1. A lyophilized pooled human growth factor composition prepared by lyophilizing a pooled human growth factor starting material.
2. A lyophilized pooled human growth factor composition according to claim 1 wherein the growth factor starting material is selected from the group consisting of platelets, platelet rich plasma, platelet poor plasma, breast milk, blood, bone marrow, amniotic fluid, umbilical cord fluid, and combinations thereof.
3. A lyophilized pooled human growth factor composition according to claim 1 wherein the growth factor starting material comprises a platelet starting material.
4. A lyophilized pooled human growth factor composition according to claim 3 wherein the platelet starting material comprises platelet rich plasma.
5. A lyophilized pooled human growth factor composition according to claim 1 wherein the composition is stored at a temperature above \u221270\xb0 C.
6. A lyophilized pooled human growth factor composition according to claim 5 wherein the composition is stored at about room temperature.
7. A lyophilized pooled human growth factor composition according to claim 5 wherein the composition is stored at room temperature above \u221265\xb0 C.
8. A lyophilized pooled human growth factor composition according claim 7 wherein the composition is stored at a temperature of from about 15\xb0 C. to about 35\xb0 C.
9. A lyophilized pooled human growth factor composition according to claim 1 further comprising, prior to lyophilizing, mixing the growth factor starting material.
10. A lyophilized pooled human growth factor composition.
11. A lyophilized pooled human growth factor composition according to claim 10 wherein the compositin consists essentially of pooled human growth factors.
12. A lyophilized pooled human growth factor composition according to claim 10 wherein the growth factors are selected from the group consisting of PDGF-AA, PDGF-BB, PDGF-AB, EGF, VEGF, TGF-\u03b1, FGF, TGF-\u03b2, IGF-1, IGF-2, and NGF.
13. A lyophilized pooled human growth factor composition according to claim 10 further comprising a.pharmaceutically acceptable carrier.
14. A lyophilized pooled human growth factor composition according to claim 13 wherein the carrier is a gel.
15. A lyophilized pooled human growth factor composition according to claim 13 wherein the carrier is a cream.
16. A lyophilized pooled human growth factor composition according to claim 13 wherein the carrier is an emulsion.
17. A lyophilized pooled human growth factor composition according to claim 13 wherein the carrier is a microcapsule.
18. A lyophilized pooled human growth factor composition according to claim 16 further comprising heating the recovered growth factors.
19. A lyophilized pooled human growth factor composition according to claim 10 wherein the composition is in a sealed water resistant container.
20. A lyophilized pooled human growth factor composition according to claim 10 wherein is combined with a dressing.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A method, comprising:
obtaining two or more surfaces to be rendered in an image, wherein at least one of the surfaces occludes at least a portion of at least one other of the surfaces;
assigning depth probability density functions to the surfaces; and
rendering the image from the two or more surfaces according to the depth probability density functions of the surfaces, wherein said rendering comprises:
computing the expected color contribution of each surface according to the depth probability density functions of the surfaces; and
summing the expected color contributions of the surfaces to generate expected color for the image.
2. The method as recited in claim 1, wherein said computing the expected color contribution of each surface according to the depth probability density functions of the surfaces comprises:
determining a set of intervals for which a number of overlapping surfaces is constant;
analytically computing the expected color contribution for each of the intervals; and
summing the expected color contributions of the intervals to generate the expected color contribution of the surface.
3. The method as recited in claim 1, wherein said computing the expected color contribution of each surface according to the depth probability density functions of the surfaces comprises:
firing multiple rays at each of a plurality of points in the image from a camera position;
for each ray fired at each of the plurality of points:
generating a random depth scale factor relative to the camera position for each surface intersected by the ray; and
calculating an expected color for the point according to the random depth scale factors for this ray;
for each point on each surface of the image, averaging the expected colors for each ray fired at this point to generate an expected color contribution for this point.
4. The method as recited in claim 3, wherein the generated random depth scale factor for a given ray is different for at least two surfaces intersected by the ray.
5. The method as recited in claim 3, wherein two or more of the surfaces are correlated, and wherein said generating a random depth scale factor relative to the camera position for each surface intersected by the ray comprises, for rays that intersect at least two correlated surfaces, generating a correlated random depth scale factor for each correlated surface intersected by the ray.
6. The method as recited in claim 5, wherein the correlated random depth scale factor for two correlated surfaces is generated according to an entanglement value c, where c specifies a degree of correlation between the two correlated surfaces.
7. The method as recited in claim 6, where c is variable within a range of 0.0 to 1.0 to specify the degree of correlation between the two correlated surfaces.
8. The method as recited in claim 3, further comprising:
for each ray fired at each of the plurality of points, calculating a shaded value for the point; and
for each point on each surface of the image, averaging the shaded value for each ray fired at this point to generate a shaded value for this point.
9. The method as recited in claim 1, wherein said rendering further comprises computing shadows for the image.
10. The method as recited in claim 1, further comprising:
obtaining a clipping plane to be applied to the surfaces rendered in the image;
assigning a depth probability density function to the clipping plane; and
rendering the image from the two or more surfaces and the clipping plane according to the depth probability density functions of the surfaces and the depth probability density function of the clipping plane, wherein the rendered image shows an interior region of an object composed of the rendered surfaces revealed by applying the clipping plane to the surfaces.
11. A system, comprising:
one or more processors; and
a memory comprising program instructions, wherein the program instructions are executable by at least one of the one or more processors to:
obtain two or more surfaces to be rendered in an image, wherein at least one of the surfaces occludes at least a portion of at least one other of the surfaces;
assign depth probability density functions to the surfaces; and
render the image from the two or more surfaces according to the depth probability density functions of the surfaces, wherein said rendering comprises:
compute the expected color contribution of each surface according to the depth probability density functions of the surfaces; and
sum the expected color contributions of the surfaces to generate expected color for the image.
12. The system as recited in claim 11, wherein, to compute the expected color contribution of each surface according to the depth probability density functions of the surfaces, the program instructions are executable by at least one of the one or more processors to:
determine a set of intervals for which a number of overlapping surfaces is constant;
analytically compute the expected color contribution for each of the intervals; and
sum the expected color contributions of the intervals to generate the expected color contribution of the surface.
13. The system as recited in claim 11, wherein, to compute the expected color contribution of each surface according to the depth probability density functions of the surfaces, the program instructions are executable by at least one of the one or more processors to:
fire multiple rays at each of a plurality of points in the image from a camera position;
for each ray fired at each of the plurality of points:
generate a random depth scale factor relative to the camera position for each surface intersected by the ray; and
calculate an expected color for the point according to the random depth scale factors for this ray;
for each point on each surface of the image, average the expected colors for each ray fired at this point to generate an expected color contribution for this point.
14. The system as recited in claim 13, wherein two or more of the surfaces are correlated, and wherein, to generate a random depth scale factor relative to the camera position for each surface intersected by the ray, the program instructions are executable by at least one of the one or more processors to, for rays that intersect at least two correlated surfaces, generate a correlated random depth scale factor for each correlated surface intersected by the ray.
15. The system as recited in claim 11, wherein the program instructions are executable by at least one of the one or more processors to:
obtain a clipping plane to be applied to the surfaces rendered in the image;
assign a depth probability density function to the clipping plane; and
render the image from the two or more surfaces and the clipping plane according to the depth probability density functions of the surfaces and the depth probability density function of the clipping plane, wherein the rendered image shows an interior region of an object composed of the rendered surfaces revealed by applying the clipping plane to the surfaces.
16. The system as recited in claim 10, wherein at least one of the one or more processors is a graphics processing unit (GPU).
17. A computer-readable storage medium storing program instructions, wherein the program instructions are computer-executable to implement:
obtaining two or more surfaces to be rendered in an image, wherein at least one of the surfaces occludes at least a portion of at least one other of the surfaces;
assigning depth probability density functions to the surfaces; and
rendering the image from the two or more surfaces according to the depth probability density functions of the surfaces, wherein said rendering comprises:
computing the expected color contribution of each surface according to the depth probability density functions of the surfaces; and
summing the expected color contributions of the surfaces to generate expected color for the image.
18. The computer-readable storage medium as recited in claim 17, wherein, in said computing the expected color contribution of each surface according to the depth probability density functions of the surfaces, the program instructions are computer-executable to implement:
determining a set of intervals for which a number of overlapping surfaces is constant;
analytically computing the expected color contribution for each of the intervals; and
summing the expected color contributions of the intervals to generate the expected color contribution of the surface.
19. The computer-readable storage medium as recited in claim 17, wherein, in said computing the expected color contribution of each surface according to the depth probability density functions of the surfaces, the program instructions are computer-executable to implement:
firing multiple rays at each of a plurality of points in the image from a camera position;
for each ray fired at each of the plurality of points:
generating a random depth scale factor relative to the camera position for each surface intersected by the ray; and
calculating an expected color for the point according to the random depth scale factors for this ray;
for each point on each surface of the image, averaging the expected colors for each ray fired at this point to generate an expected color contribution for this point.
20. The computer-readable storage medium as recited in claim 19, wherein two or more of the surfaces are correlated, and wherein, in said generating a random depth scale factor relative to the camera position for each surface intersected by the ray, the program instructions are computer-executable to implement, for rays that intersect at least two correlated surfaces, generating a correlated random depth scale factor for each correlated surface intersected by the ray.