CN111179289B - Image segmentation method suitable for webpage length graph and width graph - Google Patents
Image segmentation method suitable for webpage length graph and width graph Download PDFInfo
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Abstract
The invention relates to an image segmentation method suitable for a webpage length chart and a webpage width chart, and belongs to the field of image processing. The method comprises the following steps: automatically filling the image boundary in an extrapolation mode, carrying out gray processing on the image, carrying out binary operation of gradient edge extraction and hysteresis threshold of the image based on a Canny operator after filtering processing, carrying out morphological processing of closing, corrosion and expansion, finally carrying out contour extraction and correction, and finally filtering and de-weighting according to a certain scale rule, and outputting the segmented image; from the aspect of algorithm complexity, the invention has no complex model front training process, does not need to allocate massive system resources to weight parameters of a convolution network model, completes image segmentation of a webpage length-width diagram scene under limited CPU occupancy rate, does not need to call GPU resources, and accords with the three principles of simplicity, rapidness and effectiveness of image preprocessing.
Description
Technical Field
The invention belongs to the field of image processing, and relates to an image segmentation method suitable for a webpage length map and a webpage width map.
Background
Image segmentation is an important branch of image preprocessing technology in the field of computer vision, and is also an indispensable image preprocessing technology in a large-scale image retrieval technology based on content. The excellent image segmentation effect has a decisive influence on the implementation result of project engineering in the field of computer vision.
The existing image segmentation technology is mainly divided into traditional image segmentation based on image attributes (such as gray threshold segmentation, edge gradient segmentation, straight square method segmentation and the like) and image segmentation based on specific theory (feature clustering segmentation, graph theory based segmentation, wavelet transformation segmentation and the like), and image semantic segmentation based on deep learning by utilizing strong nonlinear capability of a neural network and achieving good effect in the field of computer vision in recent years.
Although image segmentation algorithms have been studied for a long time, the above related art is mostly at the semantic level for a single normal-size image. Aiming at oversized, complex and dense plates and text images of a webpage length-graph-width graph, the traditional processing method based on the image segmentation method cannot obtain efficient, rapid and good image segmentation effect, heavy graph and missing graph can be caused, the complexity of a semantic segmentation algorithm based on deep learning is greatly improved, so that the engineering process of a project system is time-consuming, occupied memory and GPU (graphics processing unit) is increased, the three principles of simplicity, rapidness and effectiveness of image preprocessing are violated, and the existing image semantic segmentation algorithm based on deep learning cannot meet the image segmentation applicable to the webpage size of the length-graph-width graph at present, and has insufficient segmentation effect and robustness.
Disclosure of Invention
Accordingly, the present invention is directed to an image segmentation method suitable for web page length, width and image.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an image segmentation method suitable for a webpage length graph and a webpage width graph, which comprises the following steps: automatically filling the image boundary in an extrapolation mode, carrying out gray processing on the image, carrying out binary operation of gradient edge extraction and hysteresis threshold of the image based on a Canny operator after filtering processing, carrying out morphological processing of closing, corrosion and expansion, finally carrying out contour extraction and correction, and finally filtering and de-weighting according to a certain scale rule, and outputting the segmented image;
the method comprises the following specific steps:
(1) Reading in and transferring the long and wide webpage images;
(2) Filling frames of the original image; giving a parameter border value, and automatically filling a boundary value of an image in an extrapolation method mode by taking the parameter border value as a constant pixel value, so that a picture close to the edge of a webpage can be identified in subsequent contour detection;
(3) Carrying out gray processing on the multichannel picture, converting BGR three-color space of the picture into gray space, and outputting the converted picture with a single channel;
(4) Performing binary operation of extracting gradient edges of the image and hysteresis threshold values on the gray level image based on a Canny operator;
(5) Morphological processing is carried out, and closed operation, corrosion and expansion processing are carried out by establishing an elliptic kernel function kernel, so that the image boundary is clear;
(6) Performing first contour generation, establishing a contour of a hierarchical tree structure, compressing contour information in the horizontal direction, the vertical direction and the diagonal direction, and only reserving end point coordinates of the direction, namely, only 4 points of a rectangular contour for storage; according to the first contour information, performing suppression processing on the contour region of the binarized picture with a specific size, namely performing suppression zero setting operation on the repeated coverage region of the specific contour in the binarized picture; and generating the contour again; removing very regular size contours with overlarge height, overlarge width, overlarge small width and the like to finish contour correction;
(7) Solving an inscription matrix of each contour, determining the range of the inscription matrix of the contour in the original image, and storing and outputting each contour region picture to complete image segmentation.
Optionally, the step (4) specifically includes:
firstly, filtering noise in a graph by using a 5x5 Gaussian filter, and calculating first derivatives in the horizontal and vertical directions by using a sobel operator; secondly, carrying out non-maximum suppression on derivative values of the image, detecting whether the derivative values are local maximum values in the field of gradient directions at each pixel, otherwise, carrying out zero suppression processing to solve and obtain image gradient values based on a canny operator; finally, binarizing the hysteresis threshold, setting parameters of an upper limit maxVal and a lower limit minVal of the hysteresis threshold, and determining edges through the parameters maxVal and minVal: pixel gradient > maxVal must be edge, reserved; pixel gradient < minVal must be non-edge, truncated; if the connection is between the two, the connection is reserved, and if the connection is not reserved, the connection is abandoned.
The invention has the beneficial effects that:
the method comprises the steps of setting up dense webpage and complex content in a page length diagram and width diagram scene, outputting segmentation sub-diagram standard without repeated diagram and missing diagram, and filtering and eliminating text information and non-target unusual contour pictures in the original webpage.
Compared with the image semantic segmentation technology based on deep learning, the image segmentation technology provided by the invention has no complex model pre-training process, does not need to allocate massive system resources to weight parameters of a convolution network model (a convolution layer, a pooling layer and a full connection layer), completes image segmentation of a webpage length-width image scene under the limited CPU occupancy rate, does not need to call GPU resources, and accords with the three principles of simplicity, rapidness and effectiveness of image preprocessing.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow of image segmentation for web page length and width graphs;
FIG. 2 is a flowchart of channel edge detection and binarization;
FIG. 3 is an exemplary diagram of a binarized boundary for a hysteresis threshold;
fig. 4 is a flow of contour extraction and correction.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
Wherein the drawings are for illustrative purposes only and are shown in schematic, non-physical, and not intended to limit the invention; for the purpose of better illustrating embodiments of the invention, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the size of the actual product; it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numbers in the drawings of embodiments of the invention correspond to the same or similar components; in the description of the present invention, it should be understood that, if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc., that indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, it is only for convenience of describing the present invention and simplifying the description, but not for indicating or suggesting that the referred device or element must have a specific azimuth, be constructed and operated in a specific azimuth, so that the terms describing the positional relationship in the drawings are merely for exemplary illustration and should not be construed as limiting the present invention, and that the specific meaning of the above terms may be understood by those of ordinary skill in the art according to the specific circumstances.
The implementation mode of the invention is as follows: and (3) automatically filling the image boundary in an extrapolation mode, carrying out gray processing on the image, carrying out binary operation of gradient edge extraction and hysteresis threshold of the image based on a Canny operator after filtering processing, carrying out morphological processing of closing, corrosion and expansion, finally carrying out contour extraction and correction, and finally filtering and de-weighting according to a certain scale rule, and outputting the segmented image.
The method mainly comprises the following steps:
1. and reading in and transferring the long and wide webpage images.
2. And filling the frame of the original image. Given the parameter border value, the boundary value of the image is automatically filled in an extrapolation manner as a constant pixel value, so that the picture close to the edge of the webpage can be identified in subsequent contour detection.
3. And carrying out graying treatment on the multichannel picture, converting the BGR three-color space of the picture into a gray space, and outputting the converted picture with a single channel.
4. And carrying out binarization operation of gradient edge extraction and hysteresis threshold of the image on the gray level image based on the Canny operator. First, noise in the graph is filtered using a 5×5 gaussian filter, and the first derivatives in the horizontal and vertical directions are calculated using a sobel operator. Secondly, carrying out non-maximum suppression on derivative values of the image, detecting whether the derivative values are local maximum values in the field of gradient directions at each pixel, otherwise, carrying out zero suppression processing to solve and obtain image gradient values based on a canny operator; finally, binarizing the hysteresis threshold, setting parameters of an upper limit maxVal and a lower limit minVal of the hysteresis threshold, and determining edges through the parameters maxVal and minVal: pixel gradient > maxVal must be edge, reserved; pixel gradient < minVal must be non-edge, truncated; if the connection is between the two, the connection is reserved, and if the connection is not reserved, the connection is abandoned.
5. Morphological processing is carried out, and closed operation, corrosion and expansion processing are carried out by establishing an elliptic kernel function kernel, so that the image boundary is clear.
6. Performing first contour generation, establishing a contour of a hierarchical tree structure, compressing contour information in the horizontal direction, the vertical direction and the diagonal direction, and only reserving end point coordinates of the direction, namely, only 4 points of a rectangular contour for storage; according to the first contour information, performing suppression processing on the contour region of the binarized picture with a specific size, namely performing suppression zero setting operation on the repeated coverage region of the specific contour in the binarized picture; and generating the contour again; and removing the outline with very regular dimensions such as overlarge height, overlarge width and overlarge small width, and completing the correction of the outline.
7. Solving an inscription matrix of each contour, determining the range of the inscription matrix of the contour in the original image, and storing and outputting each contour region picture to complete image segmentation.
The implementation process of the image segmentation method suitable for the webpage length and width map is as shown in fig. 1: the web page length and width image is read in and transferred, frame filling and grey-scale, canny edge detection and binarization, morphological processing, contour extraction and correction, then image segmentation is completed, and a picture is output.
The specific process mainly comprises the following steps:
(1) And reading and transferring the original pictures.
(2) And performing frame filling on the original image, and outputting a white frame filling image with a given pixel width.
(3) And carrying out grey processing on the original image. And converting the BGR three-color space of the picture into a gray space, and outputting the gray space as a single-channel gray map.
(4) And (3) carrying out binarization on the edge gradient detection and hysteresis threshold value based on the canny operator shown in fig. 2 on the gray level map, wherein the binarization mode of the gradient boundary is shown in fig. 3, and finally outputting the gradient boundary map.
(5) And carrying out image morphology processing on the gradient boundary map so that the boundary is more obvious, and outputting a morphology processing map.
(6) The contour extraction and correction shown in fig. 4 are performed on the morphological processing map: firstly, carrying out first contour generation, drawing a first generated contour on a frame filling map, and outputting a first contour generation map; compressing and extracting the contour information, performing contour suppression of a specific contour dimension rule, and outputting a binary image after the suppression; secondly, performing secondary contour generation on the binary image after the suppression correction, drawing the secondarily generated contour on the frame filling image, and outputting a contour generation image after the suppression; and finally, eliminating the non-specification contours, and retaining contour information with specific dimensions.
And carrying out contour segmentation on the frame filling map according to the contour information with the reserved specific size, outputting and outputting a sub-map set, and completing image segmentation of the webpage length-width map.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (1)
1. An image segmentation method suitable for a webpage length graph and a webpage width graph is characterized by comprising the following steps of: the method comprises the following steps: automatically filling the image boundary in an extrapolation mode, carrying out gray processing on the image, carrying out binary operation of gradient edge extraction and hysteresis threshold of the image based on a Canny operator after filtering processing, carrying out morphological processing of closing, corrosion and expansion, finally carrying out contour extraction and correction, and finally carrying out filtering and de-weighting according to scale rules, and outputting the segmented image;
the method comprises the following specific steps:
(1) Reading in and transferring the long and wide webpage images;
(2) Filling frames of the original image; giving a parameter border value, and automatically filling a boundary value of an image in an extrapolation method mode by taking the parameter border value as a constant pixel value, so that a picture close to the edge of a webpage can be identified in subsequent contour detection;
(3) Carrying out gray processing on the multichannel picture, converting BGR three-color space of the picture into gray space, and outputting the converted picture with a single channel;
(4) Performing binary operation of extracting gradient edges of the image and hysteresis threshold values on the gray level image based on a Canny operator;
(5) Morphological processing is carried out, and closed operation, corrosion and expansion processing are carried out by establishing an elliptic kernel function kernel, so that the image boundary is clear;
(6) Performing first contour generation, establishing a contour of a hierarchical tree structure, compressing contour information in the horizontal direction, the vertical direction and the diagonal direction, and only reserving end point coordinates in the corresponding direction, namely, only 4 points of a rectangular contour for storage; according to the first contour information, performing suppression processing on the contour region of the binarized picture with a specific size, namely performing suppression zero setting operation on the repeated coverage region of the specific contour in the binarized picture; and generating the contour again; removing the outline with the excessive height and width and the undersize irregular size to finish the correction of the outline;
(7) Solving an inscription matrix of each contour, determining the range of the inscription matrix of the contour in the original image, and storing and outputting each contour region picture to complete image segmentation;
the step (4) specifically comprises the following steps:
firstly, filtering noise in a graph by using a 5x5 Gaussian filter, and calculating first derivatives in the horizontal and vertical directions by using a sobel operator; secondly, carrying out non-maximum suppression on derivative values of the image, detecting whether the derivative values are local maximum values in the field of gradient directions at each pixel, otherwise, carrying out zero suppression processing to solve and obtain image gradient values based on a canny operator; finally, binarizing the hysteresis threshold, setting parameters of an upper limit maxVal and a lower limit minVal of the hysteresis threshold, and determining edges through the parameters maxVal and minVal: pixel gradient > maxVal must be edge, reserved; pixel gradient < minVal must be non-edge, truncated; if the connection is between the two, the connection is reserved, and if the connection is not reserved, the connection is abandoned.
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