructed region  194  (not shaded in  FIGS. 5A and 5B ). Surfaces  134  within the visible region  192  are visible surfaces and surfaces  134  within the obstructed region  194  are obstructed surfaces. In operation, the surface analysis system  100  may use the plurality of visibility polygons  190  to identify which surfaces  134  are visible and which surfaces  134  are obstructed. Further, the surface analysis system  100  may determine the surface observation probability of each of the one or more surfaces  134  using the plurality of visibility polygons  190 . In some embodiments, the plurality of visibility polygons  190  may be displayed on the display  108 . 
       FIG. 5A  depicts a first visibility polygon  190 ′ of an individual head location  160 ′ within the observation environment  130  depicted in  FIGS. 3A and 4A . The first visibility polygon  190 ′ extends outward from the individual head location  160 ′ and includes a first visible region  192 ′ and a first obstructed region  194 ′. In the example shown in  FIG. 5A , the first visibility polygon  190 ′ shows that from the individual head location  160 ′, the first surface  134   a  and the third surface  134   c  are visible, while the second surface  134   b  is obstructed.  FIG. 5B  depicts a second visibility polygon  190 ″ of an individual head location  160 ″ within the observation environment  130  depicted in  FIGS. 3B and 4B . The second visibility polygon  190 ″ extends outward from the individual head location  160 ″ and includes a second visible region  192 ″ and a second obstructed region  194 ″. In this non-limiting example, the second visibility polygon  190 ″ shows that from the individual head location  160 ″, the fourth surface  134   d  is visible, while the fifth surface  134   e  and the sixth surface  134   f  are obstructed. 
     Referring now to  FIGS. 6A and 6B , the surface analysis system  100  may generate a surface observation probability map  170  of the one or more surfaces  134  located in the observation environment  130 , for example, one or more surfaces  134  of the vehicle  150 . The surface observation probability map  170  provides a visual depiction of the surface observation probability and each surface  134  depicted in the surface observation probability map  170 . Further, the surface observation probability map  170  depicts the probability that each surface  134  will be visible from a specific head location  160  at a discrete observation point (e.g., moment) during the observation period. The surface observation probability map  170  is based on the surface observation probability of each surface  134 , and may additionally be based on the head location probability cloud  180  and the plurality of visibility polygons  190 . In some embodiments, the surface observation probability map may be displayed on the display  108 . 
     The surface observation probability map  170  depicted in  FIGS. 6A and 6B  includes a high observation probability region  172 , an intermediate observation probability region  174 , and a low observation probability region  176 . The high observation probability region  172  corresponds with surfaces  134  of the component parts  132  which are most frequently visible during the one or more observation periods. In the non-limiting example depicted in  FIGS. 6A and 6B , the first surface  134   a  ( FIG. 6A ) and the fourth surface  134   d  ( FIG. 6B ) are high observation probability regions  172 . As another non-limiting example, when the component parts  132  are parts of the vehicle  150  and the observer  120  is the driver  121  of the vehicle  150  ( FIG. 2 ), the steering wheel  152  of the vehicle  150  ( FIG. 2 ) may comprise an example high observation probability region  172 . 
     The low observation probability region  176  corresponds with surfaces  134  of the component parts  132  which are least frequently visible during the one or more observation periods. In the non-limiting example depicted in  FIGS. 6A and 6B , the sixth surface  134   f  ( FIG. 6B ) is a low observation probability region  176 . As another non-limiting example, when the component parts  132  are parts of the vehicle  150  and the observer  120  is the driver  121  of the vehicle  150  ( FIG. 2 ), surfaces below the seat  154  may comprise example low observation probability regions  176 . The intermediate observation probability region  174  corresponds with surfaces  134  of the component parts  132  which are more often visible during the one or more observation periods than the surfaces  134  corresponding with the low observation probability region  176  and less often visible during the one or more observation periods than the surfaces  134  corresponding with the high observation probability region  172 . In the non-limiting example depicted in  FIGS. 6A and 6B , the second surface  134   b  ( FIG. 6A ), the third surface  134   c  ( FIG. 6A ), and the fifth surface  134   e  ( FIG. 6B ) are intermediate observation probability regions  174 . As another non-limiting example, when the component parts  132  are parts of the vehicle  150  and the observer  120  is the driver  121  of the vehicle  150  ( FIG. 2 ), the central storage console  155  of the vehicle  150  ( FIG. 2 ) may comprise an example intermediate observation probability region  174 . 
     Moreover, in some embodiments, the high observation probability region  172 , the intermediate observation probability region  174 , and the low observation probability region  176  may each correspond with a percentage range of observation probabilities. As one non-limiting example, the high observation probability region  172  corresponds with an observation probability of from about 67% to about 100%, the intermediate observation probability region  174  corresponds with an observation probability of from about 34% to about 66%, and the low observation probability region  176  corresponds with an observation probability of from about 0% to about 33%. 
     Referring still to  FIGS. 6A and 6B , in some embodiments, the surface observation probability map  170  comprises a color map such that the high observation probability region  172 , the intermediate observation probability region  174 , and the low observation probability region  176  may each be represented by a different color in the surface observation probability map  170 . For example the high observation probability region  172  may be red, the intermediate observation probability region  174  may be yellow, and the low observation probability region  176  may be blue. Further, in some embodiments, the surface observation probability map  170  may depict a visual gradient of colors or other visual indicators (e.g., patterns, shadings, or the like) which correspond with the surface observation probability of each surface  134 . 
     Referring now to  FIGS. 7A and 7B , in some embodiments, the head orientation  162  of the observer  120  may be monitored by the one or more sensors  112 . The head orientation  162  of the observer  120  corresponds with a field of view  126  extending outward from the face  124  of the observer  120 , for example, extending in the pointing direction of the face  124  of the observer  120 . Further, the field of view  126  corresponds with a region within the observation environment  130  that is visible to the observer  120  having the individual head orientation  162 . While not intending to be limited by theory, the field of view  126  of the observer  120  (e.g., the field of view of an average human) may extend horizontally (e.g., in the X-Y plane of  FIG. 7A ) about 120° and may extend vertically (e.g., in the Y-Z plane of  FIG. 7B ) about 120°. In operation, the surfaces  134  that are within the field of view  126  of the observer  120  corresponding with at least one head orientation  162  measured during one or more observation periods are visible surfaces and the surfaces  134  that are not within the field of view  126  corresponding with any head orientation  162  measured during one or more observation periods are obstructed surfaces, e.g., not visible surfaces. 
       FIG. 7A  schematically depicts a top view of the observer  120  and the plurality of component parts  132  each comprising at least one surface  134  positioned in the observation environment  130  of  FIGS. 3A, 4A, 5A, and 6A .  FIG. 7A  depicts multiple head orientations  162  of the observer  120  along the X-Y plane, for example, a first head orientation  162   a  and a second head orientation  162   b .  FIG. 7B  schematically depicts a side view of the observer  120  and the plurality of component parts  132  each comprising at least one surface  134 . Further,  FIG. 7B  depicts multiple head orientations along the Y-Z plane, for example, a third head orientation  162   c  and a fourth head orientation  162   d.    
     In the example embodiment depicted in  FIG. 7A , when the head  122  of the observer  120  is in the first head orientation  162   a , a first field of view  126   a  extends outward from the face  124  of the observer  120  and when the head  122  of the observer  120  is in the second head orientation  162   b , a second field of view  126   b  extends outward from the face  124  of the observer  120 . As depicted in  FIG. 7A , in both the first head orientation  162   a  and the second head orientation  162   b , both the first surface  134   a  and the third surface  134   c  are visible and the second surface  134   b  is obstructed. Further, a larger portion of the first surface  134   a  is visible in the second head orientation  162   b  than is visible in the first head orientation  162   a.    
     In the example embodiment depicted in  FIG. 7B , when the head  122  of the observer  120  is in the third head orientation  162   c , a third field of view  126   c  extends outward from the face  124  of the observer  120  and when the head  122  of the observer  120  is in the fourth head orientation  162   d , a fourth field of view  126   d  extends outward from the face  124  of the observer  120 . As depicted in  FIG. 7B , in the third head orientation  162   c , the fifth surface  134   e  is visible, while the fourth surface  134   d  and the sixth surface  134   f  are each obstructed. Further, in the fourth head orientation  162   d , the fourth surface  134   d  and the fifth surface  134   e  are both visible, while the sixth surface  134   f  is obstructed. Further, a larger portion of the fifth surface  134   e  is visible in the fourth head orientation  162   d  than in the third head orientation  162   c.    
     In some embodiments, the surface analysis system  100  may determine the surface observation probability of at least one of the one or more surfaces  134  based on the one or more head orientations  162  measured during the one or more observation periods. In this embodiment, the surface observation probability is a probability that an individual surface  134  is visible to the observer  120  having any one individual head orientation  162  of the plurality of head orientations  162 . The surface observation probability determined in this embodiment may also be used to generate a surface observation probability map  170  of the one or more surfaces  134  located in the observation environment  130 , as described above. 
     Further, the surface analysis system  100  may determine a plurality of head orientation probabilities, each comprising a probability that the head of the observer is in an individual head orientation  162  at a discrete observation point during the observation period based on the plurality of head orientations  162  measured during the observation period. Moreover, in some embodiments, the surface analysis system  100  may generate one or more visibility polygons  190 , similar to the visibility polygons  190  depicted in  FIGS. 5A and 5B , which correspond with one or more head orientations  162 . In this embodiment, the visible region  192  of the visibility polygon  190  comprises the regions within the observation environment  130  that are within the field of view  126  of the observer  120  and the obstructed region  194  of the visibility polygon  190  comprises the regions within the observation environment  130  that are outside of the field of view  126  of the observer  120 . 
     Referring now to  FIG. 8 , a flow chart  10  depicting a method for identifying one or more visible surfaces in the observation environment  130  is illustrated. The flow chart  10  depicts a number of method steps illustrated by boxes  12 - 24 . While the method is described in a particular order, it should be understood that other orders are contemplated. First, at box  12 , the method comprises monitoring the observer  120  located within the observation environment  130  during the one or more observation periods, for example, using the one or more sensors  112 . Monitoring the observer  120  may comprise measuring, using the one or more sensors  112 , a plurality of head locations  160  ( FIGS. 3A and 3B ) of the head  122  of the observer  120  during the one or more observation periods. Further, in some embodiments, monitoring the observer  120  may comprise measuring, using the one or more sensors  112 , a plurality of head orientations  162  ( FIGS. 7A and 7B ) of the head  122  of the observer  120  positioned within the observation environment  130  during the one or more observation periods. 
     Next, at box  14 , the method may include determining a plurality of head location probabilities based on the plurality of head locations  160  observed during the observation period. As stated above, each individual head location probability comprises a probability that the head  122  of the observer  120  is in an individual location within the observation environment  130  at a discrete observation point during one or more observation periods. In some embodiments, at box  16 , the method further includes determining a plurality of head orientation probabilities based on the plurality of head orientations observed during the observation period. As stated above, each individual head orientation probability comprises a probability that the head of the observer is in an individual head orientation  162  at a discrete observation point during the observation period. Further, at box  18 , the method may include generating the head location probability cloud  180  ( FIGS. 4A and 4B ) based on the plurality of head locations  160  measured during the observation period and corresponding with the plurality of head location probabilities of the head  122  of the observer  120 . Moreover, at box  20 , the method may include generating the one or more visibility polygons  190  ( FIGS. 5A and 5B ) corresponding with the one or more head locations  160  of the head  122  of the observer  120  measured during the observation period. 
     Referring still to  FIG. 8 , at box  22 , the method may next include identifying one or more visible surfaces of the one or more surfaces  134  positioned in the observation environment  130  based on one or both of the plurality of head locations  160  measured during the one or more observation periods and the plurality of head orientations  162  measured during the one or more observation periods. In some embodiments, the one or more visible surfaces may comprise the one or more surfaces  134  positioned unobstructed from at least one head location  160  of the observer  120  measured during the one or more observation periods (e.g., when the visible surfaces are identified using the plurality of head locations  160 ). Further, in some embodiments, the one or more visible surfaces may comprise the one or more surfaces  134  positioned within a field of view  126  corresponding with at least one head orientation  162  of the observer measured during the observation period (e.g., when the visible surfaces using the plurality of head orientations  162 ). Moreover, at box  24 , the method may include generating the surface observation probability map  170  based on the surface observation probability of each surface  134 , and in some embodiments, based on the head location probability cloud  180  and the plurality of visibility polygons  190 . 
     It should be understood that embodiments described herein provide for surface analysis systems for identifying visible surfaces of parts positioned in an observation environment, such as visible surfaces of parts of a vehicle, and in some embodiments, determining the surface observation probability of each of the surfaces positioned in the observation environment. The surface analysis system may measure a plurality of head locations and/or head orientations of the observer during one or more observation periods using one or more sensors. Further, the surface analysis system may identify the one or more visible surface by determining which surfaces are positioned unobstructed from at least one head location and/or positioned within a field of view of at least one head orientation of the observer. The one or more sensors may be image sensors, proximity sensors, and/or motion capture sensors and may interact with one or more motion trackers located on the observer to determine the head location of the head of the observer. The surface analysis system may also generate head location probability clouds and visibility polygons corresponding with one or more head locations to help identify the visible surfaces of the parts positioned in the observation environment. Identifying visible surfaces and surface observation probabilities may help improve the design, manufacture, and assembly of vehicles or other products having visible surfaces. 
     It is noted that the terms “substantially” and “about” may be utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation. These terms are also utilized herein to represent the degree by which a quantitative representation may vary from a stated reference without resulting in a change in the basic function of the subject matter at issue. 
     While particular embodiments have been illustrated and described herein, it should be understood that various other changes and modifications may be made without departing from the spirit and scope of the claimed subject matter. Moreover, although various aspects of the claimed subject matter have been described herein, such aspects need not be utilized in combination. It is therefore intended that the appended claims cover all such changes and modifications that are within the scope of the claimed subject matter.