WO2000075869A1 - Procede de traitement d'image - Google Patents

Procede de traitement d'image Download PDF

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Publication number
WO2000075869A1
WO2000075869A1 PCT/JP2000/003641 JP0003641W WO0075869A1 WO 2000075869 A1 WO2000075869 A1 WO 2000075869A1 JP 0003641 W JP0003641 W JP 0003641W WO 0075869 A1 WO0075869 A1 WO 0075869A1
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WO
WIPO (PCT)
Prior art keywords
image
function
contour
approximation
image processing
Prior art date
Application number
PCT/JP2000/003641
Other languages
English (en)
Japanese (ja)
Inventor
Kazuo Toraichi
Kouichi Wada
Kouichi Mori
Original Assignee
Fluency Research & Development Co., Ltd.
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Fluency Research & Development Co., Ltd. filed Critical Fluency Research & Development Co., Ltd.
Publication of WO2000075869A1 publication Critical patent/WO2000075869A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/40—Filling a planar surface by adding surface attributes, e.g. colour or texture

Definitions

  • the present invention relates to an image processing method for converting an image printed on paper or the like into a high-definition digital image, and storing, displaying, and transmitting the image.
  • the change in the resolution of the output device is not taken into account, but the resolution of the output device actually changes depending on the usage.
  • the transmitted document will be viewed on the receiving side by enlarging or reducing it, or that output will be suitable for devices with various resolutions, from low-resolution displays to high-resolution printers.
  • separate image files for each resolution will make it difficult to manage the data overnight, and saving at the highest possible resolution at any time will waste resources. there were.
  • Considering the features of document images most of document images are different from natural images, It can be expressed with a small number of colors, and the boundaries of colors are clear. Therefore, an image can be perceived as a set of closed contours in each color gamut.
  • the present invention has been made in view of the above points, and an object of the present invention is to provide an image processing method capable of converting a content of a paper document into a high-definition digital image for display and printing. Is to do.
  • a content drawn on a paper document is read to create a binarized pixel image
  • a contour point sequence included in the pixel image Is extracted in a second step, a contour point sequence included in the pixel image Is extracted
  • the function of image data is generated by approximating the shape of the contour point sequence by a function.
  • an approximate function is obtained based on the functionalized image data, and a closed contour of the color gamut is generated using the approximate function.
  • Painting is performed to generate a drawing data
  • display processing or printing processing is performed based on the drawing data. Since the contours are approximated by functions, high-resolution digital images can be generated for display and printing.
  • the generation of the closed contour in the fourth step described above is performed by generating a sequence of contour points at intervals corresponding to the resolution of the display processing and the print processing in the sixth step. Since the contour point sequence is generated at intervals according to the resolution of the display screen and the resolution of printing, a high-definition digital image suitable for display and printing can be obtained.
  • the function approximation in the third step be performed by dividing the contour point sequence extracted in the second step into a plurality of pieces and determining an optimum approximation function for each of the divided portions.
  • the division position of the contour point sequence be determined by an optimization method based on dynamic programming. Since the optimal function approximation is performed for each divided part, a more accurate function approximation is possible.
  • FIG. 1 is a diagram for explaining an outline of an image processing system according to an embodiment to which the image processing method of the present invention is applied,
  • FIG. 2 is a diagram illustrating a function approximation expression of a contour
  • FIG. 3 is a diagram for explaining the section division of the contour line and the approximation function determination process.
  • FIG. 5 is a diagram illustrating a contour reconstruction method in an arc section
  • FIG. 6 is a diagram illustrating a contour reconstruction method in a free curve section
  • FIG. 7 is a diagram illustrating an experimental system
  • Figure 8 shows a document image (CCITT test image or part of it) prepared to evaluate the performance of the implemented encoder and view II.
  • Figure 9 shows the statements provided to evaluate the performance of the implemented encoder and view II.
  • Figure 9 shows the document image (CCITT test image or part of it)
  • Figure 10 is a diagram showing a document image (paper document scanned by a scanner) prepared to evaluate the performance of the implemented encoder and view II.
  • Fig. 11 shows the result of comparing the size of the functionalized image file output with the images shown in Figs. 8 to 10 as input and the size of the GIF file of the same image.
  • a diagram showing an example of an execution screen using a Java application
  • FIG. 13 is a diagram showing an example of an execution screen using the Java application
  • FIG. 14 is a diagram showing an example of displaying on a web browser using the Ja Va applet
  • Figure 15 shows an enlarged view of the original pixel image.
  • FIG. 16 is a diagram showing experimental results regarding the processing speed. BEST MODE FOR CARRYING OUT THE INVENTION
  • an image processing system based on these specifications is provided.
  • the image processing system according to the present embodiment is roughly divided into an encoder that outputs functionalized image data by inputting the contents drawn in a paper document and a document image that is reconstructed from image data and displayed. It consists of two parts (see Figure 1). Use an encoder and viewer to operate on a personalized system, and transmit image files using network services such as mail services and web services to display and print images on a remote system. It is assumed that the usage is simple.
  • a document or figure drawn on a paper document is captured by a scanner or a digital camera, and a binarized pixel image is used as an input image. Encoder Then, the edge of a black pixel is traced to obtain a sequence of closed contour points. Function approximation is performed for all closed contour points, and functionalized image data is output based on the information of the approximation function. In View II, an approximate function is obtained from the image of the functionalized image, and a closed contour of the color region is generated using the approximate function. Display 'To print, fill all closed areas and output.
  • the document image which is the target image, has a clear outline, and if it is binarized and its edges are followed, a closed outline can be easily obtained.
  • the obtained contour point sequence is discrete, but if it is approximated by a continuous function, it is possible to output a contour line at any resolution.
  • all of one round of the closed contour line is approximated by a combination of straight lines or a spline function (Bézier function).
  • the line is divided and each section is approximated using the optimal function.
  • appropriate approximation can be performed for the approximated section, so that the approximate data can be reduced.
  • the contour is represented by a combination of a straight line, a quadratic B-spline curve, and an arc.
  • the contour is divided into three sections, which are approximated by B-splines, arcs, and straight lines, respectively.
  • the contour can be approximated using multiple classes of continuous functions as described above.
  • the approximation function for the relevant section is not appropriate, the data required for approximation will increase.Therefore, the process of segmentation and determination of the approximation function is an important process that greatly affects the approximation accuracy and the amount of data. . However, if humans give hints on the division of contour lines, the required automatic processing cannot be achieved. In many existing methods, extraction of angles using curvature, etc., determination of approximation functions, determination of approximate approximation functions using local fitting, and segmentation and post-processing are performed in appropriate sections. You are trying to achieve a split.
  • DP Dynamic Programming
  • the fitting error is determined between the approximation function and the divided section, and the obtained error value is added for each divided section to determine the optimal fitting state for the entire contour line.
  • this is i i 0, ii, ⁇ i h , ⁇ ⁇
  • d (i h - 1; i h ) be the cost of approximating with a function, and express the overall cost as the sum of the approximate costs of the intervals.
  • n is the minimum interval length.
  • Fig. 3 shows a case where the above process is divided into three by appropriate two points.
  • the approximation cost function for each section is set not only to reduce the approximation error, but also to make the approximation section as long as possible. If the only purpose is to reduce the approximation error, it is conceivable to use the fitting error as it is as a cost function. However, the error can be reduced by increasing the coefficient of the spline curve, so if you judge only by the fitting error, all sections will be free curve sections. Therefore, in the image processing system of the present embodiment, a straight line and a circular arc are preferentially determined, and a cost function is set such that a section that is neither of them is a free curve section. The cost function used here is shown in equation (2) below.
  • ⁇ is the mean square error of the partial point sequence with the approximate circle. If the end point of the section to be approximated is symmetrically located on the X-axis as shown in Fig. 4 (b), the center coordinates (b, 0) of the approximate circle can be obtained by the following equation. Where ⁇ arc 2 is the distance from the center of the arc to the contour point minus the radius,
  • the cost of a section is calculated based on the approximation error and the section length in that section, and is calculated independently of the state of the preceding and following sections. Therefore, this optimization calculation can be performed efficiently using DP.
  • the feature of using DP is that if the optimal interval division up to H is calculated, the optimal division below H is also calculated at the same time.
  • the maximum value of H is the largest integer less than or equal to LZ 1 m: n because the minimum length of the section is l min .
  • the cost functions for each H are compared, and the H with the least cost is selected, and the data is output using the interval division and the approximation function at that time.
  • the interval division and the determination of the approximation function can be performed theoretically based on the optimization method, but the DP calculation amount increases in proportion to the square of the length. I get it.
  • the amount of calculation for fitting increases in proportion to the length, it is practically problematic to apply DP as it is when the contour is long.
  • long contour lines are processed separately.
  • the structure of the functionalized image file and the playback / display method will be described.
  • the outline point sequence is divided into sections by the above-described method, and the class of the function that approximates each section is determined.
  • the minimum data that can reproduce the approximate contour line based on the information of the approximate function should be stored.
  • the number of contour lines constituting the image is required.
  • information on each contour includes information on the entire contour and information on an approximation function for each approximation section.
  • Information on the entire contour requires the color of the area, the number of sections, and the start point coordinates.
  • Information about the approximation function for each approximation interval differs depending on the class of the approximation function, and the following information is stored for each.
  • the start point is the end point of the previous approximation section, so only the end point coordinates of the straight line are needed.
  • start point end point
  • center coordinates and drawing direction (clockwise or counterclockwise)
  • drawing direction clockwise or counterclockwise)
  • start point is the end point of the previous section just like a straight section, so there is no need to save it.
  • the coordinates, end point, and drawing direction flag may be stored.
  • the center coordinates are known from the fitting result, so this is used.
  • the free curve section it is approximated by the B-spline function.
  • the number of nodes In order to approximate with the B-spline function, the number of nodes must be determined. However, since this cannot be determined analytically, fitting is performed stepwise, and the error is within an allowable range (here, within 1 pixel). To find the minimum number of nodes. As the data, the spline coefficient at the time of fitting to obtain the number of nodes may be stored. Same as the score.
  • the end point may be added as the vertex of the polygon.
  • the center and the end point are known from the data, so the start point is added to this, and the radius, start angle, and end angle of the arc are calculated from that, and the point sequence on the arc is calculated.
  • the density of contour points can be changed by adjusting the step angle (see Fig. 5).
  • the approximate contour is obtained by calculating the convolution of the spline coefficient and the B spline in the X and y directions, respectively.
  • contour points with different densities can be constructed (see Figure 6).
  • Figures 8 (a), 8 (b), and 9 are CCITT test images or parts of them
  • Figures 10 (a) and 10 (b) are paper documents scanned by a scanner.
  • Each image is a black and white binary image with a resolution of about 300 dpi.
  • FIG. 12 to FIG. 15 are diagrams showing comparison results between the example of the embodiment screen and the pixel images.
  • Figs. 12 and 13 are screen examples of implementation using the Java application
  • Fig. 14 is an example of display using the Java applet.
  • FIG. 15 is an enlarged view of the original pixel image for comparison with the image processing system of the present embodiment. From FIGS. 12 to 15, it can be seen that in the image processing system of the present embodiment, both the reduced image and the enlarged image are reproduced with high accuracy.
  • FIG. 16 is a diagram showing experimental results regarding the processing speed.
  • the experimental results shown in Fig. 16 measured the time required to read the function image file and reconstruct the contours, and the time required to display all the contours.
  • the time required for the contour reconstruction processing is 0.16 seconds to 1.47 seconds, indicating that the processing is performed at a very high speed.
  • the drawing time is between 0.04 and 1.32 seconds. It can be said that the responsiveness of the application is sufficient at such a rendering speed.
  • the entire image is displayed. However, when the image is enlarged, the number of unnecessary contours that do not appear on the screen increases, and the drawing time is greatly reduced.
  • JaVa2 runs on a virtual machine and is inferior in execution speed compared to the native execution environment.
  • the graphic library used this time is a standard one, so it is not optimized for the latest graphic model. Nevertheless, the results of the experiment shown in Fig. 16 show that high-speed drawing is possible. If a library that takes advantage of the performance of Graphic Xerare is introduced in the future, higher-speed drawing will be achieved. Can be expected.
  • the image processing system uses a function image to generate a document image.
  • the paper document can be digitized with high definition.
  • the contour is approximated by a continuous function of a plurality of classes, it is possible to output a single image file at an arbitrary resolution.
  • the functionalized image file used in the present embodiment is configured by extracting the minimum data necessary for reproduction by approximating each contour part with an optimal function, so that the file size is small.
  • the playback and display are very fast. Industrial applicability

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé de traitement d'image permettant de convertir le contenu d'un document papier en image numérique haute définition puis d'afficher et d'imprimer l'image. Après qu'un dispositif de balayage ou une caméra numérique a saisi un document papier et envoyé une image à pixels binarisés à un codeur, le codeur trace un bord de pixel noir, forme des suites de points de contour fermés, soumet toutes les suites de points de contour fermés à une approximation de fonction et émet des données d'image à conversion de fonction sur la base des informations provenant d'une fonction d'approximation. L'utilisateur détermine une fonction d'approximation à partir des données d'image à conversion de fonction et génère des contours fermés d'une zone de couleur en utilisant la fonction d'approximation. L'utilisateur remplit toutes les zones fermées avec des couleurs pour produire l'image destinée à un affichage/impression.
PCT/JP2000/003641 1999-06-03 2000-06-05 Procede de traitement d'image WO2000075869A1 (fr)

Applications Claiming Priority (2)

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JP15717199 1999-06-03
JP11/157171 1999-06-03

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WO2000075869A1 true WO2000075869A1 (fr) 2000-12-14

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002035472A2 (fr) * 2000-10-24 2002-05-02 Starlab Nv/Sa Creation de dessins animes
CN101192307B (zh) * 2006-11-17 2012-05-23 鸿富锦精密工业（深圳）有限公司 点云三角网格面构建方法
US9453716B2 (en) 2010-10-22 2016-09-27 Makino Milling Machine Co., Ltd. Method of measurement and apparatus for measurement of tool dimensions

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH03234559A (ja) * 1990-02-09 1991-10-18 Fuji Xerox Co Ltd パターン変形方式
JPH05282440A (ja) * 1992-03-30 1993-10-29 Alps Electric Co Ltd 位置データの近似方法
JPH0785268A (ja) * 1993-06-30 1995-03-31 Kazuo Toraichi 文字図形の通信装置

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH03234559A (ja) * 1990-02-09 1991-10-18 Fuji Xerox Co Ltd パターン変形方式
JPH05282440A (ja) * 1992-03-30 1993-10-29 Alps Electric Co Ltd 位置データの近似方法
JPH0785268A (ja) * 1993-06-30 1995-03-31 Kazuo Toraichi 文字図形の通信装置

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
KOUICHI HATA ET AL.: "Fukuzatsuna rinkaku no jidou chuushitsu tsuisekihou", TRANSACTIONS ON THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS, vol. J81-D-II, no. 4, April 1998 (1998-04-01), (JAPAN), pages 706 - 715, XP002945887 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2002035472A2 (fr) * 2000-10-24 2002-05-02 Starlab Nv/Sa Creation de dessins animes
WO2002035472A3 (fr) * 2000-10-24 2002-11-21 Starlab Nv Sa Creation de dessins animes
CN101192307B (zh) * 2006-11-17 2012-05-23 鸿富锦精密工业（深圳）有限公司 点云三角网格面构建方法
US9453716B2 (en) 2010-10-22 2016-09-27 Makino Milling Machine Co., Ltd. Method of measurement and apparatus for measurement of tool dimensions

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