1. A method of identifying differences between a pair of images, comprising:
loading a source image and a target image into memory of a computer;
constructing source and target edge images from the source and target images to enable processing of multiband images;
displaying the source and target images on a display device of the computer;
automatically aligning the source and target edge images by:
a) computing figure of merit values for the source and target edge images;
b) computing separation and ratio values for the source and target edge images;
c) constructing small image versions of the source and target edge images;
d) computing remap coefficients for the small image versions of the source and target edge images;
e) remapping the small image versions using the remap coefficients;
f) determining intensity peaks in the small image versions of the source and target edge images; and
g) matching intensity peaks in the source and target edge images; and
switching displaying of the source image and the target image on the display device to enable identification of differences between the source image and the target image.
2. The method of claim 1, wherein the aligning is automatically performed by the computer.
3. The method of claim 1, further comprising:
h) performing non-linear squares fitting;
i) computing new remap coefficients of the source and target edge images;
j) computing new figure of merit values using the new remap coefficients;
k) comparing the new figure of merit values with the figure of merit values computed in step a); and
l) iterating acts b)-k) until either the separation or figure of merit values are within predetermined limits.
4. The method of claim 1, wherein the separation comprises smoothing width for the source and target edge images, and further wherein the smoothing width is inversely proportional to a degree of smoothing and local peaks present in the source and target edge images.
5. The method of claim 1, wherein the aligning comprises manually aligning the source and target images.
6. The method of claim 5, wherein the manually aligning the source and target edge images comprises:
enabling a user selecting a tie-point in the target image and a tie-point in the source image to perform alignment of the source and target images; and
computing remap coefficients using the selected tie-points in the source and target images.
7. The method of claim 1, wherein the constructing the source and target edge images comprises:
constructing an edge image for individual color components of the source image;
constructing an edge image for individual color components of the target image;
averaging the edge images of individual color components of the source image to create a single source edge image corresponding to the source image; and
averaging the edge images of individual color components of the target image to create a single target edge image corresponding to the target image.
8. The method of claim 7, wherein the constructing the source edge image comprises:
for each interior pixel of the source edge image:
examining a corresponding pixel of the source image and four neighboring pixels of the corresponding pixel of the source image;
determining largest and smallest intensity values of the corresponding pixel and the four neighboring pixels; and
establishing a difference between the largest and the smallest intensity values of the corresponding pixel and the four neighboring pixels as an intensity value of the interior pixel in the source edge image; and
assigning a value of 0 to pixels at a periphery of the source edge image.
9. The method of claim 7, wherein the constructing the target edge image comprises:
for each interior pixel of the target edge image:
examining a corresponding pixel of the target image and four neighboring pixels of the corresponding pixel of the target image;
determining largest and smallest intensity values of the corresponding pixel and the four neighboring pixels;
establishing a difference between the largest and the smallest intensity values of the corresponding pixel and the four neighboring pixels as an intensity value of the interior pixel in the target edge image; and
assigning a value of 0 to pixels at a periphery of the target edge image.
10. The method of claim 1, wherein the source and target images each comprise color images having a plurality of color bands.
11. The method of claim 1, wherein performing alignment using the target and source edge images enables alignment of source and target images that are not of similar types.
12. The method of claim 1, wherein performing alignment using the target and source edge images enables merging of information from several bands of a multi-band image into a single-band image to obtain efficient processing of images.
13. A method of identifying differences between a pair of images, comprising:
receiving source and target images in a computer;
constructing source and target edge images from the respective source and target images to enable processing of multiband images;
performing alignment of the source and target edge images displayed on a display device of the computer by:
computing figure of merit values for the source and target edge images;
computing separation and ratio values for the source and target edge images;
constructing small image versions of the source and target edge images;
computing remap coefficients for the small image versions of the source and target edge images;
remapping the small image versions using the remap coefficients;
determining intensity peaks in the small image versions of the source and target edge images; and
matching intensity peaks in the source and target edge images; and
periodically switching displaying of the source and target images on the display device.
14. The method of claim 13, wherein the periodically switching further comprises:
obtaining remap coefficients using the source and target edge images; and
computing a remapped source edge image using the remap coefficients to correct alignment errors between the source and target edge images.
15. The method of claim 13, wherein the performing the alignment is automatically performed by the computer.
16. The method of claim 15, wherein the performing the alignment by the computer further comprises:
selecting control points in the source and target edge images by a user; and
providing the selected control points to the computer for alignment of the source and target edge images by the computer.
17. The method of claim 15, wherein the performing the alignment by the computer further comprises:
selecting a region of interest in the source and target images by a user; and
providing the selected region of interest to the computer for alignment of the source and target edge images by the computer.
18. The method of claim 13, wherein the performing the alignment is manually performed by a user.
19. The method of claim 18, wherein performing the alignment manually comprises:
selecting control points in the source and target images; and
aligning the source and target images using the selected control points.
20. The method of claim 18, wherein performing the alignment manually comprises:
selecting a region of interest in the source and target images; and
aligning the source and target images using the selected region of interest.
21. The method of claim 13, wherein the periodically switching the displaying of the source and target images is performed automatically performed by the computer.
22. The method of claim 13, wherein the periodically switching the displaying of the source and target images is performed by a user.
23. The method of claim 13, wherein the performing the alignment of the source and target edge images further comprises:
using a least squares computation to match the intensity peaks in the source and target edge images.
24. The method of claim 13, wherein the constructing the source edge image comprises:
for each interior pixel of the source edge image:
examining a corresponding pixel of the source image and four neighboring pixels of the corresponding pixel of the source image;
determining largest and smallest intensity values of the corresponding pixel and the four neighboring pixels; and
establishing a difference between the largest and the smallest intensity values of the corresponding pixel and the four neighboring pixels as an intensity value of the interior pixel in the source edge image; and
assigning a value of 0 to pixels at a periphery of the source edge image.
25. The method of claim 13, wherein the constructing the target edge image comprises:
for each interior pixel of the target edge image:
examining a corresponding pixel of the target image and four neighboring pixels of the corresponding pixel of the target image;
determining largest and smallest intensity values of the corresponding pixel and the four neighboring pixels; and
establishing a difference between the largest and the smallest intensity values of the corresponding pixel and the four neighboring pixels as an intensity value of the interior pixel in the target edge image; and
assigning a value of 0 to pixels at a periphery of the target edge image.
26. An image change detection system, comprising:
a storage device configured to receive and store a pair of images, as a pair of image files including a source image and a target image; and
processing circuitry configured to:
receive the source and target images;
compute source and target edge images from the respective source and target images to enable processing of multiband images;
execute alignment of the source and target edge images by;
computing figure of merit values for the source and target edge images;
computing separation and ratio values for the source and target edge images;
constructing small image versions of the source and target edge images;
computing remap coefficients for the small image versions of the source and target edge images;
remapping the small image versions using the remap coefficients;
determining intensity peaks in the small image versions of the source and target edge images; and
matching intensity peaks in the source and target edge images; and
execute periodic switching of the source and target images to enable a user to determine changes between the source and target images.
27. The image change detection system of claim 26, wherein the source and target images are automatically aligned by the processing circuitry.
28. The image change detection system of claim 26, wherein the source and target images are manually aligned by a user of the image change detection system.
29. An article of manufacture comprising:
processor-usable media embodying programming configured to cause a processing circuitry of an image change detection system to:
load a source image and a target image into memory of a computer;
construct source and target edge images from the source and target images to enable processing of multiband images;
display the source and target images on a display device of the computer;
align the source and target edge images by causing the processing circuitry to:
a) compute figure of merit values for the source and target edge images;
b) compute separation and ratio values for the source and target edge images;
c) construct small image versions of the source and target edge images;
d) compute remap coefficients for the small image versions of the source and target edge images;
e) remap the small image versions using the remap coefficients;
f) determine intensity peaks in the small image versions of the source and target edge images; and
g) match intensity peaks in the source and target edge images; and
switch displaying of the source image and the target image on the display device, to enable identification of differences between the source image and the target image.
30. The article of manufacture of claim 29, further comprising causing the processing circuitry to:
h) perform non-linear squares fitting;
i) compute new remap coefficients of the source and target edge images;
j) compute new figure of merit values using the new remap coefficients;
k) compare the new figure of merit values with the first computed figure of merit values; and
l) iterate acts b)-k) until either the separation or figure of merit values are within predetermined limits.
31. An image change detection system, comprising:
means for loading a source image and a target image into a memory means of a computer means;
means for constructing source and target edge images from the source and target images to enable processing of multiband images;
means for displaying the source and target images on a display means of the computer means;
means for aligning the source and target edge images, comprising:
means for computing figure of merit values for the source and target edge images;
means for computing separation and ratio values for the source and target edge images;
means for constructing small image versions of the source and target edge images;
means for computing remap coefficients for the small image versions of the source and target edge images;
means for remapping the small image versions using the remap coefficients;
means for determining intensity peaks in the small image versions of the source and target edge images; and
means for matching intensity peaks in the source and target edge images; and
means for switching displaying of the source image and the target image on the display means, to enable identification of differences between the source image and the target image.
32. The image change detection system of claim 31, further comprising:
means for performing non-linear squares fitting; and
means for computing new remap coefficients of the source and target edge images.
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 of making a medicinal inhalation device or a component of a medicinal inhalation device, said method comprising a step of: applying to at least a portion of a surface of the device or the component, respectively, a composition comprising a monofunctional polyfluoropolyether silane and a non-fluorinated cross-linking agent.
2. A method according to claim 1, wherein the polyfluoropolyether segment of the polyfluoropolyether silane is not linked to the functional silane group(s) via a functionality that includes nitrogen-silicon bond or a sulfur-silicon bond.
3. A method according to claim 1, wherein the polyfluoropolyether segment of the polyfluoropolyether silane is linked to the functional silane group(s) via a functionality that includes a carbon-silicon bond.
4. A method according to claim 3, wherein the polyfluoropolyether segment of the polyfluoropolyether silane is linked to the functional silane group(s) via a \u2014C(R)2\u2014Si functionality where R is independently hydrogen or a C1-4 alkyl group.
5. A method according to claim 4, wherein the polyfluoropolyether segment of the polyfluoropolyether silane is linked to the functional silane group(s) via a \u2014(CR2)k\u2014C(R)2\u2014Si functionality where k is at least 2 and where R is independently hydrogen or a C1-4 alkyl group.
6. A method according to claim 1, wherein the polyfluoropolyether segment of the polyfluoropolyether is a perfluorinated polyfluoropolyether segment.
7. A method according to claim 6, wherein in the repeating units of the perfluorinated polyfluoropolyether segment the number of carbon atoms in sequence is at most 6.
8. A method according to claim 1, wherein the weight average molecular weight of the polyfluoropolyether segment is about 1000 or higher.
9. A method according to claim 1 or 8, wherein the weight average molecular weight of the polyfluoropolyether segment is about 6000 or less, in particular about 4000 or less.
10. A method according to claim 1, wherein the cross-linking agent comprises one or more non-fluorinated compounds, each compound being independently selected from the group consisting of a compound having at least two hydrolysable groups and a compound having at least one reactive functional group and at least one hydrolysable group.
11. A method according to claim 1, wherein the composition is applied to said surface, such that polyfluoropolyether-containing coating provided on said surface has a thickness of at most about 300 nm.
12. A method according to claim 1 or 11, wherein the composition is applied to said surface, such that polyfluoropolyether-containing coating provided on said surface has a thickness of at least about 20 nm.
13. A medicinal inhalation device or a component of a medicinal inhalation device made according to claim 1.
14. A medicinal inhalation device or a component of a medicinal inhalation device comprising a polyfluoropolyether-containing coating bonded to at least a portion of a surface of the device or the component, respectively, said polyfluorpolyether-containing coating comprising a plurality of monofunctional polyfluoropolyether-silane entities cross-linked through non-fluorinated cross-linking entities and said polyfluorpolyether-containing coating sharing at least one covalent bond with said surface.
15. A device or a component according to claim 13 or 14, where said medicinal inhalation device is a metered dose inhaler or a dry powder inhaler.