CN104715478A - A method and system for detecting exposure area in image picture - Google Patents
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Abstract
The invention relates to the field of images, in particular to a method and system for detecting an exposure area in an image picture. Line standard deviation and row standard deviation of the image picture are obtained through calculating respectively, a line gradient corresponding to the line standard deviation and a row gradient corresponding to the row standard deviation are calculated, a weighted coefficient array of a line standard deviation gradient is calculated according to the line gradient, a weighted coefficient array of a row standard deviation gradient is calculated according to the row gradient, the line gradient multiplies the weighted coefficient array of the line standard deviation gradient to obtain a line standard deviation gradient weighting array, the row gradient multiplies the weighted coefficient array of the row standard deviation gradient to obtain a row standard deviation gradient weighting array, and an upper boundary, a lower boundary, a left boundary and a right boundary are determined through the line standard deviation gradient weighting array and the row standard deviation gradient weighting array at last, so that the final exposure area of the image picture is determined. According to the method and system for detecting the exposure area in the image picture, the exposure area in the image picture can be detected rapidly.
Description
Technical field
The present invention relates to image field, particularly relate to a kind of method and system detecting exposure area in image picture.
Background technology
In DR image collection process, improper, the stability of hardware of DR equipment, the difference etc. of different film making patient density that the bat breath dosage selected due to the experience level of doctor is chosen, the original image collected is caused to have luminance difference clearly, if now post processing of image process selects identical parameters, the image of aftertreatment can not guarantee the effect of brightness and contrast, details and luminance proportion can not well show, and have a great impact the diagnosis of doctor.So the available gray-scale information of adaptive analysis original image is very important, and analyze the available gray-scale information of original image, the detection in tissue (skin, muscle and bone portion) region is necessary, and tissue region detection is that the basis accurately detected in exposure area is carried out.On the other hand, doctor also after getting image, can carry out cutting to image, and general clipping region all can elect exposure area as.
Therefore, need a kind of method that can detect exposure area in image picture fast, and then increase work efficiency.
Summary of the invention
Technical matters to be solved by this invention is: provide a kind of method and system detecting exposure area in image picture, can detect the exposure area in image picture fast.
In order to solve the problems of the technologies described above, the technical solution used in the present invention is:
Detect a method for exposure area in image picture, comprise the following steps:
S100, obtain image picture to be detected;
S200, step S100 gained image picture is carried out image scaling process;
S300, marginal analysis is carried out to step S200 gained image picture;
Described marginal analysis comprises step S301 to S305, specific as follows:
S301, according to step S200 gained image picture calculate column criterion difference and row standard deviation;
S302, calculate row gradient according to step S301 gained column criterion difference, calculate row gradient according to step S301 gained row standard deviation;
S303, to obtain the weighting coefficient array of column criterion difference gradient according to the capable gradient calculation of step S302 gained; Must the weighting coefficient array of row standard deviation gradient according to step S302 gained row gradient calculation;
S304, calculate column criterion difference gradient weighting array according to the weighting coefficient array of the capable gradient of step S302 gained and step S303 gained column criterion difference gradient; Weighting coefficient array according to step S302 gained row gradient and step S303 gained row standard deviation gradient calculates row standard deviation gradient weighting array;
S305, according to step S304 gained column criterion difference gradient weighting array and row standard deviation gradient weighting array determination initial boundary;
S400, border correction is carried out to step S305 gained image picture;
S500, using the exposure area of revised for step S400 gained border as image picture, obtain the exposure area scope of described image picture.
Another technical scheme that the present invention adopts is:
Detect a system for exposure area in image picture, comprising: acquisition module, convergent-divergent processing module, marginal analysis module, border correcting module and exposure area module;
Described acquisition module, for obtaining image picture to be detected;
Described convergent-divergent processing module, for carrying out image scaling process by acquisition module gained image picture;
Described marginal analysis module comprises the first computing unit, the second computing unit, the 3rd computing unit, the 4th computing unit and determining unit;
Described first computing unit, for calculating column criterion difference and row standard deviation according to gained image picture;
Described second computing unit, for calculating row gradient according to the first computing unit gained column criterion difference, calculates row gradient according to step the first computing unit gained row standard deviation;
Described 3rd computing unit, for obtaining the weighting coefficient array of column criterion difference gradient according to the capable gradient calculation of the second computing unit gained; Must the weighting coefficient array of row standard deviation gradient according to the second computing unit gained row gradient calculation;
Described 4th computing unit, for calculating the weighting of standard deviation gradient to the 3rd computing unit gained image picture; Weighting coefficient array according to the capable gradient of the second computing unit gained and the 3rd computing unit gained column criterion difference gradient calculates column criterion difference gradient weighting array; Weighting coefficient array according to the second computing unit gained row gradient and the 3rd computing unit gained row standard deviation gradient calculates row standard deviation gradient weighting array;
Described determining unit, for according to the 4th computing unit gained column criterion difference gradient weighting array and row standard deviation gradient weighting array determination initial boundary;
Described border correcting module, for carrying out border correction to determining unit gained image picture;
Described exposure area module, for using the exposure area of the revised border of correcting module gained, border as image picture, obtains the exposure area scope of described image picture.
Beneficial effect of the present invention is: by calculating column criterion difference and the row standard deviation of the image picture of acquisition respectively, calculate row gradient corresponding to described column criterion difference and row gradient corresponding to described row standard deviation again, the weighting coefficient array of column criterion difference gradient is obtained again according to row gradient calculation, row gradient calculation must the weighting coefficient array of row standard deviation gradient, weighting coefficient array row gradient being multiplied by column criterion difference gradient calculates column criterion difference gradient weighting array and the weighting coefficient array that row gradient is multiplied by row standard deviation gradient calculates row standard deviation gradient weighting array, coboundary is determined finally by column criterion difference gradient weighting array and row standard deviation gradient weighting array, lower boundary, left margin and right margin, thus determine the final exposure area of described image picture, by method and system provided by the invention, the exposure area in image picture can be detected fast.
Accompanying drawing explanation
Fig. 1 is the flow chart of steps of the method for exposure area in the detection image picture of the specific embodiment of the invention;
Fig. 2 is the structural representation of the system of exposure area in the detection image picture of the specific embodiment of the invention;
Fig. 3 is weighting coefficient array schematic diagram in specific embodiment of the invention embodiment 1;
Label declaration:
10, acquisition module; 20, convergent-divergent processing module; 30, marginal analysis module; 40, border correcting module; 50, exposure area module; 301, the first computing unit; 302, the second computing unit; 303, the 3rd computing unit; 304, the 4th computing unit; 305, determining unit.
Embodiment
By describing technology contents of the present invention in detail, realized object and effect, accompanying drawing is coordinated to be explained below in conjunction with embodiment.
The design of most critical of the present invention is: by determining 4 borders up and down of the effective coverage of image picture, thus determine the exposure area of image.
Please refer to Fig. 1, is the flow chart of steps of the method for exposure area in the detection image picture of the specific embodiment of the invention, specific as follows:
Detect a method for exposure area in image picture, comprise the following steps:
S100, obtain image picture to be detected;
S200, step S100 gained image picture is carried out image scaling process;
S300, marginal analysis is carried out to step S200 gained image picture;
Described marginal analysis comprises step S301 to S305, specific as follows:
S301, according to step S200 gained image picture calculate column criterion difference and row standard deviation;
S302, calculate row gradient according to step S301 gained column criterion difference, calculate row gradient according to step S301 gained row standard deviation;
S303, to obtain the weighting coefficient array of column criterion difference gradient according to the capable gradient calculation of step S302 gained; Must the weighting coefficient array of row standard deviation gradient according to step S302 gained row gradient calculation;
S304, calculate column criterion difference gradient weighting array according to the weighting coefficient array of the capable gradient of step S302 gained and step S303 gained column criterion difference gradient; Weighting coefficient array according to step S302 gained row gradient and step S303 gained row standard deviation gradient calculates row standard deviation gradient weighting array;
S305, according to step S304 gained column criterion difference gradient weighting array and row standard deviation gradient weighting array determination initial boundary;
S400, border correction is carried out to step S305 gained image picture;
S500, using the exposure area of revised for step S400 gained border as image picture, obtain the exposure area scope of described image picture.
From foregoing description, beneficial effect of the present invention is: by calculating column criterion difference and the row standard deviation of the image picture of acquisition respectively, calculate row gradient corresponding to described column criterion difference and row gradient corresponding to described row standard deviation again, the weighting coefficient array of column criterion difference gradient is obtained again according to row gradient calculation, row gradient calculation must the weighting coefficient array of row standard deviation gradient, weighting coefficient array row gradient being multiplied by column criterion difference gradient calculates column criterion difference gradient weighting array and the weighting coefficient array that row gradient is multiplied by row standard deviation gradient calculates row standard deviation gradient weighting array, coboundary is determined finally by column criterion difference gradient weighting array and row standard deviation gradient weighting array, lower boundary, left margin and right margin, thus determine the final exposure area of described image picture, by method provided by the invention, the exposure area in image picture can be detected fast.
Further, described image scaling process is specially: the gray-scale value extracting gained image picture according to interlacing every row, obtains the image picture after image scaling process.
Further, described step S301 specifically comprises: according to the gray-scale value of gained image picture, calculates the standard deviation that image picture level is often gone, and calculates the standard deviation that image picture vertically often arranges.
Further, described step S302 specifically comprises: the standard deviation of often going according to step S301 gained image picture level, calculated level standard deviation gradient array, according to the standard deviation that step S301 gained image picture vertically often arranges, calculates vertical standard deviation gradient array.
Further, described step S304 is specially: the weighting coefficient each row gradient being multiplied by the column criterion difference gradient of correspondence position obtains column criterion difference gradient weighting array; The weighting coefficient each row gradient being multiplied by the row standard deviation gradient of correspondence position must row standard deviation gradient weighting array.
Further, described step S305 is specially:
By the maximal value in column criterion difference gradient weighting array, determine the left margin of initial boundary; By the maximal value in row standard deviation gradient weighting array, determine the coboundary of initial boundary;
By the minimum value in column criterion difference gradient weighting array, determine the right margin of initial boundary; By the minimum value in row standard deviation gradient weighting array, determine the lower boundary of initial boundary.
Further, described step S400 also comprises: judge S305 gained initial boundary whether in the invalid bounds preset, if not in the invalid bounds preset, then enter step S500; If in the invalid bounds preset, then obtain the second maximal value or the second minimum value as revised ultimate bound.
Further, described step S400 also comprises: judge whether the second maximal value is greater than the first maximal value preset multiple, if so, then using described second maximal value as revised ultimate bound; Judge whether the second minimum value is less than the first minimum value preset multiple, if so, then using described second minimum value as revised ultimate bound; Described preset multiple scope is 0.5 to 0.7; The optimal value of described preset multiple is 0.6.
Seen from the above description, show through test of many times data, when A value is in 0.5 to 0.7 scope, the accuracy of testing result reaches more than 90%, and when the value of A is 0.6, the accuracy of testing result can reach 98%.
Further, described step S500 is specially: obtain coboundary, lower boundary, left margin and right margin according to step S400, and form rectangular area, the scope of described rectangular area is the scope of exposure area.
Referring to Fig. 2, is the structural representation of the system of exposure area in the detection image picture of the specific embodiment of the invention, specific as follows:
Detect a system for exposure area in image picture, comprising: acquisition module 10, convergent-divergent processing module 20, marginal analysis module 30, border correcting module 40 and exposure area module 50;
Described acquisition module 10, for obtaining image picture to be detected;
Described convergent-divergent processing module 20, for carrying out image scaling process by acquisition module 10 gained image picture;
Described marginal analysis module 30 comprises the first computing unit 301, second computing unit 302, the 3rd computing unit 303, the 4th computing unit 304 and determining unit 305;
Described first computing unit 301, for calculating column criterion difference and row standard deviation according to gained image picture;
Described second computing unit 302, for calculating row gradient according to the first computing unit 301 gained column criterion difference, calculates row gradient according to step the first computing unit 301 gained row standard deviation;
Described 3rd computing unit 303, for obtaining the weighting coefficient array of column criterion difference gradient according to the capable gradient calculation of the second computing unit 302 gained; Must the weighting coefficient array of row standard deviation gradient according to the second computing unit 302 gained row gradient calculation;
Described 4th computing unit 304, for calculating the weighting of standard deviation gradient to the 3rd computing unit 303 gained image picture; Weighting coefficient array according to the second capable gradient of computing unit 302 gained and the 3rd computing unit 303 gained column criterion difference gradient calculates column criterion difference gradient weighting array; Weighting coefficient array according to the second computing unit 302 gained row gradient and the 3rd computing unit 303 gained row standard deviation gradient calculates row standard deviation gradient weighting array;
Described determining unit 305, for according to the 4th computing unit 304 gained column criterion difference gradient weighting array and row standard deviation gradient weighting array determination initial boundary;
Described border correcting module 40, for carrying out border correction to determining unit 305 gained image picture;
Described exposure area module 50, for using the exposure area of the revised border of border correcting module 40 gained as image picture, obtains the exposure area scope of described image picture.
From foregoing description, beneficial effect of the present invention is: by calculating column criterion difference and the row standard deviation of the image picture of acquisition respectively, calculate row gradient corresponding to described column criterion difference and row gradient corresponding to described row standard deviation again, the weighting coefficient array of column criterion difference gradient is obtained again according to row gradient calculation, row gradient calculation must the weighting coefficient array of row standard deviation gradient, weighting coefficient array row gradient being multiplied by column criterion difference gradient calculates column criterion difference gradient weighting array and the weighting coefficient array that row gradient is multiplied by row standard deviation gradient calculates row standard deviation gradient weighting array, coboundary is determined finally by column criterion difference gradient weighting array and row standard deviation gradient weighting array, lower boundary, left margin and right margin, thus determine the final exposure area of described image picture, by system provided by the invention, the exposure area in image picture can be detected fast.
Embodiment 1
A kind of method detecting exposure area in image picture provided by the invention, specific as follows:
S100, obtain image picture to be detected;
S200, step S100 gained image picture is carried out image scaling process; Described image scaling process is specially: the gray-scale value extracting gained image picture according to interlacing every row, obtains the image picture after image scaling process;
S300, marginal analysis is carried out to step S200 gained image picture;
Described marginal analysis comprises step S301 to S305, specific as follows:
S301, according to step S200 gained image picture calculate column criterion difference and row standard deviation;
The processing mode of this step is: according to the gray-scale value of gained image picture, calculates the standard deviation that image picture level is often gone, and calculates the standard deviation that image picture vertically often arranges.
S302, calculate row gradient according to step S301 gained column criterion difference, calculate row gradient according to step S301 gained row standard deviation;
The processing mode of this step is: the standard deviation of often going according to step S301 gained image picture level, calculated level standard deviation gradient array, according to the standard deviation that step S301 gained image picture vertically often arranges, calculates vertical standard deviation gradient array.
S303, to obtain the weighting coefficient array of column criterion difference gradient according to the capable gradient calculation of step S302 gained; Must the weighting coefficient array of row standard deviation gradient according to step S302 gained row gradient calculation.
Due to edge, some is invalid edges boundary region to DR detector, and the invalid boundary sizes of level, vertical direction uses invalidBorderH, invalidBorderV respectively.The weighting coefficient array of level, vertical standard deviation gradient represents with hWeight, vWeight respectively.RszHeight is the height of image after convergent-divergent, and rszWidth is the wide of image after convergent-divergent.HA is the slope of horizontal direction weighting oblique line, and hB is corresponding skew, and vA is the slope of vertical direction weighting oblique line, and vB is corresponding skew, and x is row-coordinate or row coordinate.
The wherein following formulae discovery of hA, hB, vA, vB:
As can be seen from Figure 3, weighted value is from border, and just having started one section is 1, and middle one section is by 1 linear decrease to 0, and the one section of weighting coefficient that ends up is all 0.Such as weighting coefficient is: 1,1,0.8,0.6,0.4,0.2,0.0,0,0,0,0.
S304, calculate column criterion difference gradient weighting array according to the weighting coefficient array of the capable gradient of step S302 gained and step S303 gained column criterion difference gradient; Weighting coefficient array according to step S302 gained row gradient and step S303 gained row standard deviation gradient calculates row standard deviation gradient weighting array.
Above-mentioned steps S304 is specially: be multiplied by corresponding weighting coefficient to each Grad.1st Grad is multiplied by the 1st weighting coefficient, and the 2nd Grad is multiplied by the 2nd weighting coefficient, the like, Grad array is the same length with weighting coefficient numerical value length.
The weighting coefficient that each row gradient is multiplied by the column criterion difference gradient of correspondence position obtains column criterion difference gradient weighting array; Each row gradient is multiplied by the weighting coefficient of the row standard deviation gradient of correspondence position must row standard deviation gradient weighting array;
S305, according to step S304 gained column criterion difference gradient weighting array and row standard deviation gradient weighting array determination initial boundary;
The position of the maximal value in column criterion difference gradient weighting array is the coboundary of initial boundary; The position of the maximal value in row standard deviation gradient weighting array is the left margin of initial boundary;
The position of the minimum value in column criterion difference gradient weighting array is the lower boundary of initial boundary; The position of the minimum value in row standard deviation gradient weighting array is the right margin of initial boundary;
S400, border correction is carried out to step S305 gained image picture;
If not in invalid bounds, do not need to revise;
If the first maximal value of left margin or coboundary is in invalid bounds, look for the second maximal value of left margin or coboundary respectively, if the second maximal value is greater than the A of the first maximal value doubly, then use the position of the second maximal value as revised final left margin or coboundary.If the first minimum value of right margin or lower boundary is in invalid bounds, look for the second minimum value of right margin or lower boundary respectively, if the second minimum value is less than the A of the first minimum value doubly, then use the position of the second minimum value as revised final right margin or lower boundary.
A is the value between 0.5 to 0.7; The optimal value of A is 0.6;
Show through test of many times data, when A value is in 0.5 to 0.7 scope, the accuracy of testing result reaches more than 90%, and when the value of A is 0.6, the accuracy of testing result can reach 98%.
S500, using the exposure area of revised for step S400 gained border as image picture, obtain the exposure area scope of described image picture.
By 4 ultimate bounds up and down determined, thus determine the scope of exposure area.
In sum, a kind of method and system detecting exposure area in image picture provided by the invention.By calculating column criterion difference and the row standard deviation of the image picture of acquisition respectively, calculate row gradient corresponding to described column criterion difference and row gradient corresponding to described row standard deviation again, the weighting coefficient array of column criterion difference gradient is obtained again according to row gradient calculation, row gradient calculation must the weighting coefficient array of row standard deviation gradient, weighting coefficient array row gradient being multiplied by column criterion difference gradient calculates column criterion difference gradient weighting array and the weighting coefficient array that row gradient is multiplied by row standard deviation gradient calculates row standard deviation gradient weighting array, coboundary is determined finally by column criterion difference gradient weighting array and row standard deviation gradient weighting array, lower boundary, left margin and right margin, thus determine the final exposure area of described image picture, by method and system provided by the invention, the exposure area in image picture can be detected fast.
The foregoing is only embodiments of the invention; not thereby the scope of the claims of the present invention is limited; every equivalents utilizing instructions of the present invention and accompanying drawing content to do, or be directly or indirectly used in relevant technical field, be all in like manner included in scope of patent protection of the present invention.
Claims (10)
1. detect a method for exposure area in image picture, it is characterized in that, comprise the following steps:
S100, obtain image picture to be detected;
S200, step S100 gained image picture is carried out image scaling process;
S300, marginal analysis is carried out to step S200 gained image picture;
Described marginal analysis comprises step S301 to S305, specific as follows:
S301, according to step S200 gained image picture calculate column criterion difference and row standard deviation;
S302, calculate row gradient according to step S301 gained column criterion difference, calculate row gradient according to step S301 gained row standard deviation;
S303, to obtain the weighting coefficient array of column criterion difference gradient according to the capable gradient calculation of step S302 gained; Must the weighting coefficient array of row standard deviation gradient according to step S302 gained row gradient calculation;
S304, calculate column criterion difference gradient weighting array according to the weighting coefficient array of the capable gradient of step S302 gained and step S303 gained column criterion difference gradient; Weighting coefficient array according to step S302 gained row gradient and step S303 gained row standard deviation gradient calculates row standard deviation gradient weighting array;
S305, according to step S304 gained column criterion difference gradient weighting array and row standard deviation gradient weighting array determination initial boundary;
S400, border correction is carried out to step S305 gained image picture;
S500, using the exposure area of revised for step S400 gained border as image picture, obtain the exposure area scope of described image picture.
2. the method for exposure area in detection image picture according to claim 1, it is characterized in that, described image scaling process is specially: the gray-scale value extracting gained image picture according to interlacing every row, obtains the image picture after image scaling process.
3. the method for exposure area in detection image picture according to claim 1, it is characterized in that, described step S301 specifically comprises: according to the gray-scale value of gained image picture, calculates the standard deviation that image picture level is often gone, and calculates the standard deviation that image picture vertically often arranges.
4. the method for exposure area in detection image picture according to claim 3, it is characterized in that, described step S302 specifically comprises: the standard deviation of often going according to step S301 gained image picture level, calculated level standard deviation gradient array, according to the standard deviation that step S301 gained image picture vertically often arranges, calculate vertical standard deviation gradient array.
5. the method for exposure area in detection image picture according to claim 1, it is characterized in that, described step S304 is specially: the weighting coefficient each row gradient being multiplied by the column criterion difference gradient of correspondence position obtains column criterion difference gradient weighting array; The weighting coefficient each row gradient being multiplied by the row standard deviation gradient of correspondence position must row standard deviation gradient weighting array.
6. the method for exposure area in detection image picture according to claim 1, it is characterized in that, described step S305 is specially:
By the maximal value in column criterion difference gradient weighting array, determine the left margin of initial boundary; By the maximal value in row standard deviation gradient weighting array, determine the coboundary of initial boundary;
By the minimum value in column criterion difference gradient weighting array, determine the right margin of initial boundary; By the minimum value in row standard deviation gradient weighting array, determine the lower boundary of initial boundary.
7. the method for exposure area in detection image picture according to claim 1, it is characterized in that, described step S400 also comprises: judge S305 gained initial boundary whether in the invalid bounds preset, if not in the invalid bounds preset, then enter step S500; If in the invalid bounds preset, then obtain the second maximal value or the second minimum value as revised ultimate bound.
8. the method for exposure area in detection image picture according to claim 7, it is characterized in that, described step S400 also comprises: judge whether the second maximal value is greater than the first maximal value preset multiple, if so, then using described second maximal value as revised ultimate bound; Judge whether the second minimum value is less than the first minimum value preset multiple, if so, then using described second minimum value as revised ultimate bound; Described preset multiple scope is 0.5 to 0.7; The optimal value of described preset multiple is 0.6.
9. the method for exposure area in detection image picture according to claim 1, it is characterized in that, described step S500 is specially: obtain coboundary, lower boundary, left margin and right margin according to step S400, and form rectangular area, the scope of described rectangular area is the scope of exposure area.
10. detect a system for exposure area in image picture, it is characterized in that, comprising: acquisition module, convergent-divergent processing module, marginal analysis module, border correcting module and exposure area module;
Described acquisition module, for obtaining image picture to be detected;
Described convergent-divergent processing module, for carrying out image scaling process by acquisition module gained image picture;
Described marginal analysis module comprises the first computing unit, the second computing unit, the 3rd computing unit, the 4th computing unit and determining unit;
Described first computing unit, for calculating column criterion difference and row standard deviation according to gained image picture;
Described second computing unit, for calculating row gradient according to the first computing unit gained column criterion difference, calculates row gradient according to step the first computing unit gained row standard deviation;
Described 3rd computing unit, for obtaining the weighting coefficient array of column criterion difference gradient according to the capable gradient calculation of the second computing unit gained; Must the weighting coefficient array of row standard deviation gradient according to the second computing unit gained row gradient calculation;
Described 4th computing unit, for calculating the weighting of standard deviation gradient to the 3rd computing unit gained image picture; Weighting coefficient array according to the capable gradient of the second computing unit gained and the 3rd computing unit gained column criterion difference gradient calculates column criterion difference gradient weighting array; Weighting coefficient array according to the second computing unit gained row gradient and the 3rd computing unit gained row standard deviation gradient calculates row standard deviation gradient weighting array;
Described determining unit, for according to the 4th computing unit gained column criterion difference gradient weighting array and row standard deviation gradient weighting array determination initial boundary;
Described border correcting module, for carrying out border correction to determining unit gained image picture;
Described exposure area module, for using the exposure area of the revised border of correcting module gained, border as image picture, obtains the exposure area scope of described image picture.
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