Note: Claims are shown in the official language in which they were submitted.
Claims:
1. A picture-detecting apparatus, the apparatus comprising:
a pixel-processing unit, for acquiring a to-be-detected picture that has been
denoised,
performing pixel-level semantic segmentation on a denoised to-be-detected
picture, and
recognizing a subject region image and a background region image;
a hue-space-converting unit, for performing hue space conversion on the to-be-
detected
picture, so as to output hue space data and brightness space data of the
picture; and
a first determining unit, for fusing the subject region image after dilation
processing with
the hue space data, extracting a background purity value corresponding to
every pixel in
the background region image formed after dilation processing, and determining
whether
background purity of the to-be-detected picture is compliant, wherein a
compliant picture
comprises any one or more of non-violent, non-pornographic, blank, and
aesthetic
features, and wherein the aesthetic features include centered image subjects.
2. The apparatus of claim 1, the apparatus further comprising: a
binarizafion-processing unit,
for processing the brightness space data by means of plural binarization
apparatus, so as to
output plural binarization results correspondingly.
3. The apparatus of the claim 2, the apparatus further comprising: a second
determining unit,
for fusing the subject region image with the plural binarization results,
respectively,
extracting a coordinate value of every pixel in the fused subject region image
and its
corresponding background purity value, and determining whether a location of a
subject in
the to-be-detected picture is compliant.
4. The apparatus of the claim 3, wherein between the binarization-
processing unit and the
second determining unit, the apparatus further comprises: performing non-
coherence region
suppression on a first binarization result and a second binarization result,
respectively, by
means of a non-maximum suppression apparatus.
16
Date Reçue/Date Received 2023-12-18
5. The apparatus of the claim 4, wherein acquiring a to-be-detected picture
that has been
denoised, and after pixel-level semantic segmentation, recognizing a subject
region image
and a background region image comprises: denoising the to-be-detected picture
by means of
a nonlinear filtering apparatus; and performing pixel-level semantic
segmentation on the
denoised to-be-detected picture through a multi-channel deep residual fully
convolutional
network model, so as to recognize the subject region image and the background
region
image.
6. The apparatus of the claim 5, wherein performing hue space conversion on
the to-be-
detected picture to output hue space data and brightness space data of the
picture comprises:
using HSV hue space conversion apparatus to convert the to-be-detected picture
and output
the hue space data of the picture, in which the hue space data include a hue
space
component H; and using LUV hue space conversion apparatus to convert the to-be-
detected
picture and output the brightness space data of the picture, in which the
brightness space
data include a brightness space channel L.
7. The apparatus of the claim 6, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: filtering edge pixels of the subject region image by means of a
filter kemel, so as
to dilate the subject region image.
8. The apparatus of the claim 7, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: updating the part other than the dilated subject region image in
the to-be-
detected picture as the background region image.
17
Date Recue/Date Received 2023-12-18
9. The apparatus of the claim 8, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: fusing the updated background region image with data of the hue
space
component H, and determining whether the background purity value corresponding
to every
pixel in the updated background region image is compliant to a first
threshold.
10. The apparatus of the claim 9, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: determining that the background purity of the to-be-detected
picture is
compliant.
11. The apparatus of the claim 10, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: determining that the background purity of the to-be-detected
picture is non-
compliant, and wherein the first threshold includes a first background purity
threshold.
12. The apparatus of the claim 11, processing the brightness space data by
means of the plural
binarizati on apparatus to output the plural binarization results
correspondingly comprises:
processing data of the brightness space channel L by means of a fixed-
threshold binarization
apparatus, so as to obtain the first binarization result.
13. The apparatus of the claim 12, processing the brightness space data by
means of the plural
binarizati on apparatus to output the plural binarization results
correspondingly comprises:
processing the data of the brightness space channel L by means of a Gaussian-
window
binarizati on apparatus, so as to obtain the second binarizati on result.
18
Date Recue/Date Received 2023-12-18
14. The apparatus of the claim 13, the apparatus further comprising:
performing non-coherence
region suppression on the first binarization result and the second binarizati
on result,
respectively, by means of a non-maximum suppression apparatus.
15. The apparatus of the claim 14, wherein fusing the subject region image
with the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises: fusing
the subject region image recognized through pixel-level semantic segmentation
with the first
binarizati on result and the second binarization result, respectively.
16. The apparatus of the claim 15, wherein fusing the subject region image
with the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
extracting coordinate values of the pixels belonging to the subject region
image and the first
binarization result from fusing results and their corresponding background
purity values.
17. The apparatus of the claim 16, wherein fusing the subject region image
with the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
extracting coordinate vaiues of the pixels belonging to the subject region
image and the
second binarization result from fusing results and their corresponding
background purity
values.
18. The apparatus of the claim 17, wherein fusing the subject region image
with the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
summarizing and extracting the coordinate values of the pixels and their
corresponding
background purity values, and determining whether both the coordinate value of
each pixel
and its corresponding background purity value are compliant to a second
threshold.
19
Date Recue/Date Received 2023-12-18
19. The apparatus of the claim 18, wherein fusing the subject region image
with the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
determining that the location of the subject in the to-be-detected picture is
compliant.
20. The apparatus of the claim 19, wherein fusing the subject region image
with the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
determining that the location of the subject in the to-be-detected picture is
non-compliant;
and wherein the second threshold includes a second background purity threshold
and a
location coordinate interval threshold.
21. The apparatus of the claim 20, the apparatus further comprising:
idenfifying a subject region
image and a background region image in a denoised to-be-detected picture
through pixel-
level semantic segmentation, and performing hue space conversion on the
picture to output
hue space data and brightness space data of the picture.
22. The apparatus of the claim 21, the apparatus further comprising: dilating
the subject region
image to dilate the range of edge pixels of the subject region image in order
to ensure
complete coverage over the subject region image.
23. The apparatus of the claim 22, the apparatus further comprising: dilating
subject region
image is fused with hue space data.
24. The apparatus of the claim 23, the apparatus further comprising: to detect
the location of the
subject in the picture, the brightness space data are first processed by means
of the plural
binarization apparatus so as to generate the plural binarization results.
25. The apparatus of the claim 24, wherein the subject region image identified
through pixel-
level semantic segmentation is then fused with the plural binarization
results, respectively.
Date Recue/Date Received 2023-12-18
26. The apparatus of the claim 25, wherein based on the coordinate value of
every pixel in the
fused subject region image and its corresponding background purity value,
whether the
location of the subject in the to-be-detected picture is compliant can be
determined.
27. The apparatus of the claim 26, the apparatus further comprising: employing
HSV (hue,
saturation, value) hue space conversion apparatus to convert the to-be-
detected picture in the
RGB (red, green and blue) color space into the HSV color space that is closer
to human
visual perceptual characteristics.
28. The apparatus of the claim 27, wherein the conversion apparatus includes:
-V= max(R,G, B)
max(R,G,B)-mm(R,G,B)
rnax (R, 0, B)
(G - B)
60x ___________________ S~Oftmax(RAB)=R
Sx V
H = 60 x(2+ (B- R))
aamax(R,G,B)= G
S x V
60x [4+ (R-G1 0~Oftmax(RAB)=B
r7 x
= 1-1+360 H<O
29. The apparatus of the claim 28, wherein the features of brightness space
data include
extensive color gamut coverage, high visual consistency, and good capability
of expressing
color perception.
30. The apparatus of the claim 29, wherein the implantation converting
brightness space data of
the picture to be examined through: converting the to-be-detected picture from
RGB space
data into CIE XYZ space data; and converting the CIE XYZ space data into LUV
brightness
space data using the following conversion equation:
X 0.412453 0.357580 0.180423 R
Y = 0.212671 0.715160 0.072169 G
Z 0.019334 0.119193 0.950227 B
21
Date Reçue/Date Received 2023-12-18
v N:31
Yõ 29
L
29)1 Y
x
Yõ k.29
,u =13 xi_ x(11 ¨
v =13xLx(vi¨vni
wherein P. and v. are light source constants, and ri is a preset fixed value;
and
wherein
4X
,LI= X +15Y+ 3Z
9Y
v, =
X +15Y+ 3Z
31. The apparatus of the claim 30, wherein a round filter kernel k is used for
filtering pixels of
the subject region image.
32. The apparatus of the claim 31, wherein when a round filter kernel has a
diameter of 4
represented by:
1 1 1
1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
k = 1 1 1 1 x 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1
1 1 1
33. The apparatus of the claim 32, wherein the filtering equation is:
= P K = tzu (k) P,(i, j)c P}
22
Date Reçue/Date Received 2023-12-18
wherein (i,j) represents the pixel coordinates, P represents the subject
region image, Zy
(k)
represents the background purity value corresponding to the pixel, Zu
represents the
punctured neighborhood region corresponding to each pixel obtained using the
round
filter kemel k as the mask, B represents the dilates subject region image,
wherein the
background region image is updated when the subject region image is dilated,
and D
represents the updated background region image.
34. The apparatus of the claim 33, wherein the background region image is
fused with data of
the hue space component H to generate a result C.
35. The apparatus of the claim 34, wherein the fusion equation is as below:
c = ic(0) c(0) = H(i,j),(i,j) c
c(i,j) = 0, (0) E D
36. The apparatus of the claim 35, wherein (0) represents pixel coordinates,
H(i, j) represents
the background purity value corresponding to the pixel in the hue space
component H; and
wherein when a pixel located at coordinates (i,j) belongs to the dilated
background region
image D, the background purity of the pixel in the hue space component His
valuated.
37. The apparatus of the claim 36, wherein when the pixel located at
coordinates (i,j) does not
belong to the dilated background region image D, the background purity value
of the pixel
in the hue space component His valuated as zero.
38. The apparatus of the claim 37, wherein the coordinates of the pixels and
their corresponding
background purity values are gathered to form an array C, which is the array
composed of
the location coordinates of the pixels in the background region image D and
the
correspondingly converted background purity values.
39. The apparatus of the claim 38, wherein the predetermined first threshold
is compared to the
array C.
23
Date Recue/Date Received 2023-12-18
40. The apparatus of the claim 39, wherein when all the background purity
values
corresponding to the induvial pixels in the background region image D are
smaller than the
first background purity threshold, it is determined that the background purity
of the to-be-
detected picture is compliant.
41. The apparatus of the claim 40, the apparatus further comprising:
processing data of the
brightness space channel L by means of a fixed-threshold binarization
apparatus, so as to
obtain a first binarization result T.
42. The apparatus of the claim 41, the apparatus further comprising:
processing the data of the
brightness space channel L by means of a Gaussian-window binarization
apparatus, so as to
obtain a second binarization result G.
43. The apparatus of the claim 42, the apparatus further comprising: non-
coherence region
suppression is performed on the first binarization result T and the second
binarization result
G, respectively by means of the non-maximum suppression apparatus to nullify
the impact
of non-coherence regions caused by complicated a background on the detection
results,
thereby further improving detection precision.
44. The apparatus of the claim 43, the apparatus further comprising: fusing
the subject region
image recognized through pixel-level semantic segmentation with the first
binarizati on
result and the second binarization result, respectively.
45. An electronic system comprising:
at least one processor;
a memory, connected with the at least one processor;
wherein the memory stores an instruction executable by the at least one
processor
configured to:
acquire a to-be-detected picture that has been denoised, perform pixel-level
semantic segmentation on a denoised to-be-detected picture, and recognizing a
subject region image and a background region image;
24
Date Recue/Date Received 2023-12-18
perform hue space conversion on the to-be-detected picture, so as to output
hue
space data and brightness space data of the picture; and
fuse the subject region image after dilation processing with the hue space
data,
extract a background purity value corresponding to every pixel in the
background region image formed after dilation processing, and determine
whether background purity of the to-be-detected picture is compliant wherein a
compliant picture comprises any one or more of non-violent, non-
pornographic, blank, and aesthetic features, and wherein the aesthetic
features
include centered image subjects.
46. The system of claim 45, the system further comprising: processing the
brightness space data
by means of plural binarization systems, so as to output plural binarization
results
correspondingly.
47. The system of the claim 46, the system further comprising: fusing the
subject region image
with the plural binarization results.
48. The system of the claim 47, the system further comprising: extracting a
coordinate value of
every pixel in the fused subject region image and its corresponding background
purity value,
and determining whether a location of a subject in the to-be-detected picture
is compliant.
49. The system of the claim 48, wherein acquiring a to-be-detected picture
that has been
denoised, and after pixel-level semantic segmentation, recognizing a subject
region image
and a background region image comprises: denoising the to-be-detected picture
by means of
a nonlinear filtering system; and performing pixel-level semantic segmentation
on the
denoised to-be-detected picture through a multi-channel deep residual fully
convolutional
network model, so as to recognize the subject region image and the background
region
image.
Date Recue/Date Received 2023-12-18
50. The system of the claim 49, wherein performing hue space conversion on the
to-be-detected
picture to output hue space data and brightness space data of the picture
comprises: using
HSV hue space conversion system to convert the to-be-detected picture and
output the hue
space data of the picture, in which the hue space data include a hue space
component H; and
using LUV hue space conversion system to convert the to-be-detected picture
and output the
brightness space data of the picture, in which the brightness space data
include a brightness
space channel L.
51. The system of the claim 50, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: filtering edge pixels of the subject region image by means of a
filter kernel, so as
to dilate the subject region image.
52. The system of the claim 51, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: updating the part other than the dilated subject region image in
the to-be-
detected picture as the background region image.
53. The system of the claim 52, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: fusing the updated background region image with data of the hue
space
component H, and determining whether the background purity value corresponding
to every
pixel in the updated background region image is compliant to a first
threshold.
26
Date Recue/Date Received 2023-12-18
54. The system of the claim 53, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: deteimining that the background purity of the to-be-detected
picture is
compliant.
55. The system of the claim 54, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: deteimining that the background purity of the to-be-detected
picture is non-
compliant, and wherein the first threshold includes a first background purity
threshold.
56. The system of the claim 55, processing the brightness space data by means
of the plural
binarizati on systems to output the plural binarization results
correspondingly comprises:
processing data of the brightness space channel L by means of a fixed-
threshold binarization
system, so as to obtain a first binarization result.
57. The system of the claim 56, processing the brightness space data by means
of the plural
binarizati on systems to output the plural binarization results
correspondingly comprises:
processing the data of the brightness space channel L by means of a Gaussian-
window
binarizati on system, so as to obtain a second binarization result.
58. The system of the claim 57, the system further comprising: performing non-
coherence
region suppression on the first binarization result and the second binarizati
on result,
respectively, by means of a non-maximum suppression system.
27
Date Recue/Date Received 2023-12-18
59. The system of the claim 58, wherein fusing the subject region image with
the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises: fusing
the subject region image recognized through pixel-level semantic segmentation
with the first
binarizati on result and the second binarization result, respectively.
60. The system of the claim 59, wherein fusing the subject region image with
the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
extracting coordinate values of the pixels belonging to the subject region
image and the first
binarizati on result from fusing results and their corresponding background
purity values.
61. The system of the claim 60, wherein fusing the subject region image with
the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
extracting coordinate values of the pixels belonging to the subject region
image and the
second binarization result from fusing results and their corresponding
background purity
values.
62. The system of the claim 61, wherein fusing the subject region image with
the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
summarizing and extracting the coordinate values of the pixels and their
corresponding
background purity values, and determining whether both the coordinate value of
each pixel
and its corresponding background purity value are compliant to a second
threshold.
28
Date Recue/Date Received 2023-12-18
63. The system of the claim 62, wherein fusing the subject region image with
the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
determining that the location of the subject in the to-be-detected picture is
compliant.
64. The system of the claim 63, wherein fusing the subject region image with
the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
determining that the location of the subject in the to-be-detected picture is
non-compliant;
and wherein the second threshold includes a second background purity threshold
and a
location coordinate interval threshold.
65. The system of the claim 64, the system further comprising: identifying a
subject region
image and a background region image in a denoised to-be-detected picture
through pixel-
level semantic segmentation, and performing hue space conversion on the
picture to output
hue space data and brightness space data of the picture.
66. The system of the claim 65, the system further comprising: dilating the
subject region image
to dilate the range of edge pixels of the subject region image in order to
ensure complete
coverage over the subject region image.
67. The system of the claim 66, the system further comprising: dilating
subject region image is
fused with hue space data.
68. The system of the claim 67, the system further comprising: to detect the
location of the
subject in the picture, the brightness space data are first processed by means
of the plural
binarization systems so as to generate the plural binarization results.
69. The system of the claim 68, wherein the subject region image identified
through pixel-level
semantic segmentation is then fused with the plural binarization results,
respectively.
29
Date Recue/Date Received 2023-12-18
70. The system of the claim 69, wherein based on the coordinate value of every
pixel in the
fused subject region image and its corresponding background purity value,
whether the
location of the subject in the to-be-detected picture is compliant can be
determined.
71. The system of the claim 70, the system further comprising: employing HSV
(hue, saturation,
value) hue space conversion system to convert the to-be-detected picture in
the RGB (red,
geen and blue) color space into the HSV color space that is closer to human
visual
perceptual characteristics.
72. The system of the claim 71, wherein the conversion system includes:
-V= max(R,G, B)
s - max (R, G, B )- mm (R, G, B)
rnax (R, 0, B)
(G - B)
60x ___________________ S~ Oftmax(RAB)=R
Sx V
H = 60 x(2+ (B- R ))
aamax(R,G,B)= G
S x V
60x [4 + (R-G1 ~ Oftmax(RAB)=B
xV
= 1-1 +360 H<O
73. The system of the claim 72, wherein the features of brightness space data
include extensive
color gamut coverage, high visual consistency, and good capability of
expressing color
perception.
74. The system of the claim 73, wherein the implantation converting brightness
space data of
the picture to be examined through: converting the to-be-detected picture from
RGB space
data into CIE XYZ space data; and converting the CIE XYZ space data into LUV
brightness
space data using the following conversion equation:
X 0.412453 0.357580 0.180423 R
Y = 0.212671 0.715160 0.072169 G
Z 0.019334 0.119193 0.950227 B
Date Reçue/Date Received 2023-12-18
v N:31
Yni Yõ 29
L
29)1 Y
x
Yõ k.29
,u =13 xi_ x(11 ¨ ,uni)
v =13xLx(vi¨vni
wherein Pr, and v. are light source constants, and ri is a preset fixed value;
and
wherein
4X
,LI= X +15Y+ 3Z
9Y
v, =
X+15Y+ 3Z
75. The system of the claim 74, wherein a round filter kernel k is used for
filtering pixels of the
subject region image.
76. The system of the claim 75, wherein when a round filter kernel has a
diameter of 4
represented by:
1 1 1
1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
k = 1 1 1 1 x 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1
1 1 1
77. The system of the claim 76, wherein the filtering equation is:
= P K = tzu (k) P,(i, j)c
31
Date Reçue/Date Received 2023-12-18
wherein (i,j) represents the pixel coordinates, P represents the subject
region image, Zy
(k)
represents the background purity value corresponding to the pixel, Zu
represents the
punctured neighborhood region corresponding to each pixel obtained using the
round
filter kemel k as the mask, B represents the dilates subject region image,
wherein the
background region image is updated when the subject region image is dilated,
and D
represents the updated background region image.
78. The system of the claim 77, wherein the background region image is fused
with data of the
hue space component H to generate a result C.
79. The system of the claim 78, wherein the fusion equation is as below:
c = ic(0) c(i,j) = H(i,j),(i,j) c
c(i,j) = 0, (i,j) E D
80. The system of the claim 79, wherein (i,j) represents pixel coordinates,
H(i,j) represents the
background purity value corresponding to the pixel in the hue space component
11; and
wherein when a pixel located at coordinates (i,j) belongs to the dilated
background region
image D, the background purity of the pixel in the hue space component His
valuated.
81. The system of the claim 80, wherein when the pixel located at coordinates
(i,j) does not
belong to the dilated background region image D, the background purity value
of the pixel
in the hue space component His valuated as zero.
82. The system of the claim 81, wherein the coordinates of the pixels and
their corresponding
background purity values are gathered to form an array C, which is the array
composed of
the location coordinates of the pixels in the background region image D and
the
correspondingly converted background purity values.
83. The system of the claim 82, wherein the predetermined first threshold is
compared to the
array C.
32
Date Recue/Date Received 2023-12-18
84. The system of the claim 83, wherein when all the background purity values
corresponding
to the induvial pixels in the background region image D are smaller than the
first
background purity threshold, it is determined that the background purity of
the to-be-
detected picture is compliant.
85. The system of the claim 84, the system further comprising: processing data
of the brightness
space channel L by means of a fixed-threshold binarization system, so as to
obtain a first
binarization result T
86. The system of the claim 85, the system further comprising: processing the
data of the
brightness space channel L by means of a Gaussian-window binarization system,
so as to
obtain a second binarization result G.
87. The system of the claim 86, the system further comprising: non-coherence
region
suppression is performed on the first binarization result T and the second
binarization result
G, respectively by means of the non-maximum suppression system to nullify the
impact of
non-coherence regions caused by complicated a background on the detection
results, thereby
further improving detection precision.
88. The system of the claim 87, the system further comprising: fusing the
subject region image
recognized through pixel-level semantic segmentation with the first
binarization result and
the second binarization result, respectively.
89. A computer readable physical memory having stored thereon a computer
program executed
by a computer configured to:
acquire a to-be-detected picture that has been denoised, and perform pixel-
level semantic
segmentation on a denoised to-be-detected picture, recognizing a subject
region image
and a background region image;
perform hue space conversion on the to-be-detected picture to output hue space
data and
brightness space data of the picture; and
33
Date Recue/Date Received 2023-12-18
fuse the subject region image after dilation processing with the hue space
data, extract a
background purity value corresponding to every pixel in the background region
image
formed after dilation processing, and determine whether background purity of
the to-be-
detected picture is compliant, wherein a compliant picture comprises any one
or more of
non-violent, non-pornographic, blank, and aesthetic features, and wherein the
aesthetic
features include centred image subjects.
90. The memory of claim 89, the memory further comprising: processing the
brightness space
data by means of plural binarization memories, so as to output plural
binarization results
correspondingly.
91. The memory of the claim 90, the memory further comprising: fusing the
subject region
image with the plural binarization results.
92. The memory of the claim 91, the memory further comprising: extracting a
coordinate value
of every pixel in the fused subject region image and its corresponding
background purity
value, and determining whether a location of a subject in the to-be-detected
picture is
compliant.
93. The memory of the claim 92, wherein acquiring a to-be-detected picture
that has been
denoised, and after pixel-level semantic segmentation, recognizing a subject
region image
and a background region image comprises: denoising the to-be-detected picture
by means of
a nonlinear filtering memory; and performing pixel-level semantic segmentation
on the
denoised to-be-detected picture through a multi-channel deep residual fully
convolutional
network model, so as to recognize the subject region image and the background
region
image.
34
Date Recue/Date Received 2023-12-18
94. The memory of the claim 93, wherein performing hue space conversion on the
to-be-
detected picture to output hue space data and brightness space data of the
picture comprises:
using HSV hue space conversion memory to convert the to-be-detected picture
and output
the hue space data of the picture, in which the hue space data include a hue
space
component H; and using LUV hue space conversion memory to convert the to-be-
detected
picture and output the brightness space data of the picture, in which the
brightness space
data include a brightness space channel L.
95. The memory of the claim 94, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: filtering edge pixels of the subject region image by means of a
filter kernel, so as
to dilate the subject region image.
96. The memory of the claim 95, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: updating the part other than the dilated subject region image in
the to-be-
detected picture as the background region image.
97. The memory of the claim 96, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: fusing the updated background region image with data of the hue
space
component H, and determining whether the background purity value corresponding
to every
pixel in the updated background region image is compliant to a first
threshold.
Date Recue/Date Received 2023-12-18
98. The memory of the claim 97, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
detennining whether background purity of the to-be-detected picture is
compliant
comprises: deteimining that the background purity of the to-be-detected
picture is
compliant.
99. The memory of the claim 98, wherein fusing the subject region image after
dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image fomied after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: deteimining that the background purity of the to-be-detected
picture is non-
compliant, and wherein the first threshold includes a first background purity
threshold.
100. The memory of the claim 99, processing the brightness space data by means
of the plural
binarizati on memories to output the plural binarization results
correspondingly comprises:
processing data of the brightness space channel L by means of a fixed-
threshold binarization
memory, so as to obtain a first binarization result.
101. The memory of the claim 100, processing the brightness space data by
means of the plural
binarizati on memories to output the plural binarization results
correspondingly comprises:
processing the data of the brightness space channel L by means of a Gaussian-
window
binarizati on memory, so as to obtain a second binarization result.
102. The memory of the claim 101, the memory further comprising: performing
non-coherence
region suppression on the first binarization result and the second binarizati
on result,
respectively, by means of a non-maximum suppression memory.
36
Date Recue/Date Received 2023-12-18
103. The memory of the claim 102, wherein fusing the subject region image with
the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises: fusing
the subject region image recognized through pixel-level semantic segmentation
with the first
binarizati on result and the second binarization result, respectively.
104. The memory of the claim 103, wherein fusing the subject region image with
the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
extracting coordinate values of the pixels belonging to the subject region
image and the first
binarizati on result from fusing results and their corresponding background
purity values.
105. The memory of the claim 104, wherein fusing the subject region image with
the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
extracting coordinate values of the pixels belonging to the subject region
image and the
second binarization result from fusing results and their corresponding
background purity
values.
106. The memory of the claim 105, wherein fusing the subject region image with
the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
summarizing and extracting the coordinate values of the pixels and their
corresponding
background purity values, and determining whether both the coordinate value of
each pixel
and its corresponding background purity value are compliant to a second
threshold.
37
Date Recue/Date Received 2023-12-18
107. The memory of the claim 106, wherein fusing the subject region image with
the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
determining that the location of the subject in the to-be-detected picture is
compliant.
108. The memory of the claim 107, wherein fusing the subject region image with
the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
determining that the location of the subject in the to-be-detected picture is
non-compliant;
and wherein the second threshold includes a second background purity threshold
and a
location coordinate interval threshold.
109. The memory of the claim 108, the memory further comprising: identifying a
subject region
image and a background region image in a denoised to-be-detected picture
through pixel-
level semantic segmentation, and performing hue space conversion on the
picture to output
hue space data and brightness space data of the picture.
110. The memory of the claim 109, the memory further comprising: dilating the
subject region
image to dilate the range of edge pixels of the subject region image in order
to ensure
complete coverage over the subject region image.
111. The memory of the claim 110, the memory further comprising: dilating
subject region image
is fused with hue space data.
112. The memory of the claim 111, the memory further comprising: to detect the
location of the
subject in the picture, the brightness space data are first processed by means
of the plural
binarization memories so as to generate the plural binarization results.
113. The memory of the claim 112, wherein the subject region image identified
through pixel-
level semantic segmentation is then fused with the plural binarization
results, respectively.
38
Date Recue/Date Received 2023-12-18
114. The memory of the claim 113, wherein based on the coordinate value of
every pixel in the
fused subject region image and its corresponding background purity value,
whether the
location of the subject in the to-be-detected picture is compliant can be
determined.
115. The memory of the claim 114, the memory further comprising: employing HSV
(hue,
saturation, value) hue space conversion memory to convert the to-be-detected
picture in the
RGB (red, green and blue) color space into the HSV color space that is closer
to human
visual perceptual characteristics.
116. The memory of the claim 115, wherein the conversion memory includes:
-V= max(R,G, B)
s - max (R, G, B )- mm (R, G, B)
rnax (R, 0, B)
(G - B)
60x ___________________ S~ Oftmax(RAB)=R
Sx V
= 60 x(2+ (-
B
H R))
aamax(R,G,B)= G
S x V
60x [4+ (R-G1 ~ Oftmax(RAB)=B
r7 g x
= 1-1 +360 H<O
117. The memory of the claim 116, wherein the features of brightness space
data include
extensive color gamut coverage, high visual consistency, and good capability
of expressing
color perception.
118. The memory of the claim 117, wherein the implantation converting
brightness space data of
the picture to be examined through: converting the to-be-detected picture from
RGB space
data into CIE XYZ space data; and converting the CIE XYZ space data into LUV
brightness
space data using the following conversion equation:
X 0.412453 0.357580 0.180423 R
Y = 0.212671 0.715160 0.072169 G
Z 0.019334 0.119193 0.950227 B
39
Date Reçue/Date Received 2023-12-18
v N:31
L
29)1 Y
x,13
Yõ k.29
,u =13 xi_ x(11 ¨ ,uni)
v =13xLx(vi¨vni
wherein Pr, and v. are light source constants, and ri is a preset fixed value;
and
wherein
4X
,LI= X +15Y+ 3Z
9Y
v, =
X+15Y+ 3Z
119. The memory of the claim 118, wherein a round filter kernel k is used for
filtering pixels of
the subject region image.
120. The memory of the claim 119, wherein when a round filter kernel has a
diameter of 4
represented by:
1 1 1
1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
k = 1 1 1 1 x 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1
1 1 1
121. The memory of the claim 120, wherein the filtering equation is:
= P K = fzu 1 (k) P,(i, j)c
Date Reçue/Date Received 2023-12-18
wherein (i,j) represents the pixel coordinates, P represents the subject
region image, Zy
(k)
represents the background purity value corresponding to the pixel, Zu
represents the
punctured neighborhood region corresponding to each pixel obtained using the
round
filter kemel k as the mask, B represents the dilates subject region image,
wherein the
background region image is updated when the subject region image is dilated,
and D
represents the updated background region image.
122. The memory of the claim 121, wherein the background region image is fused
with data of
the hue space component H to generate a result C.
123. The memory of the claim 122, wherein the fusion equation is as below:
c(i,j) = H(i,j),(i,j) c
C = ic(i,j)
c(i,j) = 0, (i,j) E D 5;
124. The memory of the claim 122, wherein (i,j) represents pixel coordinates,
H (i, j) represents
the background purity value corresponding to the pixel in the hue space
component H; and
wherein when a pixel located at coordinates (i,j) belongs to the dilated
background region
image D, the background purity of the pixel in the hue space component His
valuated.
125. The memory of the claim 124, wherein when the pixel located at
coordinates (i,j) does not
belong to the dilated background region image D, the background purity value
of the pixel
in the hue space component His valuated as zero.
126. The memory of the claim 125, wherein the coordinates of the pixels and
their corresponding
background purity values are gathered to form an array C, which is the array
composed of
the location coordinates of the pixels in the background region image D and
the
correspondingly converted background purity values.
127. The memory of the claim 126, wherein the predetermined first threshold is
compared to the
array C.
41
Date Recue/Date Received 2023-12-18
128. The memory of the claim 127, wherein when all the background purity
values
corresponding to the induvial pixels in the background region image D are
smaller than the
first background purity threshold, it is determined that the background purity
of the to-be-
detected picture is compliant.
129. The memory of the claim 128, the memory further comprising: processing
data of the
brightness space channel L by means of a fixed-threshold binarization memory,
so as to
obtain a first binarization result T.
130. The memory of the claim 129, the memory further comprising: processing
the data of the
brightness space channel L by means of a Gaussian-window binarization memory,
so as to
obtain a second binarization result G.
131. The memory of the claim 130, the memory further comprising: non-coherence
region
suppression is performed on the first binarization result T and the second
binarization result
G, respectively by means of the non-maximum suppression memory to nullify the
impact of
non-coherence regions caused by complicated a background on the detection
results, thereby
further improving detection precision.
132. The memory of the claim 131, the memory further comprising: fusing the
subject region
image recognized through pixel-level semantic segmentation with the first
binarizati on
result and the second binarization result, respectively.
133. A picture-detecting method, the method comprising:
acquiring a to-be-detected picture that has been denoised, perform pixel-level
semantic
segmentation on a denoised to-be-detected picture, and recognizing a subject
region image
and a background region image;
performing hue space conversion on the to-be-detected picture, so as to output
hue space data
and brightness space data of the picture; and
42
Date Recue/Date Received 2023-12-18
fusing the subject region image after dilation processing with the hue space
data, extracting a
background purity value corresponding to every pixel in the background region
image formed
after dilation processing, and determining whether background purity of the to-
be-detected
picture is compliant, wherein a compliant picture comprises any one or more of
non-violent,
non-pornographic, blank, and aesthetic features, and wherein the aesthetic
features include
centred image subjects.
134. The method of claim 133, the method further comprising: processing the
brightness space
data by means of plural binarization methods, so as to output plural
binarization results
correspondingly.
135. The method of the claim 134, the method further comprising: fusing the
subject region
image with the plural binarization results.
136. The method of the claim 135, the method further comprising: extracting a
coordinate value
of every pixel in the fused subject region image and its corresponding
background purity
value, and determining whether a location of a subject in the to-be-detected
picture is
compliant.
137. The method of the claim 136, wherein acquiring a to-be-detected picture
that has been
denoised, and after pixel-level semantic segmentation, recognizing a subject
region image
and a background region image comprises: denoising the to-be-detected picture
by means of
a nonlinear filleting method; and performing pixel-level semantic segmentation
on the
denoised to-be-detected picture through a multi-channel deep residual fully
convolutional
network model, so as to recognize the subject region image and the background
region
image.
43
Date Recue/Date Received 2023-12-18
138. The method of the claim 137, wherein performing hue space conversion on
the to-be-
detected picture to output hue space data and brightness space data of the
picture comprises:
using HSV hue space conversion method to convert the to-be-detected picture
and output
the hue space data of the picture, in which the hue space data include a hue
space
component H; and using LUV hue space conversion method to convert the to-be-
detected
picture and output the brightness space data of the picture, in which the
brightness space
data include a brightness space channel L.
139. The method of the claim 138, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: filtering edge pixels of the subject region image by means of a
filter kernel, so as
to dilate the subject region image.
140. The method of the claim 139, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: updating the part other than the dilated subject region image in
the to-be-
detected picture as the background region image.
141. The method of the claim 140, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: fusing the updated background region image with data of the hue
space
component H, and determining whether the background purity value corresponding
to every
pixel in the updated background region image is compliant to a first
threshold.
44
Date Recue/Date Received 2023-12-18
142. The method of the claim 141, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
detennining whether background purity of the to-be-detected picture is
compliant
comprises: deteimining that the background purity of the to-be-detected
picture is
compliant.
143. The method of the claim 142, wherein fusing the subject region image
after dilation
processing with the hue space data, extracting a background purity value
corresponding to
every pixel in the background region image formed after dilation processing,
and
determining whether background purity of the to-be-detected picture is
compliant
comprises: deteimining that the background purity of the to-be-detected
picture is non-
compliant, and wherein the first threshold includes a first background purity
threshold.
144. The method of the claim 143, processing the brightness space data by
means of the plural
binarizati on methods to output the plural binarization results
correspondingly comprises:
processing data of the brightness space channel L by means of a fixed-
threshold binarization
method, so as to obtain a first binarization result.
145. The method of the claim 144, processing the brightness space data by
means of the plural
binarizati on methods to output the plural binarization results
correspondingly comprises:
processing the data of the brightness space channel L by means of a Gaussian-
window
binarizati on method, so as to obtain a second binarizati on result.
146. The method of the claim 145, the method further comprising: performing
non-coherence
region suppression on the first binarization result and the second binarizati
on result,
respectively, by means of a non-maximum suppression method.
Date Recue/Date Received 2023-12-18
147. The method of the claim 146, wherein fusing the subject region image with
the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
deteimining
whether a location of a subject in the to-be-detected picture is compliant
comprises: fusing
the subject region image recognized through pixel-level semantic segmentation
with the first
binarizati on result and the second binarization result, respectively.
148. The method of the claim 147, wherein fusing the subject region image with
the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
extracting coordinate values of the pixels belonging to the subject region
image and the first
binarizati on result from fusing results and their corresponding background
purity values.
149. The method of the claim 148, wherein fusing the subject region image with
the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
extracting coordinate values of the pixels belonging to the subject region
image and the
second binarization result from fusing results and their corresponding
background purity
values.
150. The method of the claim 149, wherein fusing the subject region image with
the plural
binarizati on results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
summarizing and extracting the coordinate values of the pixels and their
corresponding
background purity values, and determining whether both the coordinate value of
each pixel
and its corresponding background purity value are compliant to a second
threshold.
46
Date Recue/Date Received 2023-12-18
151. The method of the claim 150, wherein fusing the subject region image with
the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
determining that the location of the subject in the to-be-detected picture is
compliant.
152. The method of the claim 151, wherein fusing the subject region image with
the plural
binarization results, respectively, extracting a coordinate value of every
pixel in the fused
subject region image and its corresponding background purity value, and
determining
whether a location of a subject in the to-be-detected picture is compliant
comprises:
determining that the location of the subject in the to-be-detected picture is
non-compliant;
and wherein the second threshold includes a second background purity threshold
and a
location coordinate interval threshold.
153. The method of the claim 152, the method further comprising: identifying a
subject region
image and a background region image in a denoised to-be-detected picture
through pixel-
level semantic segmentation, and performing hue space conversion on the
picture to output
hue space data and brightness space data of the picture.
154. The method of the claim 153, the method further comprising: dilating the
subject region
image to dilate the range of edge pixels of the subject region image in order
to ensure
complete coverage over the subject region image.
155. The method of the claim 154, the method further comprising: dilating
subject region image
is fused with hue space data.
156. The method of the claim 155, the method further comprising: to detect the
location of the
subject in the picture, the brightness space data are first processed by means
of the plural
binarization methods so as to generate the plural binarization results.
157. The method of the claim 156, wherein the subject region image identified
through pixel-
level semantic segmentation is then fused with the plural binarization
results, respectively.
47
Date Reçue/Date Received 2023-12-18
158. The method of the claim 157, wherein based on the coordinate value of
every pixel in the
fused subject region image and its corresponding background purity value,
whether the
location of the subject in the to-be-detected picture is compliant can be
determined.
159. The method of the claim 158, the method further comprising: employing HSV
(hue,
saturation, value) hue space conversion method to convert the to-be-detected
picture in the
RGB (red, green and blue) color space into the HSV color space that is closer
to human
visual perceptual characteristics.
160. The method of the claim 159, wherein the conversion method includes:
-V= max(R,G, B)
s - max (R, G, B )- mm (R, G, B)
rnax (R, 0, B)
(G - B)
60x ___________________ S~ Oftmax(RAB)=R
Sx V
H = 60 x(2+ (B- R))
aamax(R,G,B)= G
S x V
60x [4 + (R-G1 ~ Oftmax(RAB)=B
xV
= 1-1 +360 H<O
161. The method of the claim 160, wherein the features of brightness space
data include
extensive color gamut coverage, high visual consistency, and good capability
of expressing
color perception.
162. The method of the claim 161, wherein the implantation converting
brightness space data of
the picture to be examined through: converting the to-be-detected picture from
RGB space
data into CIE XYZ space data; and converting the CIE XYZ space data into LUV
brightness
space data using the following conversion equation:
X 0.412453 0.357580 0.180423 R
Y = 0.212671 0.715160 0.072169 G
Z 0.019334 0.119193 0.950227 B
48
Date Reçue/Date Received 2023-12-18
v
L
29)1 Y
x,13
Yõ k.29
,u =13 xi_ x(11 ¨ ,uni)
v =13xLx(vi¨vni
wherein Pr, and v. are light source constants, and ri is a preset fixed value;
and
wherein
4X
,LI= X +15Y+ 3Z
9Y
v, =
X+15Y+ 3Z
=
163. The method of the claim 162, wherein a round filter kernel k is used for
filtering pixels of
the subject region image.
164. The method of the claim 163, wherein when a round filter kernel has a
diameter of 4
represented by:
1 1 1
1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1
k = 1 1 1 1 x 1 1 1 1
1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1
1 1 1 1 1
1 1 1
165. The method of the claim 164, wherein the filtering equation is:
= P K = tzu 1 (k) P,(i, j)c
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Date Reçue/Date Received 2023-12-18
wherein (i,j) represents the pixel coordinates, P represents the subject
region image, Zy
(k)
represents the background purity value corresponding to the pixel, Zu
represents the
punctured neighborhood region corresponding to each pixel obtained using the
round
filter kemel k as the mask, B represents the dilates subject region image,
wherein the
background region image is updated when the subject region image is dilated,
and D
represents the updated background region image.
166. The method of the claim 165, wherein the background region image is fused
with data of the
hue space component H to generate a result C.
167. The method of the claim 166, wherein the fusion equation is as below:
c(i,j) = H(i,j),(i,j) c
C = ic(i,j)
c(i,j) = 0, (i,j) E D 5;
168. The method of the claim 166, wherein (i,j) represents pixel coordinates,
H (i, j) represents
the background purity value corresponding to the pixel in the hue space
component H; and
wherein when a pixel located at coordinates (i,j) belongs to the dilated
background region
image D, the background purity of the pixel in the hue space component His
valuated.
169. The method of the claim 168, wherein when the pixel located at
coordinates (i,j) does not
belong to the dilated background region image D, the background purity value
of the pixel
in the hue space component His valuated as zero.
170. The method of the claim 169, wherein the coordinates of the pixels and
their corresponding
background purity values are gathered to form an array C, which is the array
composed of
the location coordinates of the pixels in the background region image D and
the
correspondingly converted background purity values.
171. The method of the claim 170, wherein the predetermined first threshold is
compared to the
array C.
Date Recue/Date Received 2023-12-18
172. The method of the claim 170, wherein when all the background purity
values corresponding
to the induvial pixels in the background region image D are smaller than the
first
background purity threshold, it is determined that the background purity of
the to-be-
detected picture is compliant.
173. The method of the claim 172, the method further comprising: processing
data of the
brightness space channel L by means of a fixed-threshold binarization method,
so as to
obtain a first binarization result T.
174. The method of the claim 173, the method further comprising: processing
the data of the
brightness space channel L by means of a Gaussian-window binarizafion method,
so as to
obtain a second binarization result G.
175. The method of the claim 174, the method further comprising: non-coherence
region
suppression is performed on the first binarization result T and the second
binarization result
G, respectively by means of the non-maximum suppression method to nullify the
impact of
non-coherence regions caused by complicated a background on the detection
results, thereby
further improving detection precision.
176. The method of the claim 175, the method further comprising: fusing the
subject region
image recognized through pixel-level semantic segmentation with the first
binarizati on
result and the second binarization result, respectively.
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Date Recue/Date Received 2023-12-18
