CN108764089A - Human body back midspinal line recognition methods based on depth camera - Google Patents
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Abstract
The human body back midspinal line recognition methods based on depth camera that the invention discloses a kind of, it includes obtaining human body back depth image, gridding is handled, curvatureization processing, extract dorsal midline region, grand bone point coordinates and hipbone point coordinates are obtained, cubic spline interpolation process of fitting treatment is carried out, obtains human body back midspinal line.The present invention carries out curvature processing by the human body back depth image to acquisition, extract human body back information, and the partial spinal column center line of extraction is handled using cubic curve row interpolation approximating method, complete human body back midspinal line can be obtained, and then improve the precision of midspinal line extraction.
Description
Technical field
The invention belongs to technical field of machine vision, and in particular to a kind of human body back midspinal line based on depth camera
Recognition methods.
Background technology
Scoliosis can also be referred to as spine convexity, have the characteristics that multiple, moreover, in recent years, teenager occurs
The case where spine convexity, is just increasing year by year, and a degree of influence is caused on teen-age life and later work.Therefore
Backbone detection, which has, to be quite widely applied.The currently used method of scoliosis is scoliosis ruler, and Adams bends forward examination
It tests.However, due to the shortcomings of manual detection efficiency is low, labor intensity is big, error-prone, in ridge today that machine vision continues to develop
The detection of column lateral bending just gradually develops to computer from artificial treatment and automatically processes.
The extraction of backbone curve is vital for a detecting system with fitting.Traditional scoliosis carries
It takes, can there is following drawback.First, with scoliosis ruler come measure the rotation angle of trunk, Adams bend forward experiment etc.
Method can have heavy workload, and when generally investigating a large amount of crowds, and artificial detection can become comparable cumbersome, efficiency
It is very low, and doctor is also possible to cause to misjudge and judge by accident because of fatigue.Second, the X-ray line used in scoliosis detection
With a certain amount of radioactivity, adolescent growth is developed totally unfavorable.And external harmless detection price newly developed very
High, this is difficult to allow the general patient of general hospital to use, thus develop harmless reliable and efficient detection device and can be
Backbone test problems bring great improvement.
Invention content
The present invention goal of the invention be：In order to solve problem above existing in the prior art, the present invention proposes one kind
Human body back midspinal line recognition methods based on depth camera.
The technical scheme is that：A kind of human body back midspinal line recognition methods based on depth camera, including with
Lower step：
A, human body back depth image is obtained using depth camera；
B, the human body back depth image for obtaining step A carries out gridding processing, obtains grid matrix；
C, the grid matrix for obtaining step B carries out curvature processing, obtains curvature matrix；
D, the curvature matrix extraction dorsal midline region obtained according to step C；
E, grand bone point coordinates and hipbone point coordinates are obtained, cubic spline is carried out in conjunction with the obtained dorsal midline regions step D
Interpolation fitting processing, obtains human body back midspinal line.
Further, the grid matrix that the step C obtains step B carries out curvature processing, obtains curvature matrix, has
Body is：The second dervative and first derivative of the grid matrix that calculating step B is obtained in the horizontal direction, then combined using curvature formulations
Obtained second dervative and first derivative obtains curvature matrix.
Further, the curvature formulations are expressed as：
Wherein, k indicates that curvature, y are the depth image that depth camera obtains.
Further, it is negative curvature value zero setting that the step C, which further includes by curvature matrix mean curvature, bent in curvature matrix
Rate is more than 1 curvature value zero setting, the curvature value zero setting isolated in curvature matrix.
Further, the curvature matrix extraction dorsal midline region that the step D is obtained according to step C, specially：It will be bent
Rate matrix carries out etching operation, and is divided into multiple characteristic areas；Isolated point is removed again, and carries out expansive working；Extract curvature
The largest connected domain of matrix, obtains dorsal midline region.
Further, the step E obtains grand bone point coordinates and hipbone point coordinates, the dorsal midline obtained in conjunction with step D
Region carries out cubic spline interpolation process of fitting treatment, obtains human body back midspinal line, specially：Obtain grand bone point coordinates and hipbone
Point coordinates is simultaneously converted into depth image coordinate；The right boundary in the dorsal midline region that step D is obtained is taken into boundary intermediate value, is obtained
Partial spinal column center line；Choose grand bone point coordinates and the discrete point coordinates and two on the straight line that vertex is connected on partial spinal column center line
Point coordinates and the discrete point coordinates on the straight line that vertex is connected under partial spinal column center line, bound fraction backbone in a hipbone point line
Center line carries out cubic spline interpolation process of fitting treatment, obtains human body back midspinal line.
Further, described to choose grand bone point coordinates and the discrete point seat on the straight line that vertex is connected on partial spinal column center line
Point coordinates and the discrete point coordinates on the straight line that vertex is connected under partial spinal column center line in mark and two hipbone point lines, in conjunction with
Partial spinal column center line carry out cubic spline interpolation process of fitting treatment, obtain human body back midspinal line, specifically include it is following step by step：
S1, structure cubic spline interpolation model of fit；
S2, the boundary coordinate that equation is determined according to grand bone point and two hipbone point line midpoints build triple diagonal matrix side
Journey group；
S3, the tri-diagonal matrix equation group built using chasing method solution procedure S2, obtain diagonal line parameter；
S4, the cubic equation between each point is reconstructed according to the diagonal line parameter that step S3 is obtained, obtains cubic spline difference system
Number；
S5, in each section xi<x<In xi+1, the separate equation that step S4 is reconstructed is described point by point, after being fitted
Backbone curve.
Further, the cubic spline interpolation model of fit is embodied as：
Pi(x)=ai+bi(x-xi)+ci(x-xi)2+di(x-xi)3
Wherein, ai,bi,ci,diFor cubic spline difference coefficient, xiFor the x coordinate of i-th of discrete point, x is the fitting generated
The x coordinate of curve.
Further, the tri-diagonal matrix equation group is embodied as：
Wherein, miFor the derivative of i-th section of matched curve, hiFor the y-coordinate of i-th of discrete point, mi=Pi′(xi), hi=
xi+1-xi。
Further, the cubic spline difference coefficient is embodied as：
The beneficial effects of the invention are as follows：The present invention carries out curvature processing by the human body back depth image to acquisition,
Human body back information is extracted, and the partial spinal column center line of extraction is handled using cubic curve row interpolation approximating method, it can
Complete human body back midspinal line is obtained, and then improves the precision of midspinal line extraction.
Description of the drawings
Fig. 1 is the flow diagram of the human body back midspinal line recognition methods based on depth camera of the present invention.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.
As shown in Figure 1, the flow signal of the human body back midspinal line recognition methods based on depth camera for the present invention
Figure.A kind of human body back midspinal line recognition methods based on depth camera, includes the following steps：
A, human body back depth image is obtained using depth camera；
B, the human body back depth image for obtaining step A carries out gridding processing, obtains grid matrix；
C, the grid matrix for obtaining step B carries out curvature processing, obtains curvature matrix；
D, the curvature matrix extraction dorsal midline region obtained according to step C；
E, grand bone point coordinates and hipbone point coordinates are obtained, cubic spline is carried out in conjunction with the obtained dorsal midline regions step D
Interpolation fitting processing, obtains human body back midspinal line.
In an alternate embodiment of the present invention where, above-mentioned steps A obtains human body back depth image using depth camera,
Human body is using removing jacket and upright stance.
In an alternate embodiment of the present invention where, the human body back depth image that above-mentioned steps B obtains step A uses
Quickhull algorithms carry out gridding processing, and it is 200*200 grid matrixs to obtain size.
In an alternate embodiment of the present invention where, the grid matrix that above-mentioned steps C obtains step B carries out at curvature
Reason, obtains curvature matrix, specially：The second dervative and first derivative of the grid matrix that calculating step B is obtained in the horizontal direction,
Use curvature formulations that obtained second dervative and first derivative is combined to obtain curvature matrix again.Here curvature formulations are expressed as：
Wherein, k indicates that curvature, y are the depth image data that depth camera obtains, and y ' is that the single order of depth image data is led
Number, y " are the second dervative of depth image data.
After obtaining curvature matrix, then by curvature matrix mean curvature it is negative curvature value zero setting, curvature matrix mean curvature is more than 1
Curvature value zero setting, the curvature value isolated in curvature matrix and lower or so without curvature value part zero setting on it.
In an alternate embodiment of the present invention where, above-mentioned steps D is extracted according to the curvature matrix that step C is obtained in back
Line region, specially：Curvature matrix is first subjected to etching operation, and is divided into multiple characteristic areas, including shoulder area, back
Mid line region, lumbar region；Isolated point is removed again, by isolated point zero setting, and carries out expansive working, removes the hair on each region
Thorn；The largest connected domain of curvature matrix is finally extracted, largest connected domain here is dorsal midline region.
In an alternate embodiment of the present invention where, above-mentioned steps obtain grand bone point coordinates and hipbone point coordinates, in conjunction with step
The dorsal midline region that rapid D is obtained carries out cubic spline interpolation process of fitting treatment, obtains human body back midspinal line, specially：Root
Grand bone point coordinates and hipbone point coordinates are obtained according to three-dimensional color image, and respectively converts grand bone point coordinates to hipbone point coordinates
Depth image coordinate；The right boundary in the dorsal midline region that step D is obtained is taken into boundary intermediate value again, is obtained in partial spinal column
Line；Choose grand bone point coordinates on the straight line that vertex is connected on partial spinal column center line discrete point coordinates and two hipbone points connect
Point coordinates and the discrete point coordinates on the straight line that vertex is connected under partial spinal column center line in line, bound fraction midspinal line carry out three
Secondary spline interpolation process of fitting treatment, obtains human body back midspinal line, specifically include it is following step by step：
S1, structure cubic spline interpolation model of fit, are embodied as：
Pi(x)=ai+bi(x-xi)+ci(x-xi)2+di(x-xi)3
Wherein, ai,bi,ci,diFor cubic spline difference coefficient, xiFor the x coordinate of i-th of discrete point, that is, extract in back
The x coordinate in line region, x are the x coordinate of the matched curve generated；
S2, the boundary coordinate that equation is determined according to grand bone point and two hipbone point line midpoints build triple diagonal matrix side
Journey group, is embodied as：
Wherein, miFor the derivative of i-th section of matched curve, hiFor the y-coordinate of i-th of discrete point, mi=Pi′(xi), hi=
xi+1-xi；
S3, the tri-diagonal matrix equation group built using chasing method solution procedure S2, obtain diagonal line parameter；
S4, the cubic equation between each point is reconstructed according to the diagonal line parameter that step S3 is obtained, obtains cubic spline difference system
Number, is embodied as：
S5, in each section xi<x<In xi+1, the separate equation that step S4 is reconstructed is described point by point, after being fitted
Backbone curve.
Those of ordinary skill in the art will understand that the embodiments described herein, which is to help reader, understands this hair
Bright principle, it should be understood that protection scope of the present invention is not limited to such specific embodiments and embodiments.This field
Those of ordinary skill can make according to the technical disclosures disclosed by the invention various does not depart from the other each of essence of the invention
The specific variations and combinations of kind, these variations and combinations are still within the scope of the present invention.
Claims (10)
1. a kind of human body back midspinal line recognition methods based on depth camera, which is characterized in that include the following steps：
A, human body back depth image is obtained using depth camera；
B, the human body back depth image for obtaining step A carries out gridding processing, obtains grid matrix；
C, the grid matrix for obtaining step B carries out curvature processing, obtains curvature matrix；
D, the curvature matrix extraction dorsal midline region obtained according to step C；
E, grand bone point coordinates and hipbone point coordinates are obtained, cubic spline interpolation is carried out in conjunction with the obtained dorsal midline regions step D
Process of fitting treatment obtains human body back midspinal line.
2. the human body back midspinal line recognition methods based on depth camera as described in claim 1, which is characterized in that described
The grid matrix that step C obtains step B carries out curvature processing, obtains curvature matrix, specially：Calculate what step B was obtained
The second dervative and first derivative of grid matrix in the horizontal direction, then combine obtained second dervative and single order using curvature formulations
Derivative obtains curvature matrix.
3. the human body back midspinal line recognition methods based on depth camera as claimed in claim 2, which is characterized in that described
Curvature formulations are expressed as：
Wherein, k indicates that curvature, y are the depth image that depth camera obtains.
4. the human body back midspinal line recognition methods based on depth camera as claimed in claim 3, which is characterized in that described
It is the curvature value zero setting born that step C, which further includes by curvature matrix mean curvature, and curvature matrix mean curvature is more than 1 curvature value zero setting,
The curvature value zero setting isolated in curvature matrix.
5. the human body back midspinal line recognition methods based on depth camera as claimed in claim 4, which is characterized in that described
The curvature matrix extraction dorsal midline region that step D is obtained according to step C, specially：Curvature matrix is subjected to etching operation, and
It is divided into multiple characteristic areas；Isolated point is removed again, and carries out expansive working；The largest connected domain for extracting curvature matrix, obtains
Dorsal midline region.
6. the human body back midspinal line recognition methods based on depth camera as claimed in claim 5, which is characterized in that described
Step E obtains grand bone point coordinates and hipbone point coordinates, and it is quasi- to carry out cubic spline interpolation in conjunction with the obtained dorsal midline regions step D
Conjunction is handled, and obtains human body back midspinal line, specially：It obtains grand bone point coordinates and hipbone point coordinates and is converted into depth image
Coordinate；The right boundary in the dorsal midline region that step D is obtained is taken into boundary intermediate value, obtains partial spinal column center line；Choose grand bone
Point coordinates on the straight line that vertex is connected on partial spinal column center line discrete point coordinates and two hipbone point lines in point coordinates with
Discrete point coordinates under partial spinal column center line on the connected straight line in vertex, it is quasi- that bound fraction midspinal line carries out cubic spline interpolation
Conjunction is handled, and obtains human body back midspinal line.
7. the human body back midspinal line recognition methods based on depth camera as claimed in claim 6, which is characterized in that described
Choose grand bone point coordinates on the straight line that vertex is connected on partial spinal column center line discrete point coordinates and two hipbone point lines in
Point coordinates and the discrete point coordinates on the straight line that vertex is connected under partial spinal column center line, bound fraction midspinal line carry out sample three times
The processing of interpolation fitting, obtains human body back midspinal line, specifically include it is following step by step：
S1, structure cubic spline interpolation model of fit；
S2, the boundary coordinate that equation is determined according to grand bone point and two hipbone point line midpoints build tri-diagonal matrix equation group；
S3, the tri-diagonal matrix equation group built using chasing method solution procedure S2, obtain diagonal line parameter；
S4, the cubic equation between each point is reconstructed according to the diagonal line parameter that step S3 is obtained, obtains cubic spline difference coefficient；
S5, in each section xi<x<In xi+1, the separate equation that step S4 is reconstructed is described point by point, the backbone after being fitted
Curve.
8. the human body back midspinal line recognition methods based on depth camera as claimed in claim 7, which is characterized in that described
Cubic spline interpolation model of fit is embodied as：
Pi(x)=ai+bi(x-xi)+ci(x-xi)2+di(x-xi)3
Wherein, ai,bi,ci,diFor cubic spline difference coefficient, xiFor the x coordinate of i-th of discrete point, x is the matched curve generated
X coordinate.
9. the human body back midspinal line recognition methods based on depth camera as claimed in claim 8, which is characterized in that described
Tri-diagonal matrix equation group is embodied as：
Wherein, miFor the derivative of i-th section of matched curve, hiFor the y-coordinate of i-th of discrete point, mi=Pi′(xi), hi=xi+1-xi。
10. the human body back midspinal line recognition methods based on depth camera as claimed in claim 9, which is characterized in that institute
Cubic spline difference coefficient is stated to be embodied as：
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| CN111685770A (en) * | 2019-03-12 | 2020-09-22 | 香港理工大学深圳研究院 | Wearable human body back curve detection method and device |
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