CN109145821B - Method and device for positioning pupil image in human eye image - Google Patents
Method and device for positioning pupil image in human eye image Download PDFInfo
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- CN109145821B CN109145821B CN201810959954.3A CN201810959954A CN109145821B CN 109145821 B CN109145821 B CN 109145821B CN 201810959954 A CN201810959954 A CN 201810959954A CN 109145821 B CN109145821 B CN 109145821B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
- G06V40/19—Sensors therefor
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
- G06V40/193—Preprocessing; Feature extraction
Abstract
The invention discloses a method and a device for positioning pupil images, wherein the method comprises the following steps: determining a plurality of edge points of the pupil image to fit the initial elliptical model; selecting at least three sampling edge points, determining sampling tangent lines corresponding to the sampling edge points respectively in the initial elliptical model, and determining the center of a sampling pupil according to the sampling edge points and the sampling tangent lines; determining the center of a calibrated pupil according to the non-sampling edge points and the tangent line, the sampling edge points and the sampling tangent line which correspond to the non-sampling edge points and the initial elliptical model; when the distance between the calibration pupil center and the sampling pupil center is not more than the set distance, determining the corresponding non-sampling edge point as a credible edge point; determining the ratio of the total amount of the credible edge points to the total amount of the edge points; and fitting the target ellipse model according to the credible edge points, and marking the position of the pupil image. According to the technical scheme, the pupil image can be more accurately positioned.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for positioning pupil images in human eye images.
Background
When the face recognition or the iris recognition is implemented, the pupil image carried by the image of the eye needs to be positioned, that is, the position of the pupil image is marked in the image of the eye.
At present, when positioning a pupil image carried by a human eye image, a plurality of edge points of the pupil image are generally required to be determined in the human eye image, an ellipse model is fitted according to a pupil center and each edge point, and the position of the pupil image is marked on the human eye image through the fitted ellipse model.
In the technical scheme, a large number of noise points caused by eyelashes and eyelids may exist in each determined edge point, and when the ellipse model is fitted according to each determined edge point, the fitted ellipse model may include more noise points, so that pupil images carried by the image of the eye can not be accurately positioned.
Disclosure of Invention
The invention provides a method and a device for positioning pupil images in human eye images, which can more accurately position the pupil images carried in the human eye images.
In a first aspect, the present invention provides a method for positioning a pupil image in an image of a person eye, including:
s0: determining a plurality of edge points of a pupil image in a human eye image, and fitting an initial ellipse model according to the edge points;
s1: randomly selecting at least three sampling edge points from the edge points, determining that the at least three sampling edge points respectively correspond to sampling tangent lines on the initial ellipse model, and determining the sampling pupil center according to the sampling edge points and the sampling tangent lines;
s2: for each non-sampling edge point which is not selected as a sampling edge point, determining a calibration pupil center according to the non-sampling edge point, a tangent line of the non-sampling edge point on the initial ellipse model, each sampling edge point and each sampling tangent line;
s3: for each calibration pupil center, when the separation distance between the calibration pupil center and the sampling pupil center is not greater than a set distance, determining a non-sampling edge point corresponding to the calibration pupil center as a trusted edge point;
s4, determining the ratio of the first total amount of each credible edge point to the second total amount of each edge point, detecting whether the ratio is smaller than a set threshold value, and if so, executing S1; otherwise, go to S5;
s5: and fitting a target ellipse model according to each trusted edge point, and marking the position of the pupil image in the human eye image through the target ellipse model.
Preferably, the first and second electrodes are formed of a metal,
determining the sampling pupil center according to each sampling edge point and each sampling tangent line, including:
determining the midpoint between every two adjacent sampling edge points according to the initial ellipse model;
for each midpoint, determining the intersection point of the sampling tangent lines corresponding to the two sampling edge points of the midpoint respectively, and determining the midpoint and the straight line where the intersection point is located;
and calculating a polar distance point which is closest to each straight line in the human eye image according to a least square method, and determining the polar distance point as the center of the sampling pupil.
Preferably, the first and second electrodes are formed of a metal,
the fitting of the target ellipse model according to each trusted edge point includes:
a0: forming a credible set by using each credible edge point;
a1: fitting a transition ellipse model according to each trusted edge point in the trusted set;
a2: calculating an algebraic distance between each trusted edge point in the trusted set and the transition ellipse model;
a3: determining whether each edge point is a suspicious edge point according to an algebraic distance between each trusted edge point and the transition ellipse model, and forming a new trusted set by using each edge point which is not determined as the suspicious edge point;
a4: detecting whether the formed credible set is completely the same as the formed credible set at the previous time, if so, executing A5; otherwise, a1 is executed;
a5: and determining the corresponding transition ellipse model when the belief set is formed at this time as a target ellipse model.
Preferably, the first and second electrodes are formed of a metal,
determining whether each edge point is a suspicious edge point according to an algebraic distance between each trusted edge point and the transition ellipse model, including:
calculating average fitting deviation according to the algebraic distance between each credible edge point and the transition ellipse model and the credible total amount of the credible edge points in the credible set;
determining a deviation threshold according to the mean fit deviation;
for each edge point, detecting an algebraic distance between the edge point and the transition elliptical model;
and when the algebraic distance between the edge point and the transition elliptical model is larger than the deviation threshold, determining the edge point as a suspicious edge point.
Preferably, the first and second electrodes are formed of a metal,
further comprising:
recording the ratio determined each time, and recording the cycle times of each continuously determined ratio which are continuously smaller than the set threshold;
when the cycle times reach a set value, selecting a target ratio with the maximum value from the recorded ratios;
and fitting a target ellipse model according to each credible edge point corresponding to the determined target ratio, and marking the position of the pupil image in the human eye image through the target ellipse model.
In a second aspect, the present invention provides an apparatus for locating a pupil image in an image of a person, comprising:
the preprocessing module is used for determining a plurality of edge points of the pupil image in the human eye image and fitting an initial ellipse model according to the edge points;
the sampling processing module is used for randomly selecting at least three sampling edge points from the edge points, determining that the at least three sampling edge points respectively correspond to sampling tangent lines on the initial elliptical model, and determining the sampling pupil center according to the sampling edge points and the sampling tangent lines;
the calibration processing module is used for determining a calibration pupil center according to the non-sampling edge points, tangent lines corresponding to the non-sampling edge points on the initial elliptical model, the sampling edge points and the sampling tangent lines, aiming at each non-sampling edge point which is not selected as a sampling edge point;
a trusted processing module, configured to determine, for each calibration pupil center, a non-sampling edge point corresponding to the calibration pupil center as a trusted edge point when a separation distance between the calibration pupil center and the sampling pupil center is not greater than a set distance;
the detection processing module is used for determining the ratio of the first total amount of each trusted edge point to the second total amount of each edge point, detecting whether the ratio is smaller than a set threshold value or not, and if so, triggering the sampling processing module; otherwise, triggering the mark processing module;
and the marking processing module is used for fitting a target ellipse model according to each trusted edge point and marking the position of the pupil image in the human eye image through the target ellipse model.
Preferably, the first and second electrodes are formed of a metal,
the sampling processing module is specifically configured to execute:
determining the midpoint between every two adjacent sampling edge points according to the initial ellipse model;
for each midpoint, determining the intersection point of the sampling tangent lines corresponding to the two sampling edge points of the midpoint respectively, and determining the midpoint and the straight line where the intersection point is located;
and calculating a polar distance point which is closest to each straight line in the human eye image according to a least square method, and determining the polar distance point as the center of the sampling pupil.
Preferably, the first and second electrodes are formed of a metal,
the mark processing module comprises: the device comprises a preprocessing unit, a model fitting unit, a calculating unit, an updating processing unit, a detecting unit and a determining unit; wherein the content of the first and second substances,
the preprocessing unit is used for forming a credible set by utilizing the credible edge points and triggering the model fitting unit;
the model fitting unit is configured to fit a transition ellipse model according to each trusted edge point included in the trusted set under the triggering of the preprocessing unit or the detection unit;
the computing unit is configured to compute an algebraic distance between each trusted edge point included in the trusted set and the transition ellipse model;
the updating processing unit is configured to determine whether each edge point is a suspicious edge point according to an algebraic distance between each trusted edge point and the transition ellipse model, and form a new trusted set using each edge point that is not determined as a suspicious edge point;
the detection unit is used for detecting whether the formed credible set is completely the same as the credible set formed in the previous time or not, and if so, the determination unit is triggered; otherwise, triggering the model fitting unit;
and the determining unit is used for determining the corresponding transition ellipse model as the target ellipse model when the credible set is formed at this time.
Preferably, the first and second electrodes are formed of a metal,
further comprising: a record processing module and a selection processing module; wherein the content of the first and second substances,
the recording processing module is used for recording the ratio determined each time and recording the cycle times of each continuously determined ratio which is continuously smaller than the set threshold;
the selection processing module is used for selecting a target ratio with the maximum value from the recorded ratios when the cycle times reach a set value and triggering the marking processing module;
the marking processing module is further configured to fit a target ellipse model according to each of the trusted edge points corresponding to the determination of the target ratio under the trigger of the selection processing module, and mark the position of the pupil image in the human eye image through the target ellipse model.
The invention provides a method and a device for positioning a pupil image in a human eye image, wherein the method comprises the steps of firstly fitting an initial elliptical model according to each edge point after determining a plurality of edge points of the pupil image in the human eye image, then randomly selecting at least three sampling edge points from each edge point, determining each sampling edge point to respectively correspond to a sampling tangent on the initial elliptical model, and further determining a sampling pupil center corresponding to each sampling edge point according to each sampling edge point and each sampling tangent; in the subsequent process, for each non-sampling edge point which is not selected as a sampling edge point in each edge point, a calibration pupil center can be determined according to the tangent line corresponding to the non-sampling edge point and the non-sampling edge point on the initial elliptical model, each sampling edge point and each sampling tangent line, the smaller the interval distance between the calibration pupil center and the sampling pupil center, the higher the probability that the non-sampling edge point corresponding to the calibration pupil center is the real edge point of the pupil image in the human eye image when each selected sampling edge point is the real edge point of the pupil image in the human eye image, otherwise, the lower the probability that the non-sampling edge point corresponding to the calibration pupil center is the real edge point of the pupil image in the human eye image, therefore, for each determined calibration pupil center, when the interval distance between the calibration pupil center and the sampling pupil center is not greater than the set distance, then, the non-sampling edge point corresponding to the calibrated pupil center can be determined as a trusted edge point, and obviously, the non-sampling edge point which is not determined as a trusted edge point is a noise point relative to each sampling edge point; since the number of the noise points in each determined edge point should be far less than the number of the true edge points of the pupil image in the eye image, when a ratio between a first total amount of all the reliable edge points relative to each sampling edge point and a second total amount of each edge point is determined, the larger the ratio is, the higher the probability that each selected sampling edge point is the true edge point of the pupil image in the eye image is, otherwise, the lower the probability that each selected sampling edge point is the true edge point of the pupil image in the eye image is, and correspondingly, when the determined ratio is smaller than a set threshold, that is, the probability that each selected sampling edge point is the true edge point of the pupil image in the eye image is too low, the sampling edge points can be reselected and the similar method described above can be performed to filter the noise points in each edge point until the determined ratio is not smaller than the set threshold, the target ellipse model is fitted according to the remaining trusted edge points after the noise points are removed so as to mark the positions of the pupil images in the human eye images, so that the pupil images in the human eye images are more accurately positioned.
Drawings
In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a flowchart of a method for positioning a pupil image in an image of a human eye according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating an embodiment of determining a sampling pupil center according to a selected sampling point and an initial ellipse model;
fig. 3 is a flowchart of another method for positioning a pupil image in an eye image according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a device for positioning a pupil image in an image for a person eye according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of another apparatus for positioning a pupil image in an image for a person eye according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for positioning a pupil image in an image of a person eye, including:
s0: determining a plurality of edge points of a pupil image in a human eye image, and fitting an initial ellipse model according to the edge points;
s1: randomly selecting at least three sampling edge points from the edge points, determining that the at least three sampling edge points respectively correspond to sampling tangent lines on the initial ellipse model, and determining the sampling pupil center according to the sampling edge points and the sampling tangent lines;
s2: for each non-sampling edge point which is not selected as a sampling edge point, determining a calibration pupil center according to the non-sampling edge point, a tangent line of the non-sampling edge point on the initial ellipse model, each sampling edge point and each sampling tangent line;
s3: for each calibration pupil center, when the separation distance between the calibration pupil center and the sampling pupil center is not greater than a set distance, determining a non-sampling edge point corresponding to the calibration pupil center as a trusted edge point;
s4, determining the ratio of the first total amount of each credible edge point to the second total amount of each edge point, detecting whether the ratio is smaller than a set threshold value, and if so, executing S1; otherwise, go to S5;
s5: and fitting a target ellipse model according to each trusted edge point, and marking the position of the pupil image in the human eye image through the target ellipse model.
As shown in fig. 1, after determining a plurality of edge points of a pupil image in a human eye image, an initial elliptical model is first fitted according to each edge point, then at least three sampling edge points can be randomly selected from each edge point, and sampling tangents of each sampling edge point on the initial elliptical model are determined, so as to determine a sampling pupil center corresponding to each sampling edge point according to each sampling edge point and each sampling tangent.
Further, for each non-sampling edge point which is not selected as a sampling edge point in each edge point, determining a calibration pupil center according to the non-sampling edge point, a tangent line of the non-sampling edge point on the initial elliptical model, each sampling edge point and each sampling tangent line; the smaller the distance between the calibration pupil center and the sampling pupil center is, the higher the probability that the selected sampling edge point is the real edge point of the pupil image in the human eye image is, and the higher the probability that the non-sampling edge point corresponding to the calibration pupil center is the real edge point of the pupil image in the human eye image is, otherwise, the lower the probability that the non-sampling edge point corresponding to the calibration pupil center is the real edge point of the pupil image in the human eye image is.
Therefore, for each determined calibration pupil center, when the separation distance between the calibration pupil center and the sampling pupil center is not greater than the set distance, the non-sampling edge point corresponding to the calibration pupil center may be determined as the trusted edge point. Obviously, the non-sampling edge points which are not determined as the credible edge points are noise points relative to all the sampling edge points; since the number of noise points in each edge point determined should be much smaller than the number of true edge points of the pupil image in the human eye image. And when the ratio between the first total amount of all the credible edge points relative to each sampling edge point and the second total amount of each edge point is determined, the larger the ratio is, the higher the probability that each selected sampling edge point is the real edge point of the pupil image in the human eye image is, and otherwise, the lower the probability that each selected sampling edge point is the real edge point of the pupil image in the human eye image is. When the determined ratio is smaller than the set threshold value, namely the probability that each selected sampling edge point is the real edge point of the pupil image in the human eye image is too low, the sampling edge points can be reselected and the similar method is executed to filter noise points in each edge point, and until the determined ratio is not smaller than the set threshold value, the target ellipse model is fitted according to each remaining credible edge point after the noise points are removed to mark the position of the pupil image in the human eye image, so that the pupil image in the human eye image is more accurately positioned.
In the embodiment shown in fig. 1, when at least three sampling edge points are randomly selected from the determined edge points to continue the subsequent traffic processing, the larger the number of the selected sampling edge points is, the larger the calculation amount is. Therefore, in order to reduce the amount of calculation to more quickly and accurately locate the pupil image carried in the human eye image, when the sampling edge points are randomly selected from the determined respective edge points, the number of the selected sampling edge points may be 3.
It should be noted that the sampling edge point described in the embodiment of the present invention corresponds to a sampling tangent on the initial ellipse model, and the following two cases a and B specifically exist:
and A, the sampling edge point is positioned on the fitted initial elliptical model, and at the moment, the sampling tangent line of the sampling edge point corresponding to the initial elliptical model refers to the tangent line of the sampling edge point on the initial elliptical model.
And B, the sampling edge point is not positioned on the fitted initial elliptical model, at this time, a near-distance point which is closest to the algebraic distance of the sampling edge point on the fitted initial elliptical model needs to be determined, and the sampling tangent line of the sampling edge point corresponding to the initial elliptical model refers to the tangent line of the near-distance point corresponding to the sampling edge point on the initial elliptical model.
In an embodiment of the present invention, the determining a sampling pupil center according to each of the sampling edge points and each of the sampling tangents includes:
determining the midpoint between every two adjacent sampling edge points according to the initial ellipse model;
for each midpoint, determining the intersection point of the sampling tangent lines corresponding to the two sampling edge points of the midpoint respectively, and determining the midpoint and the straight line where the intersection point is located;
and calculating a polar distance point which is closest to each straight line in the human eye image according to a least square method, and determining the polar distance point as the center of the sampling pupil.
When the sampling pupil center is determined according to each sampling edge point and each sampling tangent line in step S1, if each selected sampling edge point is a real edge point of a pupil image carried in a human eye image, the determined sampling pupil center should be very close to or overlapped with the real pupil center of the pupil image, so that noise points in each edge point are removed in the subsequent process to extract a reliable edge point with a high probability of being the real edge point of the pupil image.
Specifically, when the calibration pupil center corresponding to each non-sampling edge point is determined by a similar method based on the selected sampling edge points in the subsequent process, the closer the distance between the calibration pupil center corresponding to the non-sampling edge point and the sampling pupil center is, the higher the probability that the sampling edge point is the true edge point of the pupil image is, and otherwise, the higher the probability that the non-sampling edge point is the noise point rather than the trusted edge point is.
For example, referring to fig. 2, taking the example of extracting three sampling edge points M1, M2, M3 from each edge point, after determining that M1, M2, M3 respectively correspond to sampling tangents on the initial elliptical model, it is able to determine the intersection point P2 of the sampling tangents respectively corresponding to M1 and M2, the intersection point P1 of the sampling tangents respectively corresponding to M1 and M3, and the intersection point P3 of the sampling tangents respectively corresponding to M2 and M3; determining that M1 is adjacent to M2, M2 is adjacent to M3, and M1 is adjacent to M3 according to the initial elliptical model, and further determining a midpoint X between adjacent sampled edge points M1 and M2, a midpoint Y between adjacent sampled edge points M1 and M3, and a midpoint Z between adjacent sampled edge points M2 and M3; correspondingly, for the midpoint X, an intersection point P2 of the sampling tangents respectively corresponding to the two sampling edge points M1 and M2 corresponding to the midpoint X can be determined, and then a straight line L2 where the midpoint X and the intersection point P2 are located can be determined, and a straight line L1 where the midpoint Y and the intersection point P1 are located and a straight line L3 where the midpoint Z and the intersection point P3 are located can be determined by a similar method; in the subsequent process, the polar distance points closest to the straight lines L1, L2 and L in the human eye image can be calculated by a least square method or other algorithms, and the polar distance points are the sampling pupil centers O corresponding to the sampling edge points M1, M2 and M3.
Note that the centers of the pupils in fig. 2 are intersections of L1, L2, and L3, and in an actual service scene, there may be no common intersection among L1, L2, and L3, and in this case, it is necessary to calculate the closest polar distance points to the straight lines L1, L2, and L in the human eye image by a least square method or other algorithm.
In an embodiment of the present invention, the fitting a target ellipse model according to each of the trusted edge points includes:
a0: forming a credible set by using each credible edge point;
a1: fitting a transition ellipse model according to each trusted edge point in the trusted set;
a2: calculating an algebraic distance between each trusted edge point in the trusted set and the transition ellipse model;
a3: determining whether each edge point is a suspicious edge point according to an algebraic distance between each trusted edge point and the transition ellipse model, and forming a new trusted set by using each edge point which is not determined as the suspicious edge point;
a4: detecting whether the formed credible set is completely the same as the formed credible set at the previous time, if so, executing A5; otherwise, a1 is executed;
a5: and determining the corresponding transition ellipse model when the belief set is formed at this time as a target ellipse model.
When the ratio of the first total amount of each trusted edge point to the second total amount of each edge point is greater than the set threshold, the probability that only the randomly selected edge points represent the true edge points of the pupil image in the human eye image is very high, and it cannot be absolutely excluded that no noise point exists in each selected sampling edge point, so that a very small number of noise points may still exist in each determined trusted edge point, and meanwhile, part of the true edge points may be determined as noise points (i.e., the true edge points are not determined as trusted edge points) due to the deformation of the pupil image itself.
In the embodiment, a credible set is formed by utilizing each determined credible edge point, then a transition ellipse model is fitted according to each credible edge point in the credible set, and an algebraic distance between each credible edge point in the credible set and the transition ellipse model is calculated; further, whether each edge point is a noise point (i.e. whether each edge point is a suspicious edge point) can be independently measured according to each algebraic distance, so that the real edge point of the pupil image which is not determined as a credible edge point can be determined as a credible edge point again, and then the noise point which is determined as a credible edge point by mistake can be detected, and therefore a new credible set is formed by using each edge point which is determined as a suspicious edge point (not determined as a noise point) by violation.
The method is circularly executed aiming at the credible set, until the credible sets formed twice are completely the same, the noise points in all the edge points in the obtained credible set are completely removed, all the credible edge points in the new credible set formed this time are used as the real edge points of the pupil images in the human eye images, correspondingly, the corresponding transition ellipse model when the credible set is formed this time is determined as the target ellipse model, and when the pupil images carried in the human eye images are marked through the target ellipse model, the pupil images carried in the human eye images can be more accurately positioned.
Based on the foregoing embodiment, in particular, in a preferred embodiment of the present invention, the determining whether each edge point is a suspicious edge point according to an algebraic distance between each trusted edge point and the transition ellipse model includes:
calculating average fitting deviation according to the algebraic distance between each credible edge point and the transition ellipse model and the credible total amount of the credible edge points in the credible set;
determining a deviation threshold according to the mean fit deviation;
for each edge point, detecting an algebraic distance between the edge point and the transition elliptical model;
and when the algebraic distance between the edge point and the transition elliptical model is larger than the deviation threshold, determining the edge point as a suspicious edge point.
In this embodiment, each time a credible set is formed, an average fitting deviation is calculated according to an algebraic distance between each credible edge point in the credible set and the transition ellipse model and a credible total amount of the credible edge points included in the credible set, and the average fitting deviation may be audited with the transition ellipse model formed this time to independently measure whether each edge point is a noise point relative to the transition ellipse model formed this time.
Specifically, after determining the deviation threshold according to the average fitting deviation, after calculating an algebraic distance between each edge point and the transition elliptical model formed this time, when the algebraic distance between one edge point and the transition elliptical model is greater than the deviation threshold, it indicates that the edge point is a noise point with respect to the transition elliptical model formed this time, and may be determined as a suspicious edge point, otherwise, it may be determined as a trusted edge point (i.e., it is determined as a true edge point of the pupil image in the human eye image).
In this embodiment, the average fitting deviation specifically refers to an average value of algebraic distances between each trusted edge point in the currently-formed trusted set and the currently-formed transition ellipse model.
In this embodiment, each time the variation threshold is formed, the variation threshold formed this time is usually 1 to 2 times of the average fitting deviation calculated this time, and the specific multiple may be adjusted by aggregating actual service scenarios.
In order to prevent that a plurality of sampling edge points with higher probability of being the true edge points of the pupil image in the image of the human eye cannot be quickly selected when the determined noise points in the edge points are too many, thereby causing that the pupil image carried in the image of the human eye cannot be quickly positioned, in a preferred embodiment of the present invention, the method further includes:
recording the ratio determined each time, and recording the cycle times of each continuously determined ratio which are continuously smaller than the set threshold;
when the cycle times reach a set value, selecting a target ratio with the maximum value from the recorded ratios;
and fitting a target ellipse model according to each credible edge point corresponding to the determined target ratio, and marking the position of the pupil image in the human eye image through the target ellipse model.
In the embodiment, by recording the determined ratios and recording the cycle times of continuously determined ratios which are continuously smaller than the set threshold, when the times of continuously selecting a plurality of sampling edge points with higher probability of being the real edge points of the pupil images in the human eye images reach the set value, namely the cycle times of continuously determined ratios which are continuously smaller than the set threshold reach the set value, because the ratio determined each time directly reflects the probability that each sampling edge point correspondingly selected when the ratio is determined this time is the real edge point of the pupil images in the human eye images, the target ratio with the maximum value can be selected from the recorded ratios, the target elliptical model is fitted according to each credible edge point corresponding to the determined target ratio, and then the position of the pupil images in the human eye images is marked through the target elliptical model, the method avoids the problem that pupil images carried in the human eye images cannot be quickly positioned due to the fact that a plurality of suitable sampling edge points cannot be selected within a long time.
To more clearly illustrate the technical solution of the present invention, the following specific reference fig. 2 is further used to describe the method for positioning a pupil image in a human eye image according to the present invention, and when the method for positioning a pupil image in a human eye image according to the present invention is implemented, as shown in fig. 3, the following steps may be specifically included.
It should be noted that the number of edge points of the opening work image determined in the person image may be relatively large, and should generally be not less than 20.
It should be understood by those skilled in the art that the specific method for fitting the elliptical model according to the known plurality of edge points on the image can be selected by those skilled in the art by aggregating the actual service scene, and generally, the elliptical model can be fitted by an algebraic distance method on the premise that the known plurality of edge points.
Referring to fig. 2, the embodiment of the invention only takes the determination of the midpoint X between the adjacent sampling edge points M1 and M2, the midpoint Y between the adjacent sampling edge points M1 and M3, and the midpoint Z between the adjacent sampling edge points M2 and M3 as an example.
And 304, aiming at each midpoint, determining the intersection points of the sampling tangent lines respectively corresponding to the two sampling edge points corresponding to the midpoint, and determining the midpoint and the straight line where the intersection points are located.
Referring to fig. 2, for the midpoint X, an intersection point P2 of the sampling tangents respectively corresponding to the two sampling edge points M1 and M2 corresponding to the midpoint X may be determined, and then a straight line L2 where the midpoint X and the intersection point P2 are located may be determined, and a straight line L1 where the midpoint Y and the intersection point P1 are located and a straight line L3 where the midpoint Z and the intersection point P3 are located may be determined by a similar method.
And 305, calculating a polar distance point which is closest to each straight line in the human eye image according to a least square method, and determining the polar distance point as the center of the sampling pupil.
Referring to fig. 2, a polar distance point O closest to the straight lines L1, L2, and L in the human eye image can be calculated by a least square method or other algorithms, where the polar distance point is a sampling pupil center corresponding to the sampling edge points M1, M2, and M3.
And step 306, determining the calibrated pupil center according to the non-sampling edge points, the tangent lines of the non-sampling edge points on the initial elliptical model, the sampling edge points and the sampling tangent lines, aiming at each non-sampling edge point which is not selected as a sampling edge point.
Step 306 may be implemented by a method similar to steps 303 to 305.
Obviously, for each sampling edge point selected at this time, when the separation distance between the calibration pupil center and the sampling pupil center is greater than the set distance, the non-sampling edge point corresponding to the calibration pupil center is a noise point with respect to each selected sampling edge point.
And 310, selecting a target ratio with the largest value from the recorded ratios, fitting a target elliptical model according to each credible edge point corresponding to the determined target ratio, and marking the position of the pupil image in the human eye image through the target elliptical model.
It should be noted that, in step 310, the target ellipse model is fitted according to each trusted edge point corresponding to the target ratio, and the position of the pupil image in the human eye image is marked by the target ellipse model, which may be specifically implemented by executing the same or similar method in steps 311 to 317.
And step 312, fitting a transition ellipse model according to each trusted edge point included in the trusted set.
And 313, calculating the algebraic distance between each trusted edge point in the trusted set and the transition ellipse model.
And step 314, calculating an average fitting deviation according to the algebraic distance between each credible edge point and the transition ellipse model and the credible total amount of the credible edge points in the credible set, and determining a deviation threshold according to the average fitting deviation.
Step 316, detecting whether the formed credible set is completely the same as the formed credible set at the previous time, if yes, executing 317; otherwise, 313 is performed.
It should be noted that, when the steps after step 313 are executed in a loop after step 316, the same or similar method as that of the steps after step 313 is executed for the new trust set formed in step 316.
And 317, determining the corresponding transition ellipse model when the belief set is formed as a target ellipse model, and marking the position of the pupil image in the human eye image through the target ellipse model.
Based on the same concept as the method embodiment of the present invention, an embodiment of the present invention further provides an apparatus for positioning a pupil image in a human eye image, as shown in fig. 4, the apparatus includes:
the preprocessing module 401 is configured to determine a plurality of edge points of a pupil image in a human eye image, and fit an initial ellipse model according to each edge point;
a sampling processing module 402, configured to randomly select at least three sampling edge points from the edge points, determine that the at least three sampling edge points respectively correspond to sampling tangents on the initial elliptical model, and determine a sampling pupil center according to each sampling edge point and each sampling tangent;
a calibration processing module 403, configured to determine, for each non-sampling edge point that is not selected as a sampling edge point, a calibration pupil center according to the non-sampling edge point, a tangent line corresponding to the non-sampling edge point on the initial elliptical model, each sampling edge point, and each sampling tangent line;
a trusted processing module 404, configured to determine, for each calibrated pupil center, a non-sampling edge point corresponding to the calibrated pupil center as a trusted edge point when an interval distance between the calibrated pupil center and the sampling pupil center is not greater than a set distance;
a detection processing module 405, configured to determine a ratio between a first total amount of each trusted edge point and a second total amount of each edge point, and detect whether the ratio is smaller than a set threshold, if so, trigger the sampling processing module; otherwise, triggering the mark processing module;
the marking processing module 406 is configured to fit a target ellipse model according to each trusted edge point, and mark the position of the pupil image in the human eye image through the target ellipse model.
In a preferred embodiment of the present invention, the sampling processing module 402 is specifically configured to execute:
determining the midpoint between every two adjacent sampling edge points according to the initial ellipse model;
for each midpoint, determining the intersection point of the sampling tangent lines corresponding to the two sampling edge points of the midpoint respectively, and determining the midpoint and the straight line where the intersection point is located;
and calculating a polar distance point which is closest to each straight line in the human eye image according to a least square method, and determining the polar distance point as the center of the sampling pupil.
In a preferred embodiment of the present invention, the mark processing module 406 includes: the device comprises a preprocessing unit, a model fitting unit, a calculating unit, an updating processing unit, a detecting unit and a determining unit; wherein the content of the first and second substances,
the preprocessing unit is used for forming a credible set by utilizing the credible edge points and triggering the model fitting unit;
the model fitting unit is configured to fit a transition ellipse model according to each trusted edge point included in the trusted set under the triggering of the preprocessing unit or the detection unit;
the computing unit is configured to compute an algebraic distance between each trusted edge point included in the trusted set and the transition ellipse model;
the updating processing unit is configured to determine whether each edge point is a suspicious edge point according to an algebraic distance between each trusted edge point and the transition ellipse model, and form a new trusted set using each edge point that is not determined as a suspicious edge point;
the detection unit is used for detecting whether the formed credible set is completely the same as the credible set formed in the previous time or not, and if so, the determination unit is triggered; otherwise, triggering the model fitting unit;
and the determining unit is used for determining the corresponding transition ellipse model as the target ellipse model when the credible set is formed at this time.
Referring to fig. 5, in a preferred embodiment of the present invention, the method further includes: a record processing module 501 and a selection processing module 502; wherein the content of the first and second substances,
the recording processing module 501 is configured to record the ratio determined each time, and record the number of cycles that each continuously determined ratio is continuously smaller than the set threshold;
the selection processing module 502 is configured to select a target ratio with a largest value from the recorded ratios when the cycle number reaches a set value, and trigger the mark processing module 406;
then, the marking processing module 406 is further configured to, under the triggering of the selection processing module 502, fit a target ellipse model according to each of the trusted edge points corresponding to the determination of the target ratio, and mark the position of the pupil image in the human eye image through the target ellipse model.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry standard architecture) bus, a PCI (peripheral component interconnect) bus, an EISA (Extended Industry standard architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 6, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
In a possible implementation manner, the processor reads a corresponding computer program from the non-volatile memory into the memory and then runs the computer program, and may also obtain the corresponding computer program from other devices to form the apparatus for positioning the pupil image in the human eye image on a logical level. And the processor executes the program stored in the memory so as to realize the method for positioning the pupil image in the human eye image provided by any embodiment of the invention through the executed program.
The method performed by the apparatus for locating a pupil image in an image for a person according to the embodiment of the present invention shown in fig. 6 may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed by the embodiment of the invention can be directly embodied as the execution of a hardware decoding processor, or the steps can be executed by the combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and assembles hardware thereof to complete the steps of the method.
Embodiments of the present invention also provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by an electronic device including a plurality of application programs, enable the electronic device to perform the apparatus and method for locating a pupil image in an image for a person eye provided in any embodiment of the present invention, and in particular to perform the method shown in fig. 1/3.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units or modules by function, respectively. Of course, the functionality of the units or modules may be implemented in the same one or more software and/or hardware when implementing the invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and sets of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (9)
1. A method for locating a pupil image in a human eye image, comprising:
s0: determining a plurality of edge points of a pupil image in a human eye image, and fitting an initial ellipse model according to the edge points;
s1: randomly selecting at least three sampling edge points from the edge points, determining that the at least three sampling edge points respectively correspond to sampling tangent lines on the initial ellipse model, and determining the sampling pupil center according to the sampling edge points and the sampling tangent lines;
s2: for each non-sampling edge point which is not selected as a sampling edge point, determining a calibration pupil center according to the non-sampling edge point, a tangent line of the non-sampling edge point on the initial ellipse model, each sampling edge point and each sampling tangent line;
s3: for each calibration pupil center, when the separation distance between the calibration pupil center and the sampling pupil center is not greater than a set distance, determining a non-sampling edge point corresponding to the calibration pupil center as a trusted edge point;
s4, determining the ratio of the first total amount of each credible edge point to the second total amount of each edge point, detecting whether the ratio is smaller than a set threshold value, and if so, executing S1; otherwise, go to S5;
s5: and fitting a target ellipse model according to each trusted edge point, and marking the position of the pupil image in the human eye image through the target ellipse model.
2. The method of claim 1, wherein determining a sampled pupil center from each of the sampled edge points and each of the sampled tangents comprises:
determining the midpoint between every two adjacent sampling edge points according to the initial ellipse model;
for each midpoint, determining the intersection point of the sampling tangent lines corresponding to the two sampling edge points of the midpoint respectively, and determining the midpoint and the straight line where the intersection point is located;
and calculating a polar distance point which is closest to each straight line in the human eye image according to a least square method, and determining the polar distance point as the center of the sampling pupil.
3. The method of claim 1, wherein fitting a target ellipse model based on each of the trusted edge points comprises:
a0: forming a credible set by using each credible edge point;
a1: fitting a transition ellipse model according to each trusted edge point in the trusted set;
a2: calculating an algebraic distance between each trusted edge point in the trusted set and the transition ellipse model;
a3: determining whether each edge point is a suspicious edge point according to an algebraic distance between each trusted edge point and the transition ellipse model, and forming a new trusted set by using each edge point which is not determined as the suspicious edge point;
a4: detecting whether the formed credible set is completely the same as the formed credible set at the previous time, if so, executing A5; otherwise, a1 is executed;
a5: and determining the corresponding transition ellipse model when the belief set is formed at this time as a target ellipse model.
4. The method of claim 3, wherein determining whether each of the edge points is a suspicious edge point according to an algebraic distance between each of the edge points and the transition ellipse model comprises:
calculating average fitting deviation according to the algebraic distance between each credible edge point and the transition ellipse model and the credible total amount of the credible edge points in the credible set;
determining a deviation threshold according to the mean fit deviation;
for each edge point, detecting an algebraic distance between the edge point and the transition elliptical model;
and when the algebraic distance between the edge point and the transition elliptical model is larger than the deviation threshold, determining the edge point as a suspicious edge point.
5. The method of any of claims 1 to 4, further comprising:
recording the ratio determined each time, and recording the cycle times of each continuously determined ratio which are continuously smaller than the set threshold;
when the cycle times reach a set value, selecting a target ratio with the maximum value from the recorded ratios;
and fitting a target ellipse model according to each credible edge point corresponding to the determined target ratio, and marking the position of the pupil image in the human eye image through the target ellipse model.
6. An apparatus for locating a pupil image in an image of a person, comprising:
the preprocessing module is used for determining a plurality of edge points of the pupil image in the human eye image and fitting an initial ellipse model according to the edge points;
the sampling processing module is used for randomly selecting at least three sampling edge points from the edge points, determining that the at least three sampling edge points respectively correspond to sampling tangent lines on the initial elliptical model, and determining the sampling pupil center according to the sampling edge points and the sampling tangent lines;
the calibration processing module is used for determining a calibration pupil center according to the non-sampling edge points, tangent lines corresponding to the non-sampling edge points on the initial elliptical model, the sampling edge points and the sampling tangent lines, aiming at each non-sampling edge point which is not selected as a sampling edge point;
a trusted processing module, configured to determine, for each calibration pupil center, a non-sampling edge point corresponding to the calibration pupil center as a trusted edge point when a separation distance between the calibration pupil center and the sampling pupil center is not greater than a set distance;
the detection processing module is used for determining the ratio of the first total amount of each trusted edge point to the second total amount of each edge point, detecting whether the ratio is smaller than a set threshold value or not, and if so, triggering the sampling processing module; otherwise, triggering the mark processing module;
and the marking processing module is used for fitting a target ellipse model according to each trusted edge point and marking the position of the pupil image in the human eye image through the target ellipse model.
7. The apparatus of claim 6, wherein the sampling processing module is specifically configured to perform:
determining the midpoint between every two adjacent sampling edge points according to the initial ellipse model;
for each midpoint, determining the intersection point of the sampling tangent lines corresponding to the two sampling edge points of the midpoint respectively, and determining the midpoint and the straight line where the intersection point is located;
and calculating a polar distance point which is closest to each straight line in the human eye image according to a least square method, and determining the polar distance point as the center of the sampling pupil.
8. The apparatus of claim 6, wherein the tag processing module comprises: the device comprises a preprocessing unit, a model fitting unit, a calculating unit, an updating processing unit, a detecting unit and a determining unit; wherein the content of the first and second substances,
the preprocessing unit is used for forming a credible set by utilizing the credible edge points and triggering the model fitting unit;
the model fitting unit is used for fitting a transition ellipse model according to each trusted edge point in the trusted set under the triggering of the preprocessing unit or the detection unit;
the computing unit is configured to compute an algebraic distance between each trusted edge point included in the trusted set and the transition ellipse model;
the updating processing unit is configured to determine whether each edge point is a suspicious edge point according to an algebraic distance between each trusted edge point and the transition ellipse model, and form a new trusted set using each edge point that is not determined as a suspicious edge point;
the detection unit is used for detecting whether the formed credible set is completely the same as the credible set formed in the previous time or not, and if so, the determination unit is triggered; otherwise, triggering the model fitting unit;
and the determining unit is used for determining the corresponding transition ellipse model as the target ellipse model when the credible set is formed at this time.
9. The apparatus according to any one of claims 6 to 8,
further comprising: a record processing module and a selection processing module; wherein the content of the first and second substances,
the recording processing module is used for recording the ratio determined each time and recording the cycle times of each continuously determined ratio which is continuously smaller than the set threshold;
the selection processing module is used for selecting a target ratio with the maximum value from the recorded ratios when the cycle times reach a set value and triggering the marking processing module;
the marking processing module is further configured to fit a target ellipse model according to each of the trusted edge points corresponding to the determination of the target ratio under the trigger of the selection processing module, and mark the position of the pupil image in the human eye image through the target ellipse model.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679180A (en) * | 2012-09-19 | 2014-03-26 | 武汉元宝创意科技有限公司 | Sight tracking method based on single light source of single camera |
| CN104182720A (en) * | 2013-05-22 | 2014-12-03 | 北京三星通信技术研究有限公司 | Pupil detection method and device |
| TW201605407A (en) * | 2014-08-14 | 2016-02-16 | 由田新技股份有限公司 | Method, apparatus and computer program product for positioning pupil |
| CN108280403A (en) * | 2017-12-27 | 2018-07-13 | 北京七鑫易维信息技术有限公司 | The method and apparatus for determining pupil position |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103955675A (en) * | 2014-04-30 | 2014-07-30 | 上海华博信息服务有限公司 | Facial feature extraction method |
| CN106919933A (en) * | 2017-03-13 | 2017-07-04 | 重庆贝奥新视野医疗设备有限公司 | The method and device of Pupil diameter |
| CN108053444B (en) * | 2018-01-02 | 2021-03-12 | 京东方科技集团股份有限公司 | Pupil positioning method and device, equipment and storage medium |
-
2018
- 2018-08-16 CN CN201810959954.3A patent/CN109145821B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103679180A (en) * | 2012-09-19 | 2014-03-26 | 武汉元宝创意科技有限公司 | Sight tracking method based on single light source of single camera |
| CN104182720A (en) * | 2013-05-22 | 2014-12-03 | 北京三星通信技术研究有限公司 | Pupil detection method and device |
| TW201605407A (en) * | 2014-08-14 | 2016-02-16 | 由田新技股份有限公司 | Method, apparatus and computer program product for positioning pupil |
| CN108280403A (en) * | 2017-12-27 | 2018-07-13 | 北京七鑫易维信息技术有限公司 | The method and apparatus for determining pupil position |
Non-Patent Citations (3)
| Title |
|---|
| 基于FPGA的实时瞳孔定位技术研究;吴孟杰;《中国优秀硕士学位论文全文数据库 》;20180515(第5期);第E080-1页正文第42、46-48页 * |
| 基于视频图像的瞳孔定位算法研究;谢波;《中国优秀硕士学位论文全文数据库 信息科技辑》;20170415(第4期);第I138-512页正文第37-39页 * |
| 视线跟踪过程中变形瞳孔的定位;张文聪等;《电子与信息学报》;20100228;第32卷(第2期);第417-419页 * |
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