CN105741253A - Enhancement estimation method of image fractal feature on the basis of merge replication - Google Patents
Enhancement estimation method of image fractal feature on the basis of merge replication Download PDFInfo
- Publication number
- CN105741253A CN105741253A CN201610054984.0A CN201610054984A CN105741253A CN 105741253 A CN105741253 A CN 105741253A CN 201610054984 A CN201610054984 A CN 201610054984A CN 105741253 A CN105741253 A CN 105741253A
- Authority
- CN
- China
- Prior art keywords
- image
- fractal
- mirror
- estimation
- merging
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000010076 replication Effects 0.000 title abstract 6
- 238000012545 processing Methods 0.000 claims abstract description 9
- 230000003362 replicative effect Effects 0.000 claims description 18
- 239000011521 glass Substances 0.000 claims description 14
- 230000002708 enhancing effect Effects 0.000 claims description 8
- 238000003384 imaging method Methods 0.000 claims description 8
- 230000035945 sensitivity Effects 0.000 claims description 5
- 238000004422 calculation algorithm Methods 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 5
- 238000003909 pattern recognition Methods 0.000 description 5
- 230000011218 segmentation Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000005728 strengthening Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 206010010356 Congenital anomaly Diseases 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000739 chaotic effect Effects 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses an enhancement estimation method of an image fractal feature on the basis of merge replication. An original image is subjected to n times of merge replication processing, and a texture image which is newly formed has a bigger size so as to bring convenience for carrying out multi-scale statistics; a box counting method is taken as an example, and the image needs to be subjected to meshing at different scales for realizing multi-scale coverage; if a merge replication form is adopted to enlarge image scale, the approximate number of the enlarged image size can be adopted as a mesh size to guarantee that the multi-scale statistics has enough data; since the fractal features of a mirror image and an original image are consistent, the complexity level of a new texture image formed by the arrangement and combination of the original image and the mirror image of the original image can be considered to only contain the complexity degree of the original image and complexity change formed by image arrangement; in addition, the complexity level of the arrangement and combination is increased along with the increase of the times of the merge replication; and the image fractal feature is enhanced along with the increase of the times of the merge replication, and the fractal dimension of the image is enlarged.
Description
Technical field
The invention belongs to fractal image processing technology field, be specifically related to a kind of based on the enhancing method of estimation merging the image type charcteristics replicated.
Background technology
Fractal theory results from beginning of the eighties late 1970s, is the new branch of science of research irregular figure and chaotic motion.Associate naturally between fractal theory and image has established its application in image procossing, and the fractal characteristic of image has also attracted the concern of great amount of images researcher.A lot of natural scenes in nature all can be by what fractal model was been described by, for instance sky, ocean and ground etc..And for the surface of culture and space structure, it also exists intrinsic difference with the characteristic rule described by fractal model.Therefore, available fractal characteristic is as estimating that man-made target detects.Fractal characteristic in the core tool of image processing field, is possible not only to the degree of irregularity of tolerance imaging surface as applications of fractal, and has the invariance of multiple dimensioned multiresolution change, and the perception of imaging surface grain roughness is consistent with human vision by this.In sum, fractal characteristic becomes the effective way describing imaging surface feature.Image fractal characteristic common in research has fractal dimension, Hurst index, fractal intercept feature etc., and this fractal characteristic, mainly for fractal dimension, is carried out strengthening and estimates by the present invention.
Based in the fractal image processing field such as the shape analysis of fractal dimension, pattern recognition, Texture Segmentation, mostly the flow process of whole algorithm is each pixel of image is taken neighborhood window, or the big window such as directly image uniform is divided into, utilize the fractal dimension computational methods such as box-covering method to estimate the fractal dimension of this image in window sub-block, according to the fractal dimension calculated, image is carried out further shape analysis, pattern recognition, Texture Segmentation etc..For above-mentioned algorithm flow, it is primarily present two large problems.
First, be window size select problem.In order to reflect the complexity that image local changes, detect the local detail of image, usually require that window size is little as much as possible.But owing to the mode of the fractal dimension multiple dimensioned covering of many employings is calculated, it calculates is a process added up, if window size is too small, will be unable to obtain the statistical data of abundant group, to such an extent as to fractal dimension cannot calculate or result of calculation is not accurate enough.
Secondly, it is the problem of algorithm sensitivity.Owing to fractal theory self has yardstick statistical property, algorithm is congenital has relatively low sensitivity for image change, and only when image change is enough violent, significant change just can occur its fractal dimension.When this also results in actual treatment image, the fractal dimension of parts of images details is distinguished inconspicuous with background area, is unfavorable for carrying out the image procossing such as shape analysis, pattern recognition, Texture Segmentation.
Summary of the invention
In view of this, it is an object of the invention to provide a kind of method the fractal characteristic of image to be strengthened and estimated, solve the unconspicuous problem of image fractal characteristic of problem that small-sized image fractal characteristic is difficult to estimate and extraction, original image can be carried out size and be exaggerated in order to the estimation of image fractal characteristic, and its fractal characteristic of image after amplification is enhanced compared to original image.
A kind of based on the enhancing method of estimation merging the image fractal characteristic replicated, comprise the steps:
The first step, it is determined that original image to be estimated；
Second step, the original image that the first step is determined carries out horizon glass picture, vertical mirror and diagonal angle mirror image processing respectively；
3rd step, by original image with its horizon glass picture, vertical mirror and diagonal mirror picture according to the arrangement of corresponding mirror position and combination, obtains size and is enlarged into the image of twice, is the image after first time merging replicates；
4th step, image fractal dimension potential demand according to actual needs and sensitivity requirement, it is determined that merge frequency n；
The image that first time is merged after replicating carries out horizon glass picture, vertical mirror and diagonal angle mirror image processing respectively, and is merged the image after replicating with its horizon glass picture, vertical mirror and diagonal mirror picture first time according to the arrangement of corresponding mirror position and combination, obtain second time and merge the image after replicating；By that analogy, finally give size and amplify 2nImage again, is n-th and merges the image after replicating；
5th step, merges the image after replicating to n-th and carries out the calculating of fractal characteristic, be the estimation of the fractal characteristic of described original image to be estimated.
It is also preferred that the left described merging number of copy times n takes 2～4.
It is also preferred that the left adopt box-covering method to carry out fractal characteristic estimation: regard the image merged after replicating as three-dimensional surface, z-axis representative image gray scale；Constructing three-dimensional box with yardstick r and cover whole imaging surface, statistics covers required minimum box number Nr；By changing the yardstick r covering box, calculate and under different scale r, cover the number N of box required for whole imaging surfacer, definition according to box-counting dimension:
Distributed points (log (1/r), the log (N in log-log coordinate system corresponding to formula (1)r)) carrying out least square linear fit, the slope of the fitting a straight line obtained is the fractal dimension of image.
It is also preferred that the left adopt blanket method to carry out fractal characteristic estimation.
It is also preferred that the left adopt Fractal Brownian method to carry out fractal characteristic estimation.
There is advantages that
Original image adopts n time the image processing method merging duplication can obtain a size and amplifies 2nThe texture image being arranged in a combination based on original image again.On the one hand, the texture image size being newly formed is bigger, it is simple to carry out multiple dimensioned statistics.Box-covering method is example, in order to realize multiple dimensioned covering, it is necessary to different yardsticks to image division grid.Increase graphical rule according to merging the mode replicated, the approximate number of picture size just can be adopted after increase as size of mesh opening, it is ensured that multiple dimensioned statistics has enough data.
On the other hand, owing to mirror image is consistent with the fractal characteristic of original image, it is believed that the new texture image that original image and its mirror alignment combine, its complexity only comprises the complexity of original image, and the complexity that graphical arrangement is formed changes, and the complexity of permutation and combination is to increase with the increase merging number of copy times.In brief, the fractal characteristic of image is strengthened along with the increase merging number of copy times, and the fractal dimension of image equally also can increase.
In sum, merge clone method and can be used in strengthening estimation image fractal characteristic, adopt the method to be beneficial to realization based on image procossing such as the shape analysis of fractal characteristic, pattern recognition, Texture Segmentation.
Accompanying drawing explanation
Fig. 1 (a) is based in the fractal characteristic estimation of pixel the original image window chosen；Fig. 1 (b) is based in the fractal characteristic estimation in region the original image window chosen；
Fig. 2 (a) is the original image determined in the method for the present invention；Fig. 2 (b) is the horizontal mirror image of original image；Fig. 2 (c) is the vertical mirror image of original image；Fig. 2 (d) is the diagonal angle mirror image of original image；
Fig. 3 is the result schematic diagram adopting difference to merge number of copy times, wherein, merges number of copy times n=1 in Fig. 3 (a)；Fig. 3 (b) merges number of copy times n=2；Fig. 3 (c) merges number of copy times n=3；
Fig. 4 is two groups of detection images in Brodatz texture searching, Fig. 4 (a) and Fig. 4 (b) respectively D64 and D65 image；Fig. 4 (c) and Fig. 4 (d) respectively D95 and D96 image.
Detailed description of the invention
Develop simultaneously embodiment below in conjunction with accompanying drawing, describe the present invention.
Here main for this fractal characteristic of fractal dimension, proposition method is illustrated.
The technical scheme of the method is: carry out image copying firstly for original image or image window, specifically includes horizon glass picture, vertical mirror and diagonal mirror picture.Why carrying out image copying and mainly consider that image is carried out mirror image can't change the complexity of image, namely mirror image is consistent with the fractal characteristic of original image.The image copying image obtained is combined according to corresponding mirror position with original image, forms a size and be enlarged into the new images of twice.Merge according to mirror position and primary concern is that reducing by each block of image as far as possible produces bigger sudden change when splicing, it is ensured that merge the seriality of image, thus avoiding the Spline smoothing that image mosaic place produces to destroy the fractal characteristic of original image.In like manner, can proceeding to merge for the image after amplifying and replicate, the size of image increases along with the increase merging number of copy times；The fractal dimension of image after utilizing box-covering method calculating to amplify, result of calculation can be used for estimating the fractal dimension of original image.
The first step, it is determined that original image window.As it is shown in figure 1, no matter be based on the fractal characteristic algorithm for estimating of pixel, it is also based on the fractal characteristic algorithm for estimating in region, it is necessary first to determine the image window required for algorithm Practical Calculation.It is assumed here that the original image window size determined is M × N, it is necessary to the fractal characteristic of estimation is fractal dimension.
Second step, image copying.Assume that the gray scale chart of the original image of M × N is shown as G0(x, y), then the mirror image of original image is as follows:
Horizon glass picture: GH(x, y)=G0(M-x+1,y)；
Vertical mirror: GV(x, y)=G0(x,N-y+1)；
Diagonal mirror picture: GD(x, y)=G0(M-x+1,N-y+1)；
As in figure 2 it is shown, original image can obtain horizon glass picture, vertical mirror and diagonal mirror picture by image copying.Now, each mirror image is consistent with the fractal characteristic of original image, and fractal dimension is also identical.
3rd step, image merges.By original image with its horizon glass picture, vertical mirror and diagonal mirror picture according to corresponding mirror position permutation and combination, size can be obtained and be enlarged into the image of twice, be expressed as:
4th step, it is determined that merge number of copy times n.Merging frequency n is finally determined according to actual dimension potential demand and sensitivity requirement；The image that first time is merged after replicating carries out horizon glass picture, vertical mirror and diagonal angle mirror image processing respectively, and is merged the image after replicating with its horizon glass picture, vertical mirror and diagonal mirror picture first time according to the arrangement of corresponding mirror position and combination, obtain second time and merge the image after replicating；By that analogy, finally give size and amplify 2nImage again, is n-th and merges the image after replicating；The texture image result that fractal characteristic strengthens is as shown in Figure 3.According to the experiment to Fig. 4, it is proposed that merge number of copy times n take 2～4 times relatively reasonable.
5th step, estimates fractal characteristic.Here it is introduced for the box-covering method calculating this fractal characteristic of fractal dimension conventional.Regard the image merged after replicating as three-dimensional surface, z-axis representative image gray scale.Constructing three-dimensional box with yardstick r and cover whole imaging surface, statistics covers required minimum box number Nr.By changing the yardstick r covering box, calculate under different scale r the number N of box required for overlay imager, definition according to box-counting dimension
Can in log-log coordinate system, by these distributed points (log (1/r), log (Nr)) carrying out least square linear fit, the slope of the fitting a straight line obtained is the fractal dimension of image.
The methods such as blanket method, Fractal Brownian can also be adopted to carry out fractal characteristic estimation.
Image fractal characteristic can be strengthened owing to merging duplication, merge the Calculated Values of Fractal Dimensions of image after replicating to increase based on the fractal dimension of original image, this result can be used for estimating the fractal dimension of original image, and is applied in the image procossing such as shape analysis, pattern recognition, Texture Segmentation.
Merge, in order to verify, the impact replicated image fractal dimension, from Brodatz texture searching, have chosen these two groups of images of D64 and D65, D95 and D96 here, as shown in Figure 4.Often group image is all made up of two width texture images of differing complexity.The front and back calculated fractal dimension of image is replicated, the effect of checking proposition method by contrasting to merge.Four width for selecting are desired to make money or profit as shown in table 1 by box-covering method calculated fractal dimension result.
Table 1 image fractal dimension result of calculation
By table 1 it is found that can merge, along with window, the number of times increase replicated for each the result of calculation testing its fractal dimension of image and increase.And contrast two width texture image D64 and the D65 of Dan Zuzhong, it is possible to having found that the texture of D65 is increasingly complex, its fractal dimension calculating gained is consistently greater than D64 in different merging under the number of times replicated.In like manner, the fractal dimension of D95 is consistently greater than D96.Test result indicate that, the fractal dimension of image is increased along with merging the increase of number of copy times really, and the bigger image of fractal dimension merge replicate after its fractal dimension still bigger.It will be appreciated that along with the increase merging number of copy times, be gradually reduced with the difference of two width image fractal dimension of group.The complexity that rational explanation is permutation and combination can increase along with the increase merging number of copy times, and original image and mirror image complexity thereof are constant, therefore the increase of number of copy times can cause that the complexity that the complexity of permutation and combination is combined rear image is contributed more greatly, reduces the impact of original image fractal character differences.This provides the benefit that with second point of mentioning and to match.Based on above-mentioned experiment, merge the frequency n replicated and should control at 2～4 times, so both can ensure that the multiple dimensioned statistics of small-sized image has enough data, can also ensure that the fractal characteristic of the image after merging still has enough diversityes.
In sum, these are only presently preferred embodiments of the present invention, be not intended to limit protection scope of the present invention.All within the spirit and principles in the present invention, any amendment of making, equivalent replacement, improvement etc., should be included within protection scope of the present invention.
Claims (5)
1. the enhancing method of estimation based on the image fractal characteristic merging duplication, it is characterised in that comprise the steps:
The first step, it is determined that original image to be estimated；
Second step, the original image that the first step is determined carries out horizon glass picture, vertical mirror and diagonal angle mirror image processing respectively；
3rd step, by original image with its horizon glass picture, vertical mirror and diagonal mirror picture according to the arrangement of corresponding mirror position and combination, obtains size and is enlarged into the image of twice, is the image after first time merging replicates；
4th step, image fractal dimension potential demand according to actual needs and sensitivity requirement, it is determined that merge frequency n；
The image that first time is merged after replicating carries out horizon glass picture, vertical mirror and diagonal angle mirror image processing respectively, and is merged the image after replicating with its horizon glass picture, vertical mirror and diagonal mirror picture first time according to the arrangement of corresponding mirror position and combination, obtain second time and merge the image after replicating；By that analogy, finally give size and amplify 2nImage again, is n-th and merges the image after replicating；
5th step, merges the image after replicating to n-th and carries out the calculating of fractal characteristic, be the estimation of the fractal characteristic of described original image to be estimated.
2. a kind of based on the enhancing method of estimation merging the image fractal characteristic replicated as claimed in claim 1, it is characterised in that described merging number of copy times n takes 2～4.
3. a kind of based on the enhancing method of estimation merging the image fractal characteristic replicated as claimed in claim 1, it is characterised in that to adopt box-covering method to carry out fractal characteristic estimation: to regard the image merged after replicating as three-dimensional surface, z-axis representative image gray scale；Constructing three-dimensional box with yardstick r and cover whole imaging surface, statistics covers required minimum box number Nr；By changing the yardstick r covering box, calculate and under different scale r, cover the number N of box required for whole imaging surfacer, definition according to box-counting dimension:
Distributed points (log (1/r), the log (N in log-log coordinate system corresponding to formula (1)r)) carrying out least square linear fit, the slope of the fitting a straight line obtained is the fractal dimension of image.
4. a kind of based on the enhancing method of estimation merging the image fractal characteristic replicated as claimed in claim 1, it is characterised in that to adopt blanket method to carry out fractal characteristic estimation.
5. a kind of based on the enhancing method of estimation merging the image fractal characteristic replicated as claimed in claim 1, it is characterised in that to adopt Fractal Brownian method to carry out fractal characteristic estimation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610054984.0A CN105741253A (en) | 2016-01-27 | 2016-01-27 | Enhancement estimation method of image fractal feature on the basis of merge replication |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610054984.0A CN105741253A (en) | 2016-01-27 | 2016-01-27 | Enhancement estimation method of image fractal feature on the basis of merge replication |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105741253A true CN105741253A (en) | 2016-07-06 |
Family
ID=56247629
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610054984.0A Pending CN105741253A (en) | 2016-01-27 | 2016-01-27 | Enhancement estimation method of image fractal feature on the basis of merge replication |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105741253A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107610107A (en) * | 2017-09-01 | 2018-01-19 | 华中科技大学 | A kind of three-dimensional vascular plaque features of ultrasound pattern based on dimension describes method |
| CN108038856A (en) * | 2017-12-22 | 2018-05-15 | 杭州电子科技大学 | Based on the infrared small target detection method for improving Multi-scale Fractal enhancing |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102360399A (en) * | 2011-10-13 | 2012-02-22 | 苏州大学 | Generation method of printed fabric patterns based on generalized Mandelbrot set |
-
2016
- 2016-01-27 CN CN201610054984.0A patent/CN105741253A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102360399A (en) * | 2011-10-13 | 2012-02-22 | 苏州大学 | Generation method of printed fabric patterns based on generalized Mandelbrot set |
Non-Patent Citations (3)
| Title |
|---|
| YUNQI WANG ET AL.: "Edge extraction of optical subaperture based on fractal dimension method", 《SPIE OPTICAL ENGINEERING + APPLICATIONS》 * |
| 武震: "基于分形理论的图像分割", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 赵海英 等: "图像分形维数计算方法的比较", 《计算机系统应用》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107610107A (en) * | 2017-09-01 | 2018-01-19 | 华中科技大学 | A kind of three-dimensional vascular plaque features of ultrasound pattern based on dimension describes method |
| CN107610107B (en) * | 2017-09-01 | 2019-09-13 | 华中科技大学 | A kind of three-dimensional vascular plaque features of ultrasound pattern based on dimension describes method |
| CN108038856A (en) * | 2017-12-22 | 2018-05-15 | 杭州电子科技大学 | Based on the infrared small target detection method for improving Multi-scale Fractal enhancing |
| CN108038856B (en) * | 2017-12-22 | 2020-08-04 | 杭州电子科技大学 | Infrared small target detection method based on improved multi-scale fractal enhancement |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jancosek et al. | Exploiting visibility information in surface reconstruction to preserve weakly supported surfaces | |
| CN101520894B (en) | Method for extracting significant object based on region significance | |
| CN112288008B (en) | Mosaic multispectral image disguised target detection method based on deep learning | |
| CN106981080A (en) | Night unmanned vehicle scene depth method of estimation based on infrared image and radar data | |
| CN104346608A (en) | Sparse depth map densing method and device | |
| CN109146797B (en) | A kind of impulsive noise ancient book image inpainting method sparse based on Lp pseudonorm and overlapping group | |
| CN106679634A (en) | Spatial non-cooperative target pose measurement method based on stereoscopic vision | |
| CN109509163B (en) | FGF-based multi-focus image fusion method and system | |
| CN102289806B (en) | Method for measuring image definition by utilizing multi-scale morphological characteristics | |
| CN103049891B (en) | Video image deblurring method based on self-adaption window selection | |
| CN101610425A (en) | A kind of method and apparatus of evaluating stereo image quality | |
| CN116958453B (en) | Three-dimensional model reconstruction method, device and medium based on nerve radiation field | |
| CN102903111B (en) | Large area based on Iamge Segmentation low texture area Stereo Matching Algorithm | |
| CN106447762A (en) | Three-dimensional reconstruction method based on light field information and system | |
| Baozhong et al. | Overview of image noise reduction based on non-local mean algorithm | |
| CN103971354A (en) | Method for reconstructing low-resolution infrared image into high-resolution infrared image | |
| Luo et al. | IMNN-LWEC: A novel infrared small target detection based on spatial–temporal tensor model | |
| Stentoumis et al. | A local adaptive approach for dense stereo matching in architectural scene reconstruction | |
| CN105741253A (en) | Enhancement estimation method of image fractal feature on the basis of merge replication | |
| Lin et al. | Feature preserving filling of holes on point sampled surfaces based on tensor voting | |
| CN104463822A (en) | Multi-focus image fusing method and device based on multi-scale overall filtering | |
| CN117115359B (en) | Multi-view power grid three-dimensional space data reconstruction method based on depth map fusion | |
| CN110288701A (en) | A kind of three-dimensional rebuilding method and terminal based on deep focus | |
| Stentoumis et al. | Implementing an adaptive approach for dense stereo-matching | |
| Zhu et al. | Hybrid scheme for accurate stereo matching |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20160706 |