Patent Application
20150294473
Embodiments presented herein relate to image processing, and particularly to 3D image reconstruction.
The research in three dimensional (3D) imaging, such as 3D Video or 3D TV, has gained a lot of momentum in recent years. The term 3D is usually related to stereoscopic experiences, where each one of the user's eyes is provided with a unique image of a scene. Such unique images may be provided as a stereoscopic image pair. The unique images are then fused by the human brain to create a depth impression (i.e. an imagined 3D view).
A number of 3D movies are being produced every year, providing stereoscopic effects to the spectators. Also consumer 3D TV devices are available. It is also envisaged that 3D-enabled mobile devices (such as tablet computers and so-called smartphones) soon will be commercially available. A number of standardization bodies (ITU, EBU, SMPTE, MPEG, and DVB) and other international groups (e.g. DTG, SCTE), are working toward standards for 3D TV or Video.
Free viewpoint television (FTV) is an audio-visual system that allows users to have a 3D visual experience while freely changing their position in front of a 3D display. Unlike a typical stereoscopic TV, which enables a 3D experience to users that are sitting at a fixed position in front of the TV screen, FTV allows viewers to observe the scene from different angles, as if actually being part of the scene displayed by the FTV display. In general terms, the FTV functionality is enabled by multiple components. The 3D scene is captured by a plurality of cameras and from different views (angles)—by so-called multiview video. Multiview video can be efficiently encoded by exploiting both temporal and spatial similarities that exist in different views. However, even with multiview video coding (MVC), the transmission cost remains prohibitively high. This is why today only a subset (typically 2-3) of the captured multiple views typically is transmitted. To compensate for the missing information, depth and disparity maps can be used. From the multiview video and depth/disparity information virtual views can be generated at an arbitrary viewing position. In general terms, a depth map is a representation of the depth for each point in a texture expressed as a grey-scale image. The depth map is used to artificially render non-transmitted views at the receiver side, for example with depth image-based rendering (DIBR). Sending one texture image and one depth map image (depth image for short) instead of two texture images may be more bitrate efficient. It also gives the renderer the possibility to adjust the position of the rendered view.
In general terms, using depth and disparity maps requires the use of a depth sensor in order to find depth map values and/or disparity map values. However, for certain depth sensors, objects that are too close or too far away from the depth sensor device cannot be “sensed”, resulting in that such objects do not have any depth information in the depth or disparity map. As noted above, an example of such an area is in
Additionally, configurations of structured-light-based devices (having an IR projector and an IR camera not located in the same position) generate occlusions of the background depth due to the foreground as only the foreground receives the projected pattern. Other issues such as non-reflective surfaces or the need to register the depth map to another viewpoint (with the same of different camera intrinsics) generate areas with missing depth values. As noted above, an example of such an area is in
One issue when the depth of an object is unavailable or incorrect is how to render a scene in such a way that the eye strain and consequently a bad 3D experience for the user is avoided. Imprecise depth maps translate to misplacement of pixels in the rendered view. This is especially noticeable around object boundaries, resulting in a noisy cloud to be visible around the borders. Moreover, temporally unstable depth maps may cause flickering in the rendered view, leading to yet another 3D artifact.
In the paper “Stereoscopic image inpainting using scene geometry” by A Hervieux, N Papadakis, A Bugeau et al. in the proceedings of the 2011 IEEE International Conference on Multimedia and Expo there is proposed an inpainting technique according to which the texture image is clustered into homogeneous color regions using a mean-shift procedure and where the depth of each region is approximated by a plane and then extended into the mask. The visible parts of each extended region are inpainted using a modified exemplar-based inpainting algorithm. A number of drawbacks associated with this approach have been identified. For example, one depth plane per color segment (after a color segmentation step) is computed. The proposed method is thereby sensitive to the image segmentation parameters. If there are two walls or objects with the same color, the two walls or objects will be merged into one plane, resulting in reduced approximation quality. For example, the proposed method is computationally complex and thus is unsuitable for applications such as 3D video conferencing that require real-time processing. For example, the proposed method cannot be applied to estimate depth of eventual far walls if the walls are located entirely in the depth hole area.
Hence, there is still a need for an improved 3D image reconstruction.
An object of embodiments herein is to provide improved 3D image reconstruction.
The inventors of the enclosed embodiments have through a combination of practical experimentation and theoretical derivation discovered that the missing depth pixel values of a scene that are too far away (or too close) from the depth sensor may be filled by approximating the missing values with one or more lines. The line parameters are obtained from neighboring available (i.e., valid) pixel values in the depth representation. This approach may also be used to fill missing depth of flat non-reflective surfaces (for example representing windows, mirrors, monitors or the like) in case the flat non-reflective surfaces are placed in-between two lines that are estimated to be equal or very close to equal.
A particular object is therefore to provide improved 3D image reconstruction based on estimating at least one first line.
According to a first aspect there is presented a method of 3D image reconstruction. The method comprises acquiring a depth image part of a 3D image representation. The depth image part represents depth values of the 3D image. The method comprises determining an area in the depth image part. The area represents missing depth values in the depth image part. The method comprises estimating at least one first line in a first neighbourhood of the area by determining a first gradient of the depth values in the first neighbourhood and determining a direction of the at least one first line in accordance with the first gradient. The method comprises estimating depth values of the area based on the at least one first line and filling the area with the estimated depth values, thereby reconstructing the 3D image.
Advantageously the reconstructed 3D image comprises a complete and accurate depth map that hence will improve the 3D viewing experience of the user.
Advantageously the depth of a scene that is outside the depth range of the depth sensor may be estimated only by using already existing depth information. Hence this removes the need to use another camera. Besides, the line-based approximation enables eventual corners (e.g. of a room) from the image to be accurately determined, thereby increasing the lines estimation quality and robustness. The original sensing range of the depth sensor may thereby be extended.
Advantageously the disclosed embodiments may also be applied in order to fill holes/areas that are due to flat non-reflective content within the range of the depth sensor such as windows, TV or computer screens and other black, metallic or transparent surfaces in a more accurate way than by simple linear interpolation.
Advantageously the disclosed embodiments allow for simple execution and may hence be implemented to be performed in real-time, unlike other state-of-the-art approaches. This enables implementation of applications such as 3D video conferencing.
According to a second aspect there is presented an electronic device for 3D image reconstruction. The electronic device comprises a processing unit. The processing unit is arranged to acquire a depth image part of a 3D image representation, the depth image part representing depth values of the 3D image. The processing unit is arranged to determine an area in the depth image part, the area representing missing depth values in the depth image part. The processing unit is arranged to estimate at least one first line in a first neighbourhood of the area by determining a first gradient of the depth values in the first neighbourhood and determining a direction of the at least one first line in accordance with the first gradient. The processing unit is arranged to estimate depth values of the area based on the at least one first line and filling the area with the estimated depth values, thereby reconstructing the 3D image
According to a third aspect there is presented a computer program for 3D image reconstruction, the computer program comprising computer program code which, when run on a processing unit, causes the processing unit to perform a method according to the first aspect.
According to a fourth aspect there is presented a computer program product comprising a computer program according to the third aspect and a computer readable means on which the computer program is stored. According to an embodiment the computer readable means is a non-volatile computer readable means.
It is to be noted that any feature of the first, second, third and fourth aspects may be applied to any other aspect, wherever appropriate. Likewise, any advantage of the first aspect may equally apply to the second, third, and/or fourth aspect, respectively, and vice versa. Other objectives, features and advantages of the enclosed embodiments will be apparent from the following detailed disclosure, from the attached dependent claims as well as from the drawings.
Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to “a/an/the element, apparatus, component, means, step, etc.” are to be interpreted openly as referring to at least one instance of the element, apparatus, component, means, step, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.
The invention is now described, by way of example, with reference to the accompanying drawings, in which:
The present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which certain embodiments of the disclosure are shown. The present disclosure may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided by way of example so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art. Like numbers refer to like elements throughout the description.
Embodiments presented herein relate to image processing, and particularly to 3D image reconstruction. In 3D imaging a depth map is a simple grayscale image, wherein each pixel indicates the distance between the corresponding pixel from a video object and the capturing camera. Disparity is the apparent shift of a pixel which is a consequence of moving from one viewpoint to another. Depth and disparity are mathematically related and can be interchangeably used. One common property of depth/disparity maps is that they contain large smooth surfaces of constant grey levels. This makes depth/disparity maps easy to compress.
It is possible to construct a cloud of 3D points from the depth map according to the expression Q=d*q*K−1, where q is a 2D point (expressed in the camera coordinate frame in homogeneous coordinates), d its associated depth (measured by the depth sensor for instance), Q the corresponding 3D point in a 3D coordinate frame, and where the matrix K represents a pinhole camera model including the focal lengths, principal point, etc. In general terms, the pinhole camera model describes the mathematical relationship between the coordinates of a 3D point and its projection onto the 2D image plane.
The depth map can be measured by specialized cameras, e.g., structured-light or time-of-flight (ToF) cameras, where the depth is correlated respectively with the deformation of a projected pattern or with the round-trip time of a pulse of light. These depth sensors have limitations, some of which will be mentioned here. The first limitation is associated with the depth range: objects that are too close to or too far away from the depth sensor device will not result in any depth information. The range of a depth sensor is static and limited for the structured-light devices—for a typical depth sensor the depth range is typically from 0.8 m to 4 m. For the ToF cameras, the depth range generally depends on the light frequency used: for example, a 20 MHz based depth sensor gives a depth range between 0.5 m and 7.5 m with an accuracy of about 1 cm. Another limitation is associated with the specific configuration of structured-light-based devices (having an IR projector and an IR camera not located in the same position), which generates occlusions of the background depth due to the foreground as only the foreground receives the projected pattern. Other issues such as non-reflective surfaces or the need to register the depth map to another viewpoint may also generate areas with missing depth values.
The missing depth values are commonly referred to as holes in the depth map, hereinafter referred to holes of type 1. Areas that are out of range may typically cover larger portions in a depth map. Smaller holes, hereinafter referred to holes of type 2, may be caused by occlusion problems. Finally, even smaller holes, hereinafter referred to holes of type 3, may be due to measurement noise or similar issues. The smallest holes (type 3) may be filled by applying filtering techniques. However, larger holes (type 1 and 2) cannot be fixed by such methods and in order to fill holes of type 1 and type 2 information of the scene texture or geometry is usually required.
Inpainting is a technique originally proposed for recovering missing texture in images. In general terms, inpainting may be split into geometric-based approaches and so-called exemplar-based approaches. According to the former the geometric structure of the image is propagated from the boundary towards the interior of the holes, whereas according to the latter the missing texture is generated by sampling and copying the available neighboring color values. Inpainting can also be accomplished by combining a texture with the corresponding depth image.
A number of depth sensors exist. Some of the basic principles of different types of depth sensors will be discussed next. However, as the skilled person understands, the disclosed embodiments are not limited to any particular type of depth sensor, unless specifically specified.
As a first example, a 3D scanner is a device that is arranged to analyze a real-world object or environment to collect data on its shape and possibly its appearance (i.e. color). A 3D scanner may thus be used as a depth sensor. The collected data can then be used by the device to generate digital, three dimensional models. Many different technologies can be used to construct and build these 3D scanning devices; each technology comes with its own limitations, advantages and costs.
A second example includes structured-light based systems. When using structured-light based systems a narrow band of light is projected onto a three-dimensionally shaped surface which produces a line of illumination that appears distorted from other perspectives than that of the projector. This can be used for an exact geometric reconstruction of the surface shape (light section). The structured-light based system may be arranged to project random points in order to capture a dense representation of the scene. The structured-light based system typically also specifies whether a pixel has a depth that is outside the depth range max value with a specific flag. It also specifies if the system is not able to acquire a depth of a pixel with another specific flag. Typical structured-light based systems have a maximum limit range value of 3 or 4 meters depending on the mode that is activated.
A third example includes Time-of-Flight (ToF) camera based systems. A ToF camera is a range imaging camera system that is arranged to resolve distance based on the speed of light (assumed to be known) by measuring the time-of-flight of a light signal between the camera and the subject for each point of the image. The time-of-flight camera belongs to a class of scannerless light detection and ranging (LIDAR) based systems, where the entire scene is captured with each laser or light pulse (as opposed to point-by-point) with a laser beam, such as in scanning LIDAR systems. The current resolution for most commercially available ToF camera based systems is 320×240 pixels or less. The range is typically in the order of 5 to 10 meters. Depending on the device model, objects that are located outside the depth range will be given no depth (specific flag). Alternatively, some devices may replicate the depth of an object located outside the range to be inside the range, thereby providing an erroneous depth value. For instance, if an object is at 12 meters from the sensor (where the maximum depth of the sensor is 10 m), the depth value will given as 2 meters. For this latter type of ToF camera, it may me possible to detect such an erroneous configuration considering for instance, the received signal strength. Another disadvantage is the background light that may interfere with the emitted light and which hence may make the depth map noisy. Besides, due to multiple reflections, the light may reach the objects along several paths and therefore the measured distance may be greater than the true distance.
In contrast to ToF cameras, where the system illuminates a whole scene, a fourth example includes laser scanning systems which typically only illuminate a single point at once. This results in a sparse depth map. In this kind of depth map, a bunch of pixels are known to have no known depth.
The embodiments disclosed herein relate to 3D image reconstruction whereby holes in the depth map are filled by approximating the unknown 3D content (in the hole) with one or more lines or planes. The planes may be planes of a box. In order to obtain 3D image reconstruction there is provided an electronic device, a method performed in the electronic device, a computer program comprising code, for example in the form of a computer program product, that when run on an electronic device, causes the electronic device to perform the method.
The memory 3 may comprise persistent storage, which, for example, can be any single one or combination of magnetic memory, optical memory, solid state memory or even remotely mounted memory. The electronic device 1 may further comprise an input/output (I/O) interface 4 for receiving and providing information to a user interface and/or a display screen. The electronic device 1 may also comprise one or more transmitters 6 and/or receivers 5 for communications with other electronic devices. The processing unit 2 controls the general operation of the electronic device 1, e.g. by sending control signals to the transmitter 6, the receiver 5, the I/O interface and receiving reports from the transmitter 6, the receiver 5 and the I/O interface 4 of its operation. Other components, as well as the related functionality, of the electronic device 1 are omitted in order not to obscure the concepts presented herein.
In
The rectangles illustrated in
In each
Returning now to
In a step S4 an area 9, 10 in the depth image part 7 is determined. The area 9, 10 in the depth image part 7 is determined by the processing unit 2 of the electronic device 1. The area 9, 10 represents missing depth values in the depth image part. The missing depth values may thus represent non-confident or untrusted depth values in the depth image part. As noted above, in
The DHD functional block of the processing unit 2 may thereby detect relevant holes (as defined by the area representing missing depth values) in the depth image part 7 and associated pixels and further select the holes/areas. Assume that the depth map has a depth range between a minimum depth value Zmin and a maximum value Zmax (see
A constraint on the minimal size of the hole/area may be added in order to only consider holes/areas at least as large as the minimum size. Likewise a constraint on the shape of the hole/area may be added (e.g. the hole/area should be squared or rectangle etc.). That is, according to embodiments the area 9, 10 is determined exclusively in a case the area 9, 10 is larger than a predetermined size value.
One purpose of the PE is to find an accurate line-based approximation for the regions where a depth map has holes/areas due to the region being outside the range of the depth sensor S or for holes/areas due to the region representing non-reflective surfaces. In a step S6 at least one first line Pr in a first neighbourhood Nr of the area 9, 10 is estimated. The at least one first line Pr is estimated by the processing unit 2 of the electronic device 1. The at least one first line Pr is estimated by determining a first gradient of the depth values Lr in the first neighbourhood Nr and determining a direction of the at least one first line Pr in accordance with the first gradient.
According to embodiments, in a step S6′ at least one second line Pl is also estimated in a second neighbourhood Nl of the area 9, 10. The at least one second line Pl is estimated by the processing unit 2 of the electronic device 1. The at least one second line Pl is estimated by determining a second gradient of the depth values Ll in the second neighbourhood Nl and determining a direction of the at least one second line Pl in accordance with the second gradient. According to embodiments the first neighbourhood Nr and the second neighbourhood Nl are located at opposite sides of the area 9, 10.
Thus, first at least one line Pr, Pl is estimated from neighboring Nr, Nl of depth values. Then a plane that fits one (or more) of the at least one line may be estimated. Lines can be taken from the top, middle and bottom area of the hole region, or they can be taken with a regular spacing within the hole etc. Similarly, the number of lines provides a trade-off between estimation complexity and accuracy. According to embodiments the at least one first line is part of a first plane, and/or the at least one second line is part of a second plane.
In a case the at least one first line Pr is a horizontally oriented line the at least one second line Pl may be a vertically oriented line. That is, according to embodiments at least one vertically oriented line is in a step S6″ estimated in a vertically oriented neighbourhood of the area by determining a vertically oriented gradient of the depth values in the vertically oriented neighbourhood and determining a direction of the at least one vertically oriented line in accordance with the vertically oriented gradient. The at least one vertically oriented line is estimated by the processing unit 2 of the electronic device 1.
In a case the at least one first line Pr is a vertically oriented line the at least one second line Pl may be a horizontally oriented line. That is, according to embodiments at least one horizontally oriented line is in a step S6″′ estimated in a horizontally oriented neighbourhood of the area by determining a horizontally oriented gradient of the depth values in the horizontally oriented neighbourhood and determining a direction of the at least one horizontally oriented line in accordance with the horizontally oriented gradient. The at least one horizontally oriented line is estimated by the processing unit 2 of the electronic device 1.
In indoor applications, the camera x-axis is often aligned with the horizon. In such cases the left and the right local planes (represented by the at least one first line Pr and the at least one second line Pl, respectively) may be estimated based on respectively the right Nr and left Nl depth neighborhood of the hole/area 9, 10.
There are different ways to estimate a line Pr, Pl or a plane from a set of 3D points (or depths). One could for instance use a principal component analysis (PCA) approach and consider the main eigenvector to be the desired plane (or line). In order to cope with non-white noise that the neighboring 3D point set can have, a random sample consensus analysis (RANSAC) or an iterative closest point analysis (ICP) approach may be used where the algorithms are initialized with the nearest depths. That is, according to embodiments, a first plane and/or a second plane are/is, in a step S10, estimated by one of principal component analysis, random sample consensus analysis, and iterative closest point analysis. The first plane and/or a second plane are/is estimated by the processing unit 2 of the electronic device 1.
Different weights may also be given to neighboring Nr, Nl depth pixels, depending on the distance from the hole/area 9, 10. That is, according to embodiments, weights are, in a step S12, associated with depth values in the first neighbourhood Nr and/or the second neighbourhood Nl. The weights are associated with depth values in the first neighbourhood Nr and/or the second neighbourhood Nl by the processing unit 2 of the electronic device 1.
The weights may represent a confidence value, a variance or any quality metric. Values of the weights may depend on their distance to the area 9, 10. According to embodiments a first quality measure of a first plane and/or a second quality measure of a second plane is obtained, step S14. The first quality measure is obtained by the processing unit 2 of the electronic device 1. The first plane and/or the second plane may then be accepted as estimates only if the first quality measure and/or the second quality measure are/is above a predetermined quality value, step S16. The first plane and/or the second plane are accepted as estimates by the processing unit 2 of the electronic device 1.
In general, each hole/area 9, 10 may comprise (a) one or more non-sensed walls and/or (b) one or more corner regions. Once a set of lines Pr, Pl or planes is estimated, the PE may be arranged to detect if the number of lines or planes is large enough to generate a good approximation of the content. Therefore, it is, according to an embodiment, determined, in a step S18, whether or not at least one intersection exists between the at least one first line and the at least one second line. The determination is performed by the processing unit 2 of the electronic device 1. Thereby the processing unit 2 may be arranged to check if an intersection C of the first line Pr and second line Pl (for example right and left lines) exists and if so that the intersection is not too far away from the depth sensor S (see,
In case (a) the depth of a hole/area may be flat or possibly a linear function of the distance from the depth sensor (see,
In case (b) it is reasonable to assume that the depth of hole/area 9 changes with the same gradient as the available depth of neighboring walls (as represented by available depth values in the depth image part 7) that form a corner C (see,
In any of the cases (a), (b), the neighboring pixels with known depth values are used in order to determine one or more local approximation lines for the missing pixels (i.e., the missing depth values).
In other embodiments the PE is arranged not only to estimate left and right planes but also planes from the top and from the bottom of the hole/area 9, 10 using the same steps as disclosed above. Additionally, horizontal lines can be eventually searched in the image 12 and or the depth image part 7 in order to increase the quality of the estimated lines/planes.
Once the at least one first line Pr (alternatively together with the at least one second line Pl) has been estimated, a hole/area filling algorithm is used to fill the holes/areas 9, 10 with estimated depth values. As noted above, the depth map inpainter is arranged to use the lines approximation of the depth of the holes/areas 9, 10 in order to fill the depth map. Therefore, in a step S8, depth values of the area 9, 10 are estimated. The depth values of the area 9, 10 are estimated by the processing unit 2 of the electronic device 1. The depth values of the area 9, 10 are estimated by the processing unit 2 of the electronic device 1. The depth values of the area are estimated based on the at least one first line Pr. The area 9, 10 is filled with the estimated depth values. The 3D image is thereby reconstructed. According to an embodiment depth values of the area 9, 10 based also on the at least one second line Pl are estimated, step S8′. The estimation is performed by the processing unit 2 of the electronic device 1. According to an embodiment depth values of the area based on the at least one vertically oriented line are estimated, step S8″. The estimation is performed by the processing unit 2 of the electronic device 1. According to an embodiment depth values of the area based on the at least one horizontally oriented line are estimated S8″′. The estimation is performed by the processing unit 2 of the electronic device 1. For example, for every pixel of the hole/area, a ray starting from the camera optical center and extending through the image pixel (a method known as back-projection) intersects with the lines in one 3D point per line. Then, the missing depth value for the image pixel may be determined to be the one with the minimum distance from the camera optical center to the line.
The depth map with the filled holes/areas may be filtered to reduce eventual errors, using for instance, a joint-bilateral filter or a guided filter. That is, according to embodiments the depth image part comprising the estimated depth values is, in a step S34, filtered. The processing unit 2 of the electronic device 1 is arranged to filter the depth image part. The at least one first line may be represented by at least one equation where the at least one equation has a set of parameters and values. The step S34 of filtering may then further comprise filtering, step S34′ also the values of the at least one equation. The processing unit 2 of the electronic device 1 is arranged also to filter the values of the at least one equation. Thereby the equations of the lines/planes may also be used to filter the depth values. For instance, the line/planes may be optimized together with the depth map in order to further improve the resulting depth quality.
The electronic device 1 may be arranged to integrate a system that estimates the orientation of the camera (and depth sensor S) with respect to room box approximations in order to determine the corners of the room (angles). That is, according to embodiments an orientation of the depth image part is acquired, step S36. The depth image part is acquired by the processing unit 2 of the electronic device 1. The direction of the at least one first line Pr may then be estimated, in a step S38, based on the acquired orientation. The direction of the at least one first line Pr is estimated by the processing unit 2 of the electronic device 1. This may be accomplished by detecting infinite points from parallel lines or by using an external device such as a gyroscope. That is, according to one embodiment the orientation is acquired, step S36′, by detecting infinite points from parallel lines in the 3D image. According to another embodiment the orientation is acquired, step S36″ from a gyroscope reading. The orientation is acquired by the processing unit 2 of the electronic device 1.
In summary, unlike the above referred paper entitled “Stereoscopic image inpainting using scene geometry” where one plane per color segment is estimated, according to the herein disclosed embodiments the lines are estimated only using neighboring depth pixels with known depth at different locations (on the left, right, top and/or bottom) of the hole/area to be filled.
A flow chart according to one exemplary scenario is shown in
By knowing the depth sensor setup (calibration) and using the set of estimated lines, the depth can be determined for all missing depth pixels of the hole/area. This filled depth map can then be refined by an optimization or filter framework. The number of lines can vary, from only one to many. For instance, if a hole/area has no right border (image limit), then the left plane (or eventually estimated top and bottom lines) may be used in order to approximate the hole depth. At least one line is necessary to fill the hole/area representing missing depth values with estimated depth values.
The present disclosure has mainly been described above with reference to a few embodiments. However, as is readily appreciated by a person skilled in the art, other embodiments than the ones disclosed above are equally possible within the scope of the disclosure, as defined by the appended patent claims.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/SE2012/051230 | 11/12/2012 | WO | 00 |
