Method and device for identifying decimal point position of nixie tube
阅读说明：本技术 一种数码管小数点位置识别方法及装置 (Method and device for identifying decimal point position of nixie tube ) 是由 华绘 于 2020-05-21 设计创作，主要内容包括：本发明公开了一种数码管小数点位置识别方法及装置,属于图像识别领域。针对现有技术中存在的小数点识别所需运算量大、适应性不强的问题,本发明提高了一种数码管小数点位置识别方法及装置,其中,数码管小数点位置识别方法包括以下步骤：获取待识别图像,对待识别图像进行预处理,得到预处理后的图像；对预处理后的图像进行卷积计算,得到卷积结果；对卷积结果进行密度聚类,得到若干个分组；对分组进行区域划分,得到位置区间；根据分组和位置区间,确定小数点位置。本发明对于不同种类数码管的小数点识别适应性强,对所需硬件的计算能力要求不高,降低了所需成本。(The invention discloses a method and a device for identifying decimal point positions of a nixie tube, and belongs to the field of image identification. Aiming at the problems of large computation amount and poor adaptability required by decimal point identification in the prior art, the invention provides a method and a device for identifying the position of a decimal point of a nixie tube, wherein the method for identifying the position of the decimal point of the nixie tube comprises the following steps: acquiring an image to be identified, and preprocessing the image to be identified to obtain a preprocessed image; performing convolution calculation on the preprocessed image to obtain a convolution result; performing density clustering on the convolution result to obtain a plurality of groups; dividing the groups into regions to obtain position intervals; and determining the position of the decimal point according to the grouping and the position interval. The invention has strong adaptability to the decimal point identification of different kinds of nixie tubes, has low requirement on the computing capacity of required hardware and reduces the required cost.)
1. A method for identifying positions of decimal points of a nixie tube is characterized by comprising the following steps:
acquiring an image to be identified, and preprocessing the image to be identified to obtain a preprocessed image;
performing convolution calculation on the preprocessed image to obtain a convolution result;
performing density clustering on the convolution result to obtain a plurality of groups;
dividing the groups into regions to obtain position intervals;
and determining the position of the decimal point according to the grouping and the position interval.
2. The method for identifying the decimal point position of the nixie tube according to claim 1, wherein the preprocessing of the image to be identified comprises the following steps:
carrying out graying processing on an image to be identified to obtain a grayscale image;
and carrying out binarization processing on the gray level image, judging whether the pixel point value in the image is larger than a first preset threshold value, if so, setting the gray level value of the pixel point of the point to be 1, otherwise, setting the gray level value to be 0, and finally obtaining the binary image.
3. The nixie tube decimal point position identification method according to claim 2, wherein the convolution calculation of the preprocessed image comprises the following steps:
setting a convolution kernel, and performing convolution calculation on the image to obtain a convolved image;
and traversing all pixel points in the convolved image, and extracting the coordinate value of which the pixel point value is 0 to obtain a two-dimensional array.
4. The method for identifying the decimal point position of a nixie tube according to claim 3, wherein the step of dividing the group into regions to obtain a position interval comprises the following steps:
carrying out de-duplication and sequencing processing on elements in all the groups to obtain a processed array;
traversing the processed array, calculating the difference between adjacent element values, judging whether the difference value is greater than a preset threshold value, if so, recording the previous element value, and finally obtaining a position interval.
5. The nixie tube decimal point position identifying method according to any one of claims 1-4, wherein the decimal point position is determined according to the grouping and position interval, comprising the following steps:
determining the grouping where the decimal point is located, traversing the number of elements in all the groupings, and selecting the grouping with the minimum number of elements;
and determining the position of the decimal point according to the position interval of the elements in the group where the decimal point is located.
6. The utility model provides a charactron decimal point position recognition device which characterized in that includes:
the image acquisition unit is used for acquiring an image to be identified, and preprocessing the image to be identified to obtain a preprocessed image;
the image denoising unit is used for carrying out convolution calculation on the preprocessed image to obtain a convolution result;
the density clustering unit is used for performing density clustering on the convolution result to obtain a plurality of groups;
the region dividing unit is used for performing region division on the groups to obtain position intervals;
and the position identification unit is used for determining the position of the decimal point according to the grouping and the position interval.
7. The device for identifying the decimal point position of nixie tube according to claim 6, wherein the image capturing unit comprises:
the graying processing module is used for performing graying processing on the image to be identified to obtain a grayscale image;
and the binarization processing module is used for carrying out binarization processing on the gray level image, judging whether the pixel point value in the image is greater than a first preset threshold value, if so, setting the pixel point value of the point to be 1, otherwise, setting the pixel point value to be 0, and finally obtaining a binary image.
8. The device for identifying the position of a decimal point of a nixie tube according to claim 7, wherein the image denoising unit comprises:
the convolution calculation module is used for setting a convolution kernel and carrying out convolution calculation on the image to obtain a convolved image;
and the pixel point extraction module is used for traversing all pixel points in the convolved image, extracting the coordinate value of which the pixel point value is 0, and obtaining the two-dimensional array.
9. The device for identifying the decimal point position of a nixie tube according to claim 8, wherein the area dividing unit comprises:
the de-reordering module is used for performing de-duplication and sequencing processing on elements in all the groups to obtain a processed array;
and the judging module is used for traversing the processed array, calculating the difference between adjacent element values, judging whether the difference value is greater than a preset threshold value, if so, recording the previous element value, and finally obtaining a position interval.
10. The nixie tube decimal point position identifying device according to any one of claims 6 to 9, wherein the position identifying unit comprises:
the grouping determination module is used for determining the grouping where the decimal point is located, traversing the number of elements in all the groupings, and selecting the grouping with the minimum number of elements;
and the position determining module is used for determining the position of the decimal point according to the position interval of the elements in the group where the decimal point is located.
Technical Field
The invention relates to the field of image recognition, in particular to a method and a device for recognizing decimal point positions of a nixie tube.
Background
The nixie tube, also called LED nixie tube, is an electronic device capable of displaying digital and other information, and can be lighted up by inputting relative current to different pins of the nixie tube, so that it can display all parameters which can be represented by digital number, such as time, date and temperature, etc. because of its low price, it is simple to use, and can be extensively used in the field of electric appliances.
With the development of industries, more industries begin to develop towards intellectualization, the number indication of a nixie tube is converted into intelligent identification from the prior artificial checking and recording, the decimal point of the number indication of the nixie tube cannot be well identified in the prior art, the number identification method of the nixie tube in the prior art mainly comprises a threading method and an OCR (optical character recognition), however, when the threading method identifies the number of the nixie tube, the identification effect of a single number is better, but the threading method mainly judges the number and cannot find the position of the decimal point; OCR recognition is a character recognition technique based on deep learning, but OCR recognition is used for decimal point recognition with a low accuracy, and neither of the above two methods is suitable for decimal point recognition.
The Chinese patent application, application number CN201611031884.2, published 2017, 3, 22 and discloses a digital instrument reading image recognition method, which comprises the steps of extracting an interested area in a panoramic image by using a template matching method according to a digital instrument image calibrated in advance, and extracting a single character area and a decimal point to-be-detected area in the interested area according to the relative position relation of calibrated characters; for a single character area, carrying out single character recognition by using a convolutional neural network character model trained in advance; carrying out decimal point detection on a decimal point region to be detected by utilizing a Cascade target detector which is trained in advance and based on block LBP coding characteristics and an Adaboost classifier, and carrying out post-processing on a detection result; and finally, reading is obtained according to the character, the decimal point and the sign recognition result. The method can identify the numbers, signs and decimal points of 0-9, and has the disadvantages that the method needs to extract an interesting region in a panoramic image in advance, and then extracts a single character region and a decimal point to-be-detected region in the interesting region according to the relative position relation of the calibration characters, for different digital instruments, the number display region in the image may change greatly, the detection region needs to be extracted again, and the adaptability is not strong; for decimal point detection, coding features need to be trained in advance, the required operation amount is large, and the requirement on the computing capacity of hardware is high.
Disclosure of Invention
1. Technical problem to be solved
Aiming at the problems of large computation amount and poor adaptability required by decimal point identification in the prior art, the invention provides a method and a device for identifying the position of a decimal point of a nixie tube, which can reduce the computation amount and improve the adaptability.
2. Technical scheme
The purpose of the invention is realized by the following technical scheme.
A method for identifying positions of decimal points of a nixie tube comprises the following steps:
acquiring an image to be identified, and preprocessing the image to be identified to obtain a preprocessed image;
performing convolution and traversal on the preprocessed image to obtain a convolution result;
performing density clustering on the convolution result to obtain a plurality of groups;
dividing the groups into regions to obtain position intervals;
and determining the position of the decimal point according to the grouping and the position interval.
Further, the image to be recognized is preprocessed, and the method comprises the following steps:
carrying out graying processing on an image to be identified to obtain a grayscale image;
and (3) carrying out binarization processing on the gray level image, judging whether the pixel point value in the image is larger than a first preset threshold value, if so, setting the pixel point value of the point to be 1, otherwise, setting the pixel point value to be 0, and finally obtaining a binary image.
Further, the convolution and traversal calculation of the preprocessed image includes the following steps:
setting a convolution kernel, and performing convolution calculation on the image to obtain a convolved image;
and traversing all pixel points in the convolved image, and extracting the coordinate value of which the pixel point value is 0 to obtain a two-dimensional array.
Furthermore, the method for dividing the packet into regions to obtain the position intervals comprises the following steps:
carrying out de-duplication and sequencing processing on elements in all the groups to obtain a processed array;
traversing the processed array, calculating the difference between adjacent element values, judging whether the difference value is greater than a preset threshold value, if so, recording the previous element value, and finally obtaining a position interval.
Further, determining the position of the decimal point according to the grouping and the position interval comprises the following steps:
determining the grouping where the decimal point is located, traversing the number of elements in all the groupings, and selecting the grouping with the minimum number of elements;
and determining the position of the decimal point according to the position interval of the elements in the group where the decimal point is located.
A kind of charactron decimal point position identification means, including:
the image acquisition unit is used for acquiring an image to be identified, and preprocessing the image to be identified to obtain a preprocessed image;
the image denoising unit is used for performing convolution and traversal on the preprocessed image to obtain a convolution result;
the density clustering unit is used for performing density clustering on the convolution result to obtain a plurality of groups;
the region dividing unit is used for performing region division on the groups to obtain position intervals;
and the position identification unit is used for determining the position of the decimal point according to the grouping and the position interval.
Further, the image acquisition unit includes:
the graying processing module is used for performing graying processing on the image to be identified to obtain a grayscale image;
and the binarization processing module is used for carrying out binarization processing on the gray level image, judging whether the pixel point value in the image is greater than a first preset threshold value, if so, setting the pixel point value of the point to be 1, otherwise, setting the pixel point value to be 0, and finally obtaining a binary image.
Further, the image denoising unit includes:
the convolution calculation module is used for setting a convolution kernel and carrying out convolution calculation on the image to obtain a convolved image;
and the pixel point extraction module is used for traversing all pixel points in the convolved image, extracting the coordinate value of which the pixel point value is 0, and obtaining the two-dimensional array.
Further, the area division unit includes:
the de-reordering module is used for performing de-duplication and sequencing processing on elements in all the groups to obtain a processed array;
and the judging module is used for traversing the processed array, calculating the difference between adjacent element values, judging whether the difference value is greater than a preset threshold value, if so, recording the previous element value, and finally obtaining a position interval.
Further, the location identifying unit includes:
the grouping determination module is used for determining the grouping where the decimal point is located, traversing the number of elements in all the groupings, and selecting the grouping with the minimum number of elements;
and the position determining module is used for determining the position of the decimal point according to the position interval of the elements in the group where the decimal point is located.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that:
the three-channel image is converted into the single channel by preprocessing the image to be recognized, so that the data calculation amount of subsequent decimal point recognition is reduced; the binary image is subjected to convolution processing, so that noise points which are possibly wrongly identified as decimal points are eliminated, adhesion points among numbers are removed, and the accuracy of decimal point position identification is improved; the method for identifying the decimal point position does not need to carry out area division or train an identification model in advance, has strong adaptability to decimal point identification of different types of nixie tubes, requires small calculation amount, has low requirement on the calculation capacity of hardware, and reduces the required cost.
Drawings
FIG. 1 is a schematic diagram of an application scenario of a method for identifying decimal point positions of a nixie tube in an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for identifying decimal point positions of a nixie tube according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating image preprocessing according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a grayscale image according to an embodiment of the present invention;
FIG. 5 is a diagram of a binary image according to an embodiment of the present invention;
FIG. 6 is a schematic flow chart of image denoising according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a denoised binary image according to an embodiment of the present invention;
FIG. 8 is a flow chart illustrating region partitioning according to an embodiment of the present invention;
FIG. 9 is a flow chart illustrating location identification according to an embodiment of the present invention;
FIG. 10 is a block diagram of a device for identifying decimal point of a nixie tube according to an embodiment of the present invention;
FIG. 11 is a block diagram of an image capture unit according to an embodiment of the present invention;
FIG. 12 is a block diagram of an image denoising unit according to an embodiment of the present invention;
fig. 13 is a block diagram of a structure of a region dividing unit in the embodiment of the present invention;
fig. 14 is a block diagram of a location identification unit according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below with reference to the drawings and specific examples.
As shown in fig. 1, this embodiment provides a method for identifying a decimal point position of a nixie tube, which is applied to a system for identifying a decimal point position, where the system includes a terminal 101 and a server 102, the terminal 101 and the server 102 are connected via a network, the terminal 101 may be a device having functions of shooting and connecting to the network, such as a computer, a mobile phone, etc., and the server 102 may be an independent server or a server cluster formed by multiple servers. The terminal 101 may send data to the server 102 through a network, where the data may be pictures or video streams, and if the data acquired by the server is a video stream, the acquired video stream is split first, and in this embodiment, the data may be set to be captured once per second, that is, one picture is acquired per second, and the split data is divided into a plurality of frames of pictures; if the data acquired by the server is a picture, the splitting process is not required.
The embodiment is mainly exemplified by applying the decimal point position identification method to the terminal 101 and the server 102 in fig. 1, as shown in fig. 2, the method specifically includes the following steps:
and S100, acquiring an image to be identified, and preprocessing the image to be identified to obtain a preprocessed image.
Specifically, in this embodiment, the picture to be recognized may be an RGB or BGR image, and as shown in fig. 3, the preprocessing the picture to be recognized specifically includes the following steps:
and step S101, carrying out graying processing on the image to be recognized to obtain a grayscale image.
Specifically, in this embodiment, an RGB image is taken as an example, the RGB color mode uses an RGB model to assign an intensity value in a range of 0 to 255 to an RGB component of each pixel in the image, and if R is G is B, the color represents a gray color, where the value of R is G is B is called a gray value, so that each pixel of the gray image only needs one byte to store the gray value, and the gray range is 0 to 255. The graying processing of the image includes a component method, a maximum value method, an average value method and a weighted average method, the embodiment uses the weighted average method to process the image, and the weighted average method carries out weighted average on the three components with different weights according to importance and other indexes. Because human eyes have highest sensitivity to green and lowest sensitivity to blue, the weighted average of the RGB three components is carried out according to the following formula to obtain a more reasonable gray image.
Gray(i，j)＝0.299*R(i，j)+0.578*G(i，j)+0.114*B(i，j)
The grayed image is changed from three channels to a single channel, so that the calculation amount of subsequent decimal point position identification is reduced. In this embodiment, after performing a graying process on the image to be recognized, a grayscale image as shown in fig. 4 is obtained, where the size of the grayscale image is (h, w), where h is the pixel height of the image, w is the pixel width of the image, and the image size (h, w) in this embodiment is (33, 88).
And S102, carrying out binarization processing on the gray level image, judging whether the pixel point value in the image is larger than a first preset threshold value, if so, setting the pixel point value of the point to be 1, otherwise, setting the pixel point value to be 0, and finally obtaining a binary image.
Specifically, the binarization of the grayscale image utilizes the difference between the target and the background in the image, divides the data of the image into pixel groups larger than and smaller than a first preset threshold value through the first preset threshold value, sets the pixel value of the pixel group larger than the first preset threshold value as white, and sets the pixel value of the pixel group smaller than the first preset threshold value as black, so as to determine whether a certain pixel is the target or the background, thereby obtaining the binarized image. In this embodiment, a first preset threshold is set to 130, all pixel points in the grayscale image are traversed, whether the pixel point value is greater than 130 is determined, if so, the pixel point value of the point is set to 1, otherwise, the pixel point value is set to 0, and a 33 × 88 binary image shown in fig. 5 is obtained, where the element value in a gray area is 0, and the values of other areas are 1 and correspond to the pixel point value, and then it can be seen that besides the number and the decimal point, there are some isolated pixel points with a small area in the image, which are noise points in image identification, and may be erroneously identified as decimal points, which affect the accuracy of decimal point identification, and therefore, the noise points need to be filtered.
And S200, performing convolution calculation on the preprocessed image to obtain a convolution result.
As shown in fig. 6, the convolution calculation of the preprocessed image specifically includes the following steps:
step S201, a convolution kernel is set, and convolution calculation is carried out on the image to obtain a convolved image.
Specifically, when the convolution kernel is used for image processing, given an input image, pixels in a small region of the input image become each corresponding pixel in an output image after weighted averaging, wherein a weight is defined by a function, and the function is called as the convolution kernel. When a two-dimensional digital image is convoluted, a source image is used as input source data, an image required after processing is a convolution output result, and a convolution kernel is used as a Filter to carry out convolution operation on each pixel point of the source image in XY two directions. If a two-dimensional convolution operation function C (u, v) is assumed, an output image g (x, y) ═ f (x, y) × C (u, v) is generated, and the convolution operation is performed on the image, so that sharp noise of the image can be eliminated, and functions such as smoothing and blurring of the image can be realized.
The convolution kernel can select the size according to a specific image, in the embodiment, 2 × 2 convolution kernels [1, 1], [1, 1] are set to denoise a newly generated image, and after the convolution kernels are multiplied by pixel points in a binary image, an image as shown in fig. 7 is obtained. Meanwhile, since the pixel points in the pixel area are extracted in the subsequent steps to form an array, and only the array is calculated, the calculation amount of the subsequent decimal point identification calculation can be reduced by reducing the number and the decimal point of the pixel area, and the calculation speed is improved.
Step S202, traversing all pixel points in the convolved image, and extracting a coordinate value with the pixel point value being 0 to obtain a two-dimensional array.
The pixel coordinate values include an abscissa value x and an ordinate value y, and the obtained two-dimensional array is a final result of convolution processing, as shown in table 1:
TABLE 1
x
y
8
24
8
25
8
26
9
21
…
…
85
9
85
10
85
11
85
12
And step S300, performing density clustering on the convolution result to obtain a plurality of groups.
Specifically, after convolution processing is performed on a picture, data after convolution processing, namely a two-dimensional array, is obtained, density clustering is performed on the two-dimensional array, the purpose of clustering is to divide different pixel points into different clusters according to similarity and dissimilarity of the different pixel points, the obtained clusters are subsets after data division, the obtained clusters are the groups, and clustering ensures that data in each cluster are similar as much as possible and data in different clusters are different as much as possible, so that characters in an image can be divided by a clustering method, and compared with other clustering methods, clusters (Label) of various shapes and sizes can be found in noisy data by a density-based clustering method (DBSCAN). The DBSCAN algorithm is controlled mainly by two parameters, the maximum radius of the neighborhood (EPS) and the minimum number of points in the neighborhood (MinPts), respectively.
In this embodiment, when density clustering is performed on the array obtained in step S200, the distance parameter EPS is set to 1.5, the number MinPts of the element in the minimum cluster is 3, the clustering result obtains 17 clusters as shown in table 2, each cluster includes a plurality of elements, one element is a pixel, the number of the elements is the number of pixels of the image, each element includes an abscissa value x and an ordinate value y of the pixel and a cluster number Label where the element is located, each cluster corresponds to a region with gray scale, and the region may be a number or a decimal point.
TABLE 2
x
y
Label
8
24
0
8
25
0
8
26
0
9
21
0
…
…
85
9
16
85
10
16
85
11
16
85
12
16
And step S400, carrying out region division on the packets to obtain position intervals.
As shown in fig. 8, performing area division on the packet to obtain a location interval specifically includes the following steps:
and S401, performing deduplication and sorting processing on the elements in all the groups to obtain a processed array.
Specifically, in this embodiment, the duplication removal is performed on the abscissa values x of all the grouped pixels, and the duplicated abscissa values x are sorted from small to large to obtain an array:
[8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,69,70,71,72,73,74,75,76,77,7879,80,81,82,83,84,85]。
step S402, traversing the processed array, calculating the difference between adjacent element values, judging whether the difference is greater than a preset threshold value, if so, recording the previous element value, and finally obtaining a position interval.
Specifically, the preset threshold is used for performing area division on the processed array, if the difference between adjacent element values of the array is greater than the preset threshold, it is indicated that a blank area exists between the two elements, at this time, the previous element value of the two elements is recorded, and the element value is the abscissa of the last pixel point of each number.
The preset threshold value can be set according to a specific nixie tube image, if the number displayed by the nixie tube image is large and the distance between the numbers is wide, the numerical value of the preset threshold value can be increased, and the situation that the number is mistakenly identified into two numbers due to blank areas in the number in the convolution processing is prevented; if the number displayed by the image of the nixie tube is small and the number is compact, the number of the preset threshold value can be reduced, and the blank area between the numbers can be identified. In this embodiment, the preset threshold is set to 1, and finally an array [26,45,65] is obtained, which is a position interval.
It should be noted that, in the embodiment, the preset threshold is a value determined according to a series of empirical data, and may be set manually or generated automatically by a device, which is not limited herein.
And step S500, determining the position of the decimal point according to the grouping and the position interval.
As shown in fig. 9, determining the position of the decimal point according to the grouping and the position interval specifically includes the following steps:
step S501, determining the grouping where the decimal point is located, traversing the number of elements in all the groupings, and selecting the grouping with the minimum number of elements.
Specifically, for the result of density clustering, the number of elements in each group represents the size of each region, and for the nixie tube image from which the noise point is excluded, the decimal point is much smaller than the display size of the number, so that only the group with the minimum number of elements obtained by density clustering needs to be judged, and the position of the group is the position of the decimal point.
And step S502, determining the position of the decimal point according to the position interval of the elements in the group where the decimal point is located.
Specifically, after determining the group represented by the decimal point, the position of the group in the image needs to be further determined, and the position is the position of the group relative to other groups. In this embodiment, the array obtained in step S400 is [26,45,65], the intervals are 0 to 26, 27 to 45, 46 to 65, respectively, the group with the smallest number of element values includes a plurality of pixel points, and the interval where the abscissa of the pixel points is located is determined, which is the position interval where the decimal point is located. The horizontal coordinates of the pixel points in the decimal point group are 24 and 25 respectively and are located in the interval of 0-26, so that the decimal point of the nixie tube image is located between the first number and the second number.
The embodiments of the invention also provide a device for identifying the decimal point position of the nixie tube, which corresponds to the methods for identifying the decimal point position of the nixie tube provided in the above embodiments.
As shown in fig. 10, a device for identifying decimal point position of nixie tube includes:
an image obtaining unit 1010, configured to obtain an image to be identified, and perform preprocessing on the image to be identified to obtain a preprocessed image;
an image denoising unit 1020, configured to perform convolution and traversal on the preprocessed image to obtain a convolution result;
a density clustering unit 1030, configured to perform density clustering on the convolution result to obtain a plurality of groups;
the region dividing unit 1040 is configured to perform region division on the packet to obtain a position interval;
and a position identifying unit 1050 for determining the position of the decimal point according to the grouping and the position interval.
As shown in fig. 11, the image acquisition unit 1010 includes:
the graying processing module 1011 is used for performing graying processing on the image to be identified to obtain a grayscale image;
a binarization processing module 1012, configured to perform binarization processing on the grayscale image, determine whether a pixel point value in the image is greater than a first preset threshold, if so, set the pixel point value of the point to 1, otherwise, set the pixel point value to 0, and finally obtain a binary image.
As shown in fig. 12, the image denoising unit 1020 includes:
a convolution calculation module 1021, configured to set a convolution kernel, and perform convolution calculation on the image to obtain a convolved image;
the pixel point extracting module 1022 is configured to traverse all pixel points in the convolved image, extract a coordinate value of which a pixel point value is 0, and obtain a two-dimensional array.
As shown in fig. 13, the area dividing unit 1040 includes:
a de-reordering module 1041, configured to perform de-duplication and ordering processing on elements in all the groups to obtain a processed array;
the determining module 1042 is configured to traverse the processed array, calculate a difference between adjacent element values, determine whether the difference is greater than a preset threshold, and if so, record a previous element value to finally obtain a position interval.
As shown in fig. 14, the position recognition unit 1050 includes:
a grouping determination module 1051, configured to determine a grouping where the decimal point is located, traverse the number of elements in all the groupings, and select a grouping with the smallest number of elements;
a position determining module 1052, configured to determine a decimal point position according to a position interval where an element in a group where the decimal point is located.
It should be noted that, when the apparatus provided in the foregoing embodiment implements the functions thereof, only the division of the functional modules is illustrated, and in practical applications, the functions may be distributed by different functional modules according to needs, that is, the internal structure of the apparatus may be divided into different functional modules to implement all or part of the functions described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The invention and its embodiments have been described above schematically, without limitation, and the invention can be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The representation in the drawings is only one of the embodiments of the invention, the actual construction is not limited thereto, and any reference signs in the claims shall not limit the claims concerned. Therefore, if a person skilled in the art receives the teachings of the present invention, without inventive design, a similar structure and an embodiment to the above technical solution should be covered by the protection scope of the present patent. Furthermore, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Several of the elements recited in the product claims may also be implemented by one element in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
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