CN102693409A - Method for quickly identifying two-dimension code system type in images - Google Patents
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Abstract
The invention discloses a method for quickly identifying two-dimension code system type in images, comprising a learning training process and a classification identifying process. The learning training process is as follows: collecting and building a sample image set of various two-dimension code images; converting each sample image into a grey image, performing Gaussian smoothing filtering and binaryzation to obtain binaryzation images; scanning prospect boundaries of the binaryzation images in the horizontal and vertical directions, obtaining an outer boundary point set of the two-dimension code; enabling the two-dimension code to be horizontal by rotating images, achieving horizontal correction of the two-dimension code; performing partitioning, combining and normalizing for the two-dimension code; performing fast wavelet transform for the normalized sample image to obtain a wavelet characteristic sample set. The classification identifying process is as follows: extracting wavelet characteristic of the to-be-identified image to build a distance measurement model; using the K nearest neighbor algorithm to identify code system type. The method is convenient and quick, has real-time performance, accuracy and high identification rate.
Description
Technical field
The invention belongs to Digital Image Processing, computer vision and mode identification method, particularly relate to the method for quickly identifying of code system type in the image in 2 D code.
Background technology
Two-dimensional bar code (2-dimensional bar code) is meant that on the basis of bar code, expanding another dimension has readable bar code.The width of bar code is being put down in writing data, and its length is not put down in writing data.The length of two-dimensional bar code, width are all being put down in writing data." anchor point " and " fault tolerant mechanism " that two-dimensional bar code has bar code not have.Fault tolerant mechanism is even without recognizing whole bar codes or saying that bar code has when stained, also can correctly reduce information on the bar code.At present, the two-dimensional bar code of numerous species occurred, code system wherein commonly used has: Data Matrix, QR Code, PDF417 etc.Characteristics such as two-dimensional bar code has that storage capacity is big, confidentiality is high, tracking property is high, anti-damage property is strong, redundant property is big, cost is cheap, these characteristics are specially adapted to aspects such as list, safe and secret, tracking, license, stock taking, data redundant.
Taking the image that comprises two-dimension code through mobile phone or camera camera, utilize digital image processing techniques to discern, is domestic and international main direction of studying to two-dimensional bar code.Owing to there is multiple two-dimension code code system at present, before carrying out two-dimension code identification decoding, to discern code system earlier, could carry out decoding processing according to the code system rule then.Common processing mode is according to various code system rules; The view finding figure of two-dimension code in the scan image one by one; Confirm the code system type according to the Search Results of view finding figure, like National Standard quick response matrix code QR Code " in the parametric technique that provides be the view finding figure of search QR sign indicating number in entire image; The characteristics of QR sign indicating number view finding figure are that the ratio of the suitable width of each element is 1:1:3:1:1; Survey in level and vertical direction, find out three such view finding figures, promptly determine it and be the QR sign indicating number.When the code system type more for a long time, the efficient of carrying out match search one by one is lower, in addition, this method is had relatively high expectations to image resolution ratio, has reduced the bulk velocity that two-dimension code is handled.
The key link of image object recognition technology is feature extraction and target classification.Feature extraction can be adopted several different methods, and common have contour feature, Fourier descriptor, a wavelet character etc.Wherein contour feature and Fourier descriptor have only been considered the image information of object boundary, and be very responsive to noise, influences discrimination.Wavelet character is that template image is transformed to the recognition feature in the wavelet field, can when keeping the object space relation, describe the frequency structure information in the image, compares with the characteristic based on boundary information, has adaptability preferably.After having extracted target signature, will classify to characteristic, to pick out the pattern class of target.Common classifying identification method comprises, K nearest neighbor method, SVMs and neural network etc.Wherein, SVMs can solve small sample, non-linearity and higher-dimension pattern recognition problem preferably, but method is comparatively complicated, and calculated amount is big.Neural network is the behavior of simulated animal neural network; Through adjusting the relevant connection relation between inner a large amount of neurode, reach the purpose of information processing, can solve a lot of nonlinear problems; But it has the theoretical question that does not much have solution, makes in practical application, to have a lot of difficulties.K arest neighbors (k-Nearest Neighbor; KNN) algorithm is K the point that in hyperspace, finds with the unknown sample arest neighbors; And according to this K the point classification judge unknown sample the class; Because theoretical maturation of K nearest neighbor method and use are simple, therefore are widely used in the pattern classification problem.
Summary of the invention
The purpose of this invention is to provide two-dimension code code system recognition methods in a kind of image fast, can be used as the pre-treatment step of two-dimension code identification decoding, to improve the whole efficiency that image in 2 D code is handled.
The objective of the invention is to realize like this: two-dimension code code system recognition methods in a kind of image fast mainly comprises two processes, i.e. learning training process and Classification and Identification process.
The learning training process may further comprise the steps:
1.1) through gathering the image in 2 D code of all kinds, various version, set up the sample image collection that is used for learning training;
1.2) for each sample image, convert thereof into gray level image after, carry out Gauss's smothing filtering; Remove the noise in the image; According to gray distribution of image information, adopt the maximum between-cluster variance method to carry out binary conversion treatment, in the bianry image that obtains then; The black module of two-dimension code is a prospect, and remainder is a background;
1.3) scan the prospect border of binary image respectively in the horizontal and vertical directions; Obtain the outer boundary point set of two-dimension code; The minimum that adopts the rotation method that gets stuck to calculate point set covers rectangle; According to the orientation angles of minimum covering rectangle, image rotating is so that two-dimension code is in horizontality, thus the rectification of realization two-dimension code;
1.4) for the image behind the rectification, be the border with postrotational minimum covering rectangle, extract the image-region of two-dimension code.The corners branch of considering two-dimension code has comprised the characteristic that major part has distinguishing property; Piecemeal is carried out according to certain scale-up factor in the two-dimension code zone, choose the piecemeal that wherein is in the corner part, be combined into new sample image; And it is carried out normalization handle, obtain the normalization sample image;
1.5) each normalized sample image is carried out fast wavelet transform, will obtain wavelet conversion coefficient as characteristic, and then set up the wavelet character sample set that is used for follow-up classification;
The Classification and Identification process may further comprise the steps:
2.1) to image in 2 D code to be identified, according to step 1.2) to step 1.5) mode extract the wavelet character of image;
2.2) set up distance metric model based on the characteristic distribution weighting; Characteristic to be identified and sample characteristics are carried out the pointwise coupling; And with have a few the coupling weighted sum as the distance between the characteristic; Wherein the set-up mode of weight is; Give different weights for the point that is in different spatial, low more the closer to the some weight of regional center, high more the closer to the some weight at two-dimension code edge;
2.3) adopt K arest neighbors sorting algorithm to carry out the code system type identification, according to step 2.2) the middle distance metric model that defines, K the feature samples that chosen distance is nearest, the code system type that these sample great majority belong to is the type of image to be identified.
Beneficial effect of the present invention mainly contains following 2 points:
1, technical scheme provided by the invention is simple and efficient, and distinguishes the characteristic area of property through extracting in the two-dimension code tool, realizes high recognition, can take into account the real-time and the accuracy of processing;
2, recognition methods has only utilized the characteristics of image of two-dimension code, need not consider concrete code system rule, has general applicability preferably, can discern common two-dimension code type at present, and the profile that only requires two-dimension code is a rectangle.
The present invention is based on the target identification technology of image, through extracting the characteristics of image that has the property distinguished in the two-dimension code, the application model recognition methods is classified and identification to the two-dimension code code system, can greatly improve the code system recognition efficiency.
Description of drawings
Fig. 1 is system's schematic block diagram of the method for the invention.
Fig. 2 is the inventive method covers rectangle in the minimum of estimating the two-dimension code zone a synoptic diagram (one of top is former figure, and the left next one is the border point set, right next minimum covering rectangle for estimating).
Fig. 3 is that the inventive method is being chosen the synoptic diagram of image block.
Embodiment
Specifically describe embodiment of the present invention below in conjunction with accompanying drawing.
With reference to Fig. 1, specifically be presented in the method step of identification two-dimension code code system type in the image, the entire process process is divided into learning training and Classification and Identification, and wherein the step of learning training process is following:
Step 1: read the training image that includes two-dimension code, convert it into 256 grades gray level image, the nuclear that adopts Gaussian function to generate then carries out filtering, to remove the noise that exists in the image.According to gray distribution of image information, adopt big Tianjin method (being the maximum between-cluster variance method) to carry out binary conversion treatment, make that the black module in the two-dimension code is a display foreground, the image remainder is a background;
Step 2: in the horizontal direction, respectively from left to right, the outer boundary of progressive scanning picture prospect from right to left, obtain the outer boundary point of image vertical direction.Equally, in vertical direction, pursue the outer boundary of column scan display foreground respectively from the top down, from bottom to top, obtain the outer boundary point of image level direction.According to the outer boundary point set that obtains, shown in left figure below among Fig. 2, calculate the right of the convex closure that constitutes by these points, and then the minimum that obtains this point set covers rectangle to the heel point, the white rectangle shown in bottom-right graph among Fig. 2, with this rectangular area as the two-dimension code zone;
Step 3: cover the anglec of rotation that rectangle is confirmed two-dimension code according to the minimum that obtains, make two-dimension code be in horizontality through image rotating.Extract the image in 2 D code zone, piecemeal is carried out in this zone, the range of size of piecemeal is set to 5 ~ 7 times of two-dimension code basic module.The most representative view finding figure and the auxiliary positioning function figure of being characterized as in the image in 2 D code; These figures mainly are distributed in the fringe region of two-dimension code, therefore, select the regional image block that keeps to the side; Shown in the white rectangle piece among Fig. 3; Be combined as new sample image according to sequence of positions, reduce the redundant information of image, realize the characteristic dimensionality reduction;
Step 4: sample image is carried out normalization handle, have certain identifiability in order to guarantee image, the image resolution ratio after the normalization is set to 180 * 180 pixels; Utilize the fast wavelet transform algorithm, normalized sample image is converted into wavelet field, wavelet conversion coefficient is as the feature templates of follow-up classification.For every type two-dimension code, the image of gathering various version totally 100 width of cloth, then according to step 1 to step 4, obtain the feature templates of each sample, thereby set up the feature samples collection.
The step of Classification and Identification process is following:
Step 1:,, obtain the wavelet character of this image according to the disposal route of sample image to image in 2 D code to be identified;
Step 2: set up distance metric model, be used to measure two similarities between the wavelet character based on the characteristic distribution weighting.Because the representative characteristic of two-dimension code near the image border, therefore when characteristic matching, is given the different weight of characteristic allocation that is in different spatial, the weight of the characteristic allocation of part is high more the closer to the image border.The weight calculation of characteristic is put the Euclidean distance of picture centre for this, and weight belongs to and change between 0 and 1 a numerical value between (promptly greater than zero-sum smaller or equal to 1).With characteristic to be identified and sample characteristics pointwise coupling, institute has a few the weighted sum of mating as the distance between the characteristic;
Step 3: according to the distance model of step 2 definition; Calculate the wavelet character of image to be identified and the distance between the training set feature samples; Select wherein nearest K neighbours (getting K=10); According to the code system type under this K neighbour, selecting that maximum type of same type number is recognition result then.
Claims (4)
1. a two-dimension code code system kind identification method in the image fast is characterized in that method comprises two processing procedures, i.e. learning training process and Classification and Identification process, and the performing step of two processing procedures is following:
The treatment step of learning training process is:
1.1) through gathering the image in 2 D code of all kinds, various version, set up the sample image collection that is used for learning training;
1.2) for each sample image, convert thereof into gray level image after, carry out Gauss's smothing filtering; Remove the noise in the image; According to gray distribution of image information, adopt the maximum between-cluster variance method to carry out binary conversion treatment, in the binary image that obtains then; The black module of two-dimension code is a prospect, and remainder is a background;
1.3) scan the prospect border of binary image respectively in the horizontal and vertical directions; Obtain the outer boundary point set of two-dimension code; The minimum that adopts the rotation method that gets stuck to calculate point set covers rectangle; According to the orientation angles of minimum covering rectangle, image rotating is so that two-dimension code is in horizontality, thus the rectification of realization two-dimension code;
1.4) for the image behind the rectification, be the border with postrotational minimum covering rectangle, extract the image-region of two-dimension code; The corners branch of considering two-dimension code has comprised the characteristic that major part has distinguishing property; Piecemeal is carried out according to certain scale-up factor in the two-dimension code zone, choose the piecemeal that wherein is in the corner part, be combined into new sample image; And it is carried out normalization handle, obtain the normalization sample image;
1.5) each normalization sample image is carried out fast wavelet transform, the wavelet conversion coefficient that obtains as wavelet character, and then is set up the wavelet character sample set be used for follow-up classification;
The treatment step of Classification and Identification process is:
2.1) to image in 2 D code to be identified, according to step 1.2) to step 1.5) mode extract the wavelet character of image;
2.2) set up distance metric model based on the characteristic distribution weighting; Characteristic to be identified and sample characteristics are carried out the pointwise coupling; And with have a few the coupling weighted sum as the distance between the characteristic; Wherein the set-up mode of weight is; Give different weights for the point that is in different spatial, low more the closer to the some weight of regional center, high more the closer to the some weight at two-dimension code edge;
2.3) adopt K arest neighbors sorting algorithm to carry out the code system type identification, according to step 2.2) the middle distance metric model that defines, K the feature samples that chosen distance is nearest, the code system type that these sample great majority belong to is the type of image to be identified.
2. two-dimension code code system kind identification method in a kind of image fast according to claim 1; It is characterized in that; Said step 2.2) weight calculation of characteristic is put the Euclidean distance of picture centre for this in, weight belong to change between between numerical value 0 and 1 promptly greater than zero-sum smaller or equal to 1.
3. two-dimension code code system kind identification method in a kind of image fast according to claim 1 and 2 is characterized in that said step 2.3) in K be 10.
4. two-dimension code code system kind identification method in a kind of image fast according to claim 3 is characterized in that the profile of said two-dimension code is a rectangle.
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