CN1333327C - Optimum correlation matching method and system used to judge locus behavior - Google Patents
Optimum correlation matching method and system used to judge locus behavior Download PDFInfo
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Abstract
The present invention relates to an optimum correlation matching method and an optimum correlation matching system for judging trace behaviors. The present invention is characterized in that a corresponding moving vector in a second image of one area block in a first image is searched in the first image and the second image which are in a video data flow, wherein each image is composed of a plurality of area blocks in rows and columns, and each area block comprises a plurality of pixels composed in rows and columns. The method is characterized in that the position of a searching window area of the second image is divided into a plurality of groups of searching sub-windows, one group is selected from the searching sub-windows and used as an executing searching window, and a center point of one area block is respectively placed at all positions in the executing searching window to execute a matching operation of pixels of one area block and pixels of the second image to reduce the number of times of the matching operation.
Description
Technical field
The invention relates to a kind of in order to judge the best relevant matches method and system of track behavior, refer to especially a kind of on optical mouse in order to judge the best relevant matches method and system of track behavior.
Background technology
Known optical mouse has a video sensing device, and this video sensing device is made up of a plurality of image induction element, and the front and back image that utilizes this video sensing device to be sensed, and carries out the amount of movement detecting.And known applied optics mouse moving amount method for detecting is to search block (search block) with the partial image of first image as one, and carries out relevance (correlation) with this search block and second image at diverse location and calculate.Afterwards, find out absolute little value as displacement according to the relevance size of being calculated.
The example of the image when Fig. 1 is shown in the displacement method for detecting that carries out optical mouse.As shown in Figure 1, figure number 130 is that first image, 110 is that second image, 120 is for searching block, wherein, the size of first image 130 and second image 110 is the pixel (pixel) of 16*16, and the size of searching block 120 is the pixel of 8*8, that is to search block 120 be the image of the middle body 8*8 of first image 130, and the position of its central point is represented with X.After take out searching block 120, this method will be searched block 120 and be moved towards different directions, and respectively with second image 110 compute associations.Because the displacement of optical mouse is relevant with the speed of operator's mobile optical mouse, general first image 130 and second image 110 differ and are no more than 4 pixels, so when searching block 120 and second image 110 compute associations, be to search block 120 central points to be positioned over the position that indicates circle (o) in second image 110 respectively, calculate the relevance of this this search block 120 and second image 110 again.The position that indicates circle (o) in second image 110 has 81 places, so this method can produce 81 relevance numerical value C1～C81.Can find out the point of the most relevant property as displacement according to relevance numerical value C1～C81.
But the required operand of the method is big, for example, need move 81 times searching block 120, and carry out 81 times relevance computing.Simultaneously, also must seek out displacement point according to 81 relevance data.So known amount of movement method for detecting still has the space of improvement.
Summary of the invention
The objective of the invention is is providing a kind of in order to judge the best relevant matches method and system of track behavior, can reduce the number of times of carrying out matching operation.
According to a characteristic of the present invention, be to propose a kind of best relevant matches method in order to the behavior of judgement track, it is by in the 0th image in the video data stream, first image and second image, look for a target block corresponding motion-vector in this second image in this first image, each Zhang Qianshu image all is made up of with row and row mode plurality of blocks, each this block comprises a plurality of pixels of forming with row and row mode, it is characterized in that this method comprises:
One search window deciding step is by determining a search window position and a size in this second image;
One search window is distinguished step, and the lane place in this search window is divided into the sub-search window of plural groups;
One sub-search window is chosen step, by choosing wherein one group in the sub-search window of this plural groups, to carry out search window as one;
One first calculates the relevant matches calculation step, be that center position with this target block is placed on all positions in this execution search window respectively, the pixel of this target block is carried out matching operation with the pixel of this second image respectively, to produce one first group of measurement; And
One first determining moving vectors step by this first group of measurement, is chosen the position of measurement minimum, and is calculated the vector at this target block center, with the motion-vector as this target block.
Wherein also comprise:
One estimates the displacement step, according to the motion-vector of the block in the 0th image that corresponds to this target block, to estimate the motion-vector of this target block, to produce an estimated motion vector;
One second calculates the relevant matches calculation step, is the close position according to this estimated motion vector, and the pixel that second image of pixel and this of this target block is corresponding is carried out matching operation, to produce one second group of measurement; And
One second determining moving vectors step by this first group of measurement and this second group of measurement, is chosen the position of measurement minimum, and is calculated the vector at this position and this target block center, with the motion-vector as this target block.
Wherein, this first determining moving vectors step also comprises the following step:
Choose the close position of the position of measurement minimum according to this, the execution matching operation of the pixel that second image of pixel and this of this target block is corresponding, and choose the position of measurement minimum, with motion-vector as this target block.
Wherein, this center is to be positioned at this first image center position.
Wherein, the center of this search window is identical with this target block center.
Wherein, this sub-search window is mutual mutual exclusion.
Wherein, this first calculating relevant matches calculation step is to carry out a Mean Square Error to calculate.
Wherein, this first calculating relevant matches calculation step is to carry out a mean absolute error to calculate.
Wherein, this first calculating relevant matches calculation step is to carry out an addition absolute error to calculate.
According to a characteristic of the present invention, be propose a kind of in order to judge the best relevant matches system of track behavior, it is by in the 0th image in the video data stream, first image and second image, look for a target block corresponding motion-vector in second image in this first image, each Zhang Qianshu image is made up of with row and row mode plurality of blocks, each this block comprises a plurality of pixels of forming with row and row mode, it is characterized in that this system comprises:
One light source, it is in order to illuminate a sample plane;
One pel array device, it is made up of a plurality of photo-sensitive cell, to capture this sample plane, flows and form this video data;
One simulates to digital switching device, is coupled to this pel array device, to change this video data circulation into digital signal; And
One control device is coupled to this light source and this is simulated to digital switching device, simulates to the sequential of digital switching device conversion and the sequential of this light source igniting in order to control this;
Wherein, this control device is by determining a search window position and a size in this second image, lane place in this search window is divided into the sub-search window of plural groups, and by choosing one group in the sub-search window of this plural groups, to carry out search window as one, again the central point of this target block is placed on all positions in this execution search window respectively, the pixel of this target block is carried out matching operation with the pixel of this second image respectively, to produce one first group of measurement, at last by this first group of measurement, choose the position of measurement minimum, and calculate the vector at this target block center, with motion-vector as this target block.
Wherein, this control device is also according to the motion-vector that corresponds to the block in the 0th image of this target block, to estimate the motion-vector of this target block, to produce an estimated motion vector, and according to the close position of this estimated motion vector, the pixel that the pixel of this target block is corresponding with this second image is carried out matching operation, to produce one second group of measurement, again by this first group of measurement and this second group of measurement, choose the position of measurement minimum, and calculate the vector at this position and this target block center, with motion-vector as this target block.
Wherein, this center is to be positioned at this first image center position.
Wherein, the center of this search window is identical with this target block center.
Wherein, this sub-search window is mutual mutual exclusion.
Wherein, this matching operation is to carry out a Mean Square Error to calculate.
Wherein, this matching operation is to carry out a mean absolute error to calculate.
Wherein, this matching operation is to carry out an addition absolute error to calculate.
Wherein, this light source is a light emitting diode.
Description of drawings
For further specifying technology contents of the present invention, below in conjunction with embodiment and accompanying drawing is described in detail as after, wherein:
Fig. 1 is the synoptic diagram of known calculations relevance (correlation).
Fig. 2 is the calcspar in order to the best relevant matches system of judging the track behavior of the present invention.
Fig. 3 is the process flow diagram in order to the best relevant matches method of judging the track behavior of the present invention.
Fig. 4 is the synoptic diagram of search window decision of the present invention.
Fig. 5 is a synoptic diagram of looking for motion-vector of the present invention.
Fig. 6 is the synoptic diagram of motion-vector association of the present invention.
Embodiment
Fig. 2 is the calcspar in order to the best relevant matches system of judging the track behavior of the present invention, it is by the 0th image in the video data stream, in first image and second image, look for a block corresponding motion-vector (i is an integer) in second image in first image, each is opened image and is made up of with row and row mode plurality of blocks, each block comprises a plurality of pixels of forming with row and row mode, and this system comprises a light source 210, one pel array device 220 (pixelarray), one simulates to a digital switching device 230 and a control device 240.
This light source 210 is in order to illuminate a sample plane, and it is preferably a light emitting diode.This pel array device 220 is made up of a plurality of photo-sensitive cell, capturing this sample plane, and forms this video data stream.This is simulated to digital switching device 230 (Analogue to digitalconverter, ADC) and is coupled to this pel array device 220, to change this video data circulation into digital signal.This control device 240 is coupled to this light source 210 and this is simulated to digital switching device 230, simulates the sequential of lighting to sequential and this light source 210 of digital switching device 230 conversions in order to control this.
And please refer to the process flow diagram in order to the best relevant matches method of judging the track behavior of the present invention shown in Figure 3, it is by in first image 130 in the video data shown in Figure 4 stream and second image 110, look for a block 120 corresponding motion-vector in second image 110 in first image 130, in present embodiment, this block 120 is that the center and its size that are positioned at this first image 130 are the 8*8 pixel, first image 130 and second image 110 are made up of with row and row mode plurality of blocks, and each block comprises a plurality of pixels of forming with row and row mode.
As shown in Figure 3, at first in step S310, carry out a search window deciding step, it is according to one first rule, with by determining a search window 140 positions and size in second image 110.Because the resolution of computer screen day by day increases, the translational speed of optical mouse also need increase accordingly, so this first rule is to adjust this search window 140 sizes according to the translational speed of optical mouse.In present embodiment, these search window 140 sizes are ± 4 pixels and be positioned at the position of these second image 110 central point, the position in this search window 140 is to represent with circle (o).
In step S320, to carry out a search window and distinguish step, it is that the lane place in this search window 140 is divided into the sub-search window of plural groups.As shown in Figure 5, this search window 140 is divided into the sub-search window of two groups of mutual mutual exclusions (mutually exclusive), and the first sub-search window 141 is to be represented by the solid black circle, and the second sub-search window 142 is to be represented by the black empty circles.
In step S330, to carry out a sub-search window and choose step, it is by choosing one group in the sub-search window of plural groups, to carry out search window as one.For purposes of illustration, choose the first sub-search window 141 herein and carry out search window as one, that is the central point of this block 120 can drop on all positions in the first sub-search window 141 respectively, and corresponding with this second image 110 respectively pixel is carried out matching operation.
In step S340, carry out one first and calculate the relevant matches calculation step, be that central point with this block 120 is placed on all positions in this execution search window (the first sub-search window 141) respectively, and the pixel that the pixel of this block 120 is corresponding with this second image 110 is respectively carried out matching operation, to produce one first group of measurement.This matching operation is to carry out a Mean Square Error (mean square error) to calculate, that is:
Wherein, N is the column number of i block 120, and Cij is the pixel value of this block 120, and Rij is second image 110 corresponding pixel value.This matching operation also can be carried out a mean absolute error (mean absolute error) and calculate, that is:
Wherein, N is the column number of i block 120, and Cij is the pixel value of this block 120, and Rij is second image 110 corresponding pixel value.This matching operation also can be carried out an addition absolute error (sum absolute error) and calculate, that is:
Wherein, N is the column number of i block 120, and Cij is the pixel value of this block 120, and Rij is second image 110 corresponding pixel value.Owing to have 41 positions in this first sub-search window 141, so this first group of measurement has 41 numerical value.
In step S350, by this first group of measurement, in this first sub-search window 141, choose the position 1411 of measurement minimum, this represents in second image 110 position the most close with this block 120.Yet in the present invention, it only carries out the matching operation of the corresponding pixel of second image of pixel and this 110 of this block 120 to the position in the first sub-search window 141, the position in this second sub-search window 142 is not carried out the matching operation of the corresponding pixel of second image of pixel and this 110 of this block 120, so selected position 1411 only can be considered regional minimum value (localminimum) position in this first sub-search window 141.Yet according to statistics and thumb rule, there is universe minimum value (global minimum) position near this zone minimum value (localminimum) position, so again to the position 1421,1422,1423 and 1424 near the second sub-search window 142 this minimum value position 1411, carry out the matching operation of the corresponding pixel of second image of pixel and this 110 of this block 120, the matching operation value of being obtained by position 1411,1421,1422,1423 and 1424 is chosen the position of measurement minimum again, with the motion-vector as this block.
Because the mobile of optical mouse is that continuity is arranged, as shown in Figure 6, when optical mouse moves toward the lower left corner, T oblique line figure in the continuous image that it captured (the 0th image 100, first image 130, second image 110) then moves toward the upper right corner, can find out that wherein the T oblique line figure 111 in second image 110 and the motion-vector of the T oblique line figure 131 in first image 130 are (1,1), and the motion-vector of the T oblique line figure 101 in the T oblique line figure 131 in first image 130 and the 0th image 100 also is (1,1).So in step S360, carrying out one and estimate the displacement step, is the motion-vector according to the block in the 0th image 100 that corresponds to this block, to estimate the motion-vector of this block, in present embodiment, the position 1425 that this estimated motion vector 150 is pointed among Fig. 5.
In step S370, carry out one second and calculate the relevant matches computing, be according to this estimated motion vector 150, and to the position 1426,1427,1428 and 1429 near the second sub-search window 142 this estimated motion vector 150 position 1425 pointed, carry out the matching operation of the corresponding pixel of second image of pixel and this 110 of this block 120, to produce one second group of measurement.
In step S380, be to carry out one second determining moving vectors step, by this first group of measurement and this second group of measurement, choose the position of measurement minimum, and calculate the vector at this block center, with motion-vector as this block.
As shown in the above description, (=41+4+5) the inferior matching operation of only need carrying out 50 among the Yu Benfa, a block 120 corresponding motion-vector in second image 110 in first image 130 can be found out, first block 120 in the image 130 corresponding motion-vector in second image 110 can be found out and need not to carry out 81 times matching operation as known technology.
The foregoing description only is to give an example for convenience of description, and the interest field that the present invention advocated should be as the criterion so that claim is described certainly, but not only limits to the foregoing description.
Claims (18)
1. one kind in order to judge the best relevant matches method of track behavior, it is by in the 0th image in the video data stream, first image and second image, look for a target block corresponding motion-vector in this second image in this first image, each Zhang Qianshu image all is made up of with row and row mode plurality of blocks, each this block comprises a plurality of pixels of forming with row and row mode, it is characterized in that this method comprises:
One search window deciding step is by determining a search window position and a size in this second image;
One search window is distinguished step, and the lane place in this search window is divided into the sub-search window of plural groups;
One sub-search window is chosen step, by choosing wherein one group in the sub-search window of this plural groups, to carry out search window as one;
One first calculates the relevant matches calculation step, be that center position with this target block is placed on all positions in this execution search window respectively, the pixel of this target block is carried out matching operation with the pixel of this second image respectively, to produce one first group of measurement; And
One first determining moving vectors step by this first group of measurement, is chosen the position of measurement minimum, and is calculated the vector at this target block center, with the motion-vector as this target block.
2. the best relevant matches method in order to the behavior of judgement track as claimed in claim 1 is characterized in that, wherein also comprises:
One estimates the displacement step, according to the motion-vector of the block in the 0th image that corresponds to this target block, to estimate the motion-vector of this target block, to produce an estimated motion vector;
One second calculates the relevant matches calculation step, is the close position according to this estimated motion vector, and the pixel that second image of pixel and this of this target block is corresponding is carried out matching operation, to produce one second group of measurement; And
One second determining moving vectors step by this first group of measurement and this second group of measurement, is chosen the position of measurement minimum, and is calculated the vector at this position and this target block center, with the motion-vector as this target block.
3. as claimed in claim 1 in order to judge the best relevant matches method of track behavior, it is characterized in that wherein, this first determining moving vectors step also comprises the following step:
Choose the close position of the position of measurement minimum according to this, the execution matching operation of the pixel that second image of pixel and this of this target block is corresponding, and choose the position of measurement minimum, with motion-vector as this target block.
4. as claimed in claim 1 in order to judge the best relevant matches method of track behavior, it is characterized in that wherein, this center is to be positioned at this first image center position.
5. as claimed in claim 4 in order to judge the best relevant matches method of track behavior, it is characterized in that wherein, the center of this search window is identical with this target block center.
6. as claimed in claim 1 in order to judge the best relevant matches method of track behavior, it is characterized in that wherein, this sub-search window is mutual mutual exclusion.
7. the best relevant matches method in order to the behavior of judgement track as claimed in claim 1 is characterized in that, wherein, this first calculating relevant matches calculation step is to carry out a Mean Square Error to calculate.
8. the best relevant matches method in order to the behavior of judgement track as claimed in claim 1 is characterized in that, wherein, this first calculating relevant matches calculation step is to carry out a mean absolute error to calculate.
9. the best relevant matches method in order to the behavior of judgement track as claimed in claim 1 is characterized in that, wherein, this first calculating relevant matches calculation step is to carry out an addition absolute error to calculate.
10. one kind in order to judge the best relevant matches system of track behavior, it is by in the 0th image in the video data stream, first image and second image, look for a target block corresponding motion-vector in second image in this first image, each Zhang Qianshu image is made up of with row and row mode plurality of blocks, each this block comprises a plurality of pixels of forming with row and row mode, it is characterized in that this system comprises:
One light source, it is in order to illuminate a sample plane;
One pel array device, it is made up of a plurality of photo-sensitive cell, to capture this sample plane, flows and form this video data;
One simulates to digital switching device, is coupled to this pel array device, to change this video data circulation into digital signal; And
One control device is coupled to this light source and this is simulated to digital switching device, simulates to the sequential of digital switching device conversion and the sequential of this light source igniting in order to control this;
Wherein, this control device is by determining a search window position and a size in this second image, lane place in this search window is divided into the sub-search window of plural groups, and by choosing one group in the sub-search window of this plural groups, to carry out search window as one, again the central point of this target block is placed on all positions in this execution search window respectively, the pixel of this target block is carried out matching operation with the pixel of this second image respectively, to produce one first group of measurement, at last by this first group of measurement, choose the position of measurement minimum, and calculate the vector at this target block center, with motion-vector as this target block.
11. it is as claimed in claim 10 in order to judge the best relevant matches system of track behavior, it is characterized in that, wherein, this control device is also according to the motion-vector that corresponds to the block in the 0th image of this target block, to estimate the motion-vector of this target block, to produce an estimated motion vector, and according to the close position of this estimated motion vector, the pixel that the pixel of this target block is corresponding with this second image is carried out matching operation, to produce one second group of measurement, again by this first group of measurement and this second group of measurement, choose the position of measurement minimum, and calculate the vector at this position and this target block center, with motion-vector as this target block.
12. as claimed in claim 10 in order to judge the best relevant matches system of track behavior, it is characterized in that wherein, this center is to be positioned at this first image center position.
13. as claimed in claim 12 in order to judge the best relevant matches system of track behavior, it is characterized in that wherein, the center of this search window is identical with this target block center.
14. as claimed in claim 10 in order to judge the best relevant matches system of track behavior, it is characterized in that wherein, this sub-search window is mutual mutual exclusion.
15. as claimed in claim 10 in order to judge the best relevant matches system of track behavior, it is characterized in that wherein, this matching operation is to carry out a Mean Square Error to calculate.
16. as claimed in claim 10 in order to judge the best relevant matches system of track behavior, it is characterized in that wherein, this matching operation is to carry out a mean absolute error to calculate.
17. as claimed in claim 10 in order to judge the best relevant matches system of track behavior, it is characterized in that wherein, this matching operation is to carry out an addition absolute error to calculate.
18. as claimed in claim 10 in order to judge the best relevant matches system of track behavior, it is characterized in that wherein, this light source is a light emitting diode.
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| CN101276248B (en) * | 2007-03-27 | 2010-08-04 | 义隆电子股份有限公司 | Multiple molded boards one-dimensional area matching method and apparatus |
| KR101634562B1 (en) * | 2009-09-22 | 2016-06-30 | 삼성전자주식회사 | Method for producing high definition video from low definition video |
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| CN1109245A (en) * | 1993-08-03 | 1995-09-27 | 索尼公司 | Efficient motion vector detection |
| US6055025A (en) * | 1993-12-21 | 2000-04-25 | Lucent Technologies, Inc. | Method and apparatus for detecting abrupt and gradual scene changes in image sequences |
| US6563550B1 (en) * | 2000-03-06 | 2003-05-13 | Teranex, Inc. | Detection of progressive frames in a video field sequence |
| CN1461556A (en) * | 2001-02-21 | 2003-12-10 | 皇家菲利浦电子有限公司 | Facilitating motion estimation |
| CN1489112A (en) * | 2002-10-10 | 2004-04-14 | 北京中星微电子有限公司 | Sports image detecting method |
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| CN1109245A (en) * | 1993-08-03 | 1995-09-27 | 索尼公司 | Efficient motion vector detection |
| US6055025A (en) * | 1993-12-21 | 2000-04-25 | Lucent Technologies, Inc. | Method and apparatus for detecting abrupt and gradual scene changes in image sequences |
| US6563550B1 (en) * | 2000-03-06 | 2003-05-13 | Teranex, Inc. | Detection of progressive frames in a video field sequence |
| CN1461556A (en) * | 2001-02-21 | 2003-12-10 | 皇家菲利浦电子有限公司 | Facilitating motion estimation |
| CN1489112A (en) * | 2002-10-10 | 2004-04-14 | 北京中星微电子有限公司 | Sports image detecting method |
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