JP2006295303A - Image processing method, apparatus and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an image processing method, apparatus and program capable of reproducing an image with higher color reproducibility by means of white balance adjustment processing on which a light source condition is reflected with high accuracy. <P>SOLUTION: A parameter acquisition section 32 acquires a weighting parameter C applied to a principal component representing an illumination condition for a face part P0f' by adapting the face part P0f' in an image P0' detected by a face detection section 31 to a mathematic model M generated by the AAM method, and a white balance adjustment section 33 acquires an adjustment amount W of white balance by referencing a reference table T on the basis of the acquired parameter C to adjust the white balance on the basis of the acquired adjustment amount W. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a method and apparatus for performing image processing for adjusting white balance of an input image, and a program for causing a computer to execute this method.

  In order to reproduce a photographic image with an ideal color, image processing for adjusting the white balance of the photographic image is performed.

For example, the captured image is divided into a plurality of regions, the colors in the regions are determined, and based on the determination result, it is determined whether there is a region determined to be a skin color. When it is determined that there is a discriminated area, it is known to adjust the white balance so that the skin color becomes a preferable color (for example, Patent Document 1). In addition, it is known that the color temperature of the photographing light source is estimated using the color information of the area determined to be the skin color, and the white balance is adjusted based on the estimated color temperature (for example, Patent Document 2). ).
JP 2003-333618 A JP 2002-152772 A

  However, in the white balance adjustment processing described in Patent Documents 1 and 2, adjustment is performed based on the average value of the skin color area. Therefore, it affects the adjustment accuracy and the estimation accuracy of the color temperature of the light source.

  The present invention has been made in view of such circumstances, and an image processing method, apparatus, and program that enable reproduction of an image with higher color reproducibility by white balance adjustment processing that reflects light source conditions with high accuracy. Is intended to provide.

  The image processing method according to the present invention includes a statistical feature amount representing a light source condition obtained by performing predetermined statistical processing on a plurality of images in which a predetermined structure is represented under different light source conditions. The structure in the input image is represented in a model representing the structure by one or more statistical features and weighting parameters for weighting the statistical features according to individual features of the structure. To obtain a weighting parameter for a statistical feature amount representing a light source condition in a structure in the input image, and based on the obtained weighting parameter value, at least the structure in the input image It is characterized by adjusting the white balance of the object area.

  An image processing apparatus according to the present invention is an apparatus that performs the image processing described above. That is, one or more statistical feature quantities including a statistical feature quantity representing a light source condition, obtained by performing a predetermined statistical process on a plurality of images representing a predetermined structure under different light source conditions. And by adapting the structure in the input image to a model that represents the structure by weighting parameters that weight the statistical features according to the individual features of the structure, Based on the value of the weighting parameter acquired by the parameter acquisition means, the parameter acquisition means for acquiring the weighting parameter for the statistical feature amount representing the light source condition in the structure in the input image, at least that in the input image White balance adjusting means for adjusting the white balance of the area of the structure is provided.

  Furthermore, an image processing program according to the present invention causes a computer to execute the above-described image processing method (functions as the above-described means).

  Next, details of the image processing method, apparatus, and program according to the present invention will be described.

  As a specific method for realizing the “model expressing the (predetermined) structure” according to the present invention, it is conceivable to use an AAM (Active Appearance Models) method. AAM is one approach for trying to interpret the contents of an image based on a model. For example, when a face is to be interpreted, the shape of the face part in a plurality of images to be learned and the shape are normalized. A mathematical model of the face is generated by performing principal component analysis on the luminance information after conversion, and the face part in the new input image is represented by each principal component in the mathematical model and the weighting parameter for each principal component. Representing and reconstructing facial images (TF Cootes, GJ Edwards, CJ Taylor, “Active Appearance Models” “In Proc. 5th European Conference on Computer Vision”, Springer, Germany, 1998, vol. 2, pp 484-498; hereinafter referred to as Reference 1).

  As a specific example of the “light source condition”, a light source color is considered. The light source color can be expressed by, for example, a color temperature. Note that the plurality of statistical processing target images and input images are preferably taken under a single and constant light source.

  A “predetermined structure” is suitable for modeling, that is, a shape or color variation in an image of the structure within a certain range, in particular, by performing statistical processing on the shape or color. It is preferable that a statistical feature amount with higher explanatory power can be obtained. Moreover, it is preferable that it is the subject part in an image. A specific example is a human face.

  The “image showing a predetermined structure under different light source conditions” may be an image obtained by actually photographing the predetermined structure under different light source conditions, or under a specific light source condition. It may be an image generated by a simulation based on a photographed image.

  The “predetermined statistical process” is preferably a dimension compression process that can compress and represent a predetermined structure into a statistical feature quantity having a smaller number of dimensions than the number of pixels representing the structure. A specific example is a multivariate analysis method such as principal component analysis. Further, when principal component analysis is performed as “predetermined statistical processing”, “statistical feature amount” means a plurality of principal components obtained by principal component analysis.

  Note that the level of explanatory power means that when the predetermined statistical process is principal component analysis, the higher principal component has higher explanatory power and the lower principal component has lower explanatory power.

  The “statistical feature amount” needs to express information based on at least the color of the structure. This is because the light source condition is expressed in the image by the color distribution.

  Further, the “statistical feature amount representing the light source condition” may be one statistical feature amount or a plurality of statistical feature amounts.

  “A (predetermined) structure in the input image” may be automatically detected or may be manually detected. In addition, the present invention may further include a process (means) for detecting the structure in the input image, or a part of the structure may be detected in advance from the input image.

  Further, a process of preparing a plurality of models in the present invention for each attribute of a predetermined structure, acquiring information representing the attribute of the structure in the input image, and selecting a model according to the acquired attribute ( The weighting parameter may be acquired by adding the means) and adapting the structure in the input image to the selected model.

  Here, for example, when the predetermined structure is a human face, the “attribute” may be sex, age, race, or the like. Moreover, the information which identifies an individual may be sufficient. In this case, the model for each attribute means a model for each individual.

  As a specific method for acquiring the “attribute”, known recognition processing (for example, described in Japanese Patent Laid-Open No. 11-175724) and estimation / acquisition from supplementary information of the image such as GPS information can be considered.

  “Adapting the structure in the input image to the model expressing the structure” means an arithmetic process or the like for expressing the structure in the input image by the model. Specifically, taking the case of using the above AAM technique as an example, it means obtaining the weighting parameter value for each principal component in the mathematical model.

  “Based on the weighting parameter (to the statistical feature amount representing the light source condition)” and “Adjust white balance” are the correspondence between the obtained weighting parameter value and the white balance adjustment amount. Is required. For example, the correspondence between the acquired weighting parameter value and the color temperature value of the light source is experimentally and statistically obtained, and the known light source color temperature and white balance adjustment amount It is obtained by combining with the correspondence of.

  The white balance adjustment process may be performed not only on the area of the structure in the input image but also on an area of the same color as the structure adjacent to the structure. Here, when the structure is a human face, for example, a region of the neck corresponds to “a region of the same color as the structure adjacent to the structure”.

  Further, when the white balance adjustment process is performed on a part of the area in the input image, the white balance adjustment is performed so that the degree (intensity) of the adjustment process decreases as the area that is not subjected to the adjustment process is approached. Processing may be performed.

  Further, white balance adjustment processing may be performed on the entire input image.

  According to the image processing method, apparatus, and program of the present invention, a predetermined structure in an image is determined by one or more statistical feature amounts including a statistical feature amount representing a light source condition and a weighting parameter for the statistical feature amount. By applying the structure in the input image to the model that expresses the weighting parameter, the weighting parameter for the statistical feature amount indicating the light source condition in the structure in the input image is obtained. Based on the value, the white balance of the region of the structure in the input image can be adjusted. As described above, in the present invention, focusing on the statistical feature amount representing the light source condition, the whiteness is set to an appropriate degree (processing intensity) according to the weighting parameter for the statistical feature amount for each structure in the input image. Since the balance is adjusted, it is possible to perform white balance adjustment processing that reflects the light source conditions with high accuracy, with less influence from elements other than the light source conditions such as shading and signal skipping, and images with higher color reproducibility. Will be able to play.

  In addition, when white balance adjustment processing is performed on the same color area as the structure adjacent to the structure, color reproduction between the structure and the adjacent area is performed. The above uncomfortable feeling can be eliminated and a more preferable image can be reproduced.

  When the white balance adjustment process is performed on a part of the area in the input image, the white balance adjustment process is performed so that the degree (intensity) of the adjustment process decreases as the area near which the adjustment process is not performed is approached. By doing so, there is no sense of incongruity in color reproduction between a region where white balance adjustment is performed and a region where white balance adjustment is not performed, and a more preferable image can be reproduced.

  When the white balance adjustment process is performed on the entire input image, high-quality color reproduction of the entire image is possible.

  Further, when a process (means) for detecting the structure in the input image is added, the structure can be automatically detected, and the operability is improved.

  In addition, a plurality of models according to the present invention are provided for each attribute of a predetermined structure, and a process (means) for acquiring the attribute of the structure in the input image and selecting a model according to the acquired attribute is added. When the weighting parameter is obtained by adapting the structure in the input image to the selected model, the structure in the input image can be adapted to a more appropriate model Therefore, the processing accuracy is improved and a higher quality image can be obtained.

  When this structure is a human face, the face is often the subject part in the image, so it is possible to adjust the white balance optimized for the subject part. A more preferable image can be reproduced. Further, since the face is known to be a skin color, it is also suitable for white balance adjustment.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 1 schematically shows a hardware configuration of a digital photo printer according to an embodiment of the present invention. As shown in the figure, this digital photo printer has a film scanner 51, a flat head scanner 52, a media drive 53, a network adapter 54, a display 55, a keyboard 56, a mouse 57, a hard disk 58, and a photo print output device 59. The configuration is connected to the control device 50.

  The arithmetic / control device 50 executes the program installed from a storage medium such as a CD-ROM, and cooperates with the CPU, main storage device, and various input / output interfaces in the device to input, correct, process, and It controls the flow of output and performs image processing calculations for image correction and processing. The white balance adjustment processing according to the present invention is performed by this apparatus.

  The film scanner 51 photoelectrically reads a developed APS negative film or 135 negative film by a developing machine (not shown), and obtains digital image data P0 representing a photographic image recorded on these negative films. It is.

  The flat head scanner 52 photoelectrically reads a photographic image represented on a hard copy such as an L size photographic print to obtain digital image data P0.

  The media drive 53 acquires image data P0 representing a photographic image recorded on a recording medium such as a memory card, CD, or DVD. It is also possible to write image data P2 to be output on these recording media. In this memory card, for example, image data of an image taken by a digital camera is written. Further, for example, image data of an image read by a film scanner at the time of the previous print order is written on a CD, a DVD, or the like.

  The network adapter 54 acquires image data P0 from an order receiving machine (not shown) in a known network photo service system. This image data P0 is image data based on a photo print order from the user, and is transmitted from the user's personal computer via the Internet. Further, it may be transmitted from a photo order receiving machine installed at a lab store.

  The display 55 displays an operation screen for inputting, correcting, processing, and outputting an image in the digital photo printer, and displays a menu for selecting operation contents, an image to be processed, and the like. A keyboard 56 and a mouse 57 are used to instruct processing contents.

  The hard disk 58 stores a program for controlling the digital photographic printer, the image data P0 acquired by the film scanner 51, the flat head scanner 52, the media drive 53, and the network adapter 54, and the image after image correction. Data P1 and image data after image processing (image data to be output) P2 are also temporarily stored.

  The photographic print output machine 59 performs scanning exposure, development, and drying on the photographic paper with a laser based on the image data P2 representing the image to be output, and also prints back information such as print information, and cuts the photographic paper in print units. And sort by order. The photographic printing method may be a laser exposure thermal development transfer method or the like.

  FIG. 2 is a block diagram showing the function and processing flow of this digital photo printer. As shown in the figure, from a functional point of view, this digital photographic printer has an image input means 1 for inputting image data P0 of an image to be photographic printed, and image data P0 as an input, under predetermined image processing conditions. Image correction means 2 for automatically correcting the image quality of the image based on the image data P0 (hereinafter, the image data and the image based on the image data are represented by the same symbol), and the automatically corrected image data P1. An image processing unit 3 that performs image processing based on an instruction from an operator as an input and an image output unit 4 that outputs processed print data P2 and outputs it to a recording medium using the processed image data P2 as an input. Has been.

  The image correction unit 2 performs processing such as contrast correction, sharpness correction, and noise reduction / removal, and also performs white balance adjustment processing according to the present invention. Further, the image processing means 3 performs image processing such as manual correction of processing results by the image correction means 2, trimming, enlargement / reduction, sepia, black and white, synthesis with a decoration frame, and the like.

  The operation of this digital photo printer and the flow of processing performed by this printer are as follows.

  First, image data P0 is input by the image input means 1. When an operator performs printing or the like from an image recorded on a developed film, the operator sets the film on the film scanner 51 and uses image data recorded on a recording medium such as a memory card. When printing such as printing, the recording medium is set in the media drive 53. On the other hand, a screen for selecting an input source of image data is displayed on the display 55, and the operator selects an input source by operating the keyboard 56 and the mouse 57. When a film is selected as the input source, the film scanner 51 photoelectrically reads the set film and performs digital conversion to transmit the generated image data P0 to the arithmetic / control device 50. When a hard copy original such as a photo print is selected, the flat head scanner 52 photoelectrically reads the set hard copy original such as a photo print and converts it into digital data, thereby converting the generated image data P0. It transmits to the arithmetic / control apparatus 50. When a recording medium such as a memory card is selected, the arithmetic / control device 50 reads image data P 0 stored in the recording medium such as a memory card set in the media drive 53. In the case of an order by a network photo service system or a photo acceptance order machine at a store, the arithmetic / control device 50 receives the image data P 0 via the network adapter 54. The image data P0 acquired in this way is temporarily stored in the hard disk 58.

  Next, the image correction means 2 performs automatic image quality correction processing on the image P0. Specifically, the image processing program executed by the arithmetic / control device 50 performs known contrast correction, sharpness correction, noise reduction / removal, and the like based on the setup conditions set in advance in the digital photo printer. In addition to the processing, the white balance adjustment processing of the present invention is performed, and processed image data P1 is output. The output image data P1 is stored in the memory of the arithmetic / control device 50. Note that it may be temporarily stored in the hard disk 59.

  Thereafter, the image processing means 3 generates a thumbnail image of the corrected image P 1 and displays it on the display 55. FIG. 3A is an example of a screen displayed on the display 55. When the operator confirms the displayed thumbnail image and selects an image that requires manual image quality correction or an image processing order by operating the mouse 57 or the keyboard 56 (FIG. 3A). As shown in FIG. 3B, the selected thumbnail image is enlarged and displayed on the display 55, and manual correction and processing contents for the image are selected. A button is displayed. The operator selects a desired button from the displayed buttons by operating the mouse 57 or the keyboard 56, and performs more detailed setting of the selected processing contents as necessary. The image processing means 3 performs image processing according to the selected processing content and outputs processed image data P2. The output image data P2 is stored in the memory of the arithmetic / control device 50. Note that it may be temporarily stored in the hard disk 59. The screen display on the display 55 by the image processing means 3 described above, input reception by the mouse 57 and the keyboard 56, image processing for manual correction and processing, etc. are controlled by a program executed by the arithmetic / control device 50. Is done.

  Finally, the image output means 4 outputs the image P2. Here, the calculation / control apparatus 50 displays a screen for selecting an output destination on the display 55, and the operator selects a desired output destination by operating the mouse 57 and the keyboard 56. The arithmetic / control device 50 transmits the image data P2 to the selected output destination. When a photographic printout is performed, the image data P2 is transmitted to the photographic printout machine 59, and the image P2 is output as a photographic print. When outputting to a recording medium such as a CD, the image data P2 is written to a CD or the like set in the media drive 53.

  Here, the details of the white balance adjustment processing according to the present invention performed by the image correction means 2 will be described below. FIG. 4 is a block diagram showing details of the white balance adjustment processing. As shown in the drawing, the AAM (based on the face detection unit 31 for detecting the face part P0f 'in the image P0' and a plurality of sample images representing the human face part under illumination conditions of different color temperatures. By applying the detected face part P0f 'to the mathematical model M generated by the method of the above-mentioned Reference 1), the weighting parameter C for the principal component representing the illumination condition in the face part P0f' is obtained. The parameter acquisition unit 32 acquires the white balance adjustment amount W with reference to the reference table T based on the acquired parameter C, and adjusts the white balance of the face portion P0f ′ based on the acquired adjustment amount W. The white balance adjustment unit 33 that generates an image P1 ′ by replacing the face portion P0f ′ in the original image P0 ′ with the face portion P1f ′ after the adjustment processing, Adjust processing is realized. The control of these processes is performed by a program installed in the arithmetic / control device 50.

  The mathematical model M is generated based on the flowchart of FIG. 5 and is installed together with the above program. Below, the production | generation process of this mathematical model M is demonstrated.

  First, as shown in FIG. 6, a feature point representing a face shape is set for each of a plurality of face images (sample images) representing human face portions under illumination conditions of different color temperatures as samples. (Step # 1). Here, the number of feature points is 122 (however, only 60 locations are shown in FIG. 6 for simplicity). As for each feature point, for example, a part of the face is predetermined such that the first feature point is the left end of the left eye and the 38th feature point is the center between the eyebrows. In addition, each feature point may be set manually, or may be automatically set by recognition processing, or may be corrected manually if necessary after being set automatically. Also good.

  Next, the average shape of the face is calculated based on the feature points set in each sample image (step # 2). Specifically, the average of the position coordinates for each feature point indicating the same part in each sample image is obtained.

Further, principal component analysis is performed based on the feature points representing the face shape in each sample image and the position coordinates of the average shape (step # 3). As a result, an arbitrary face shape can be approximated by the following equation (1).

Here, S is a shape vector (x ₁ , y ₁ ,..., X ₁₂₂ , y ₁₂₂ ) expressed by arranging the position coordinates of each feature point of the face shape, and S ₀ is each feature in the average face shape. the average face shape vectors represented by arranging the position coordinates of the point, p _i is the eigenvector representing the i-th principal component of the face shape obtained by the principal component analysis, b _i represents a weighting factor for each eigenvector p _i. FIG. 7 schematically shows how the face shape changes when the values of the weighting coefficients b ₁ and b ₂ for the eigenvectors p ₁ and p ₂ of the top two principal components obtained by the principal component analysis are changed. It is a representation. The width of the change is between −3 sd and +3 sd based on the standard deviation sd of the values of the weighting coefficients b ₁ and b ₂ when each face shape of the sample image is expressed by the above equation (1). Yes, the central one of the three face shapes for each principal component is an average value. In this example, as a result of the principal component analysis, a component contributing to the contour shape of the face is extracted as the first principal component. By changing the weighting coefficient b ₁ , the round face from the elongated face (−3sd) is extracted. It can be seen that the face shape changes up to (+ 3sd). Similarly, a component that contributes to the open / closed state of the mouth and the length of the jaw is extracted as the second principal component. By changing the weighting coefficient b ₂ , a face with a long jaw (with the mouth open) It can be seen that the shape of the face changes from -3sd to a face with a short jaw (+ 3sd) with the mouth closed. In addition, it means that the explanatory power with respect to a shape is so high that the value of i is small, ie, the contribution to a face shape is large.

Next, each sample image is converted (warped) into the average face shape obtained in step # 2 (step # 4). Specifically, for each feature point, a shift amount between each sample image and the average face shape is calculated, and based on the shift amount, a two-dimensional quintic polynomial of equations (2) to (5) is used. The shift amount to the average face shape for each pixel of each sample image is calculated, and each sample image is warped to the average face shape for each pixel.

  Here, x and y are coordinates of each feature point in each sample image, x ′ and y ′ are coordinates on the average face shape to be warped, Δx and Δy are shift amounts to the average shape, n is an order, aij , Bij are coefficients. Note that the coefficient of polynomial approximation is obtained using the method of least squares. At this time, for a pixel whose coordinates after warping move to a position including a decimal point instead of an integer, a pixel value is obtained by first order approximation from four neighborhoods. That is, pixel values are distributed in proportion to the distance from the coordinates after warping to the coordinates of each pixel for the four pixels surrounding the coordinates after warping. FIG. 8 shows a state in which the face shape in each image is converted into an average face shape for two sample images.

Further, principal component analysis is performed using the R, G, and B primary color pixel values of each pixel for each sample image after conversion to the average face shape as variables (step # 5). As a result, the pixel values of the R, G, and B primary colors under the average face shape of an arbitrary face image can be approximated by the following equation (6).

Here, A is a vector (r ₁ , g ₁ , b ₁ , r ₂ , g ₂ , b ₂ , b) representing the pixel values of the R, G, and B primary colors of each pixel under the average face shape. ..., r _m , g _m , b _m ) (r, g, b are pixel values of the three primary colors R, G, B, 1 to m are subscripts for identifying each pixel, and m is an average face shape. The order of arrangement of the vector components is not limited to the above order. For example, (r ₁ , r ₂ ,..., R _m , g ₁ , g ₂ ,. _m , b ₁ , b ₂ ,..., b _m ). A ₀ is an average vector expressed by arranging average values of pixel values of R, G, and B primary colors for each pixel of each sample image in the average face shape, and q _i is a face obtained by principal component analysis. An eigenvector representing the i-th principal component for pixel values of R, G, and B primary colors, λ _i represents a weighting coefficient for each eigenvector q _i . It is to be noted that the smaller the value of the main component order i, the higher the explanatory power for the pixel values of the R, G, B three primary colors, that is, the greater the contribution to the pixel values of the R, G, B primary colors.

FIG. 9 shows a case where the values of the weighting coefficients λ _i1 and λ _i2 for the eigenvectors q _i1 and q _i2 representing the i ₁ and i ₂ principal components among the principal components obtained by the principal component analysis are changed. This is a schematic representation of how the face pixel value changes. The width of the change is from −3 sd to +3 sd based on the standard deviation sd of the values of the weighting coefficients λ _i1 and λ _i2 when the pixel value of each face of the sample image is expressed by the above equation (6). The middle one of the three face images for each principal component is an average value. In this example, as a result of the principal component analysis, a component contributing to the presence or absence of a beard is extracted as the i _1st principal component, and by changing the weighting coefficient λ _i1 , the dark face with a beard (−3sd) is extracted. It turns out that it changes to a face without a beard (+ 3sd). Similarly, a component that contributes to the shadow state of the face is extracted as the i ₂ main component, and a face with shadow on the right side of the face (−3sd) by changing the weighting coefficient λ _i2 It turns out that the face changes to a shadowed face (+ 3sd) on the left side. It should be noted that what elements each principal component contributes is determined by human interpretation.

In the present embodiment, a plurality of face images representing human face portions under illumination conditions with different color temperatures are used as sample images, so that a component contributing to the difference in illumination conditions is extracted as the first principal component. If shall be, when changing the value of the weighting factor lambda ₁ for eigenvector q ₁ of the first principal component, for example, the color of the 'face portion P0f in' image P0 is reddish ones (color temperature From low) to bluish (high color temperature). It should be noted that the main component that contributes to the difference in illumination conditions is not necessarily extracted as the first main component. When the component contributing to the difference in illumination conditions is extracted as the Kth principal component (K ≠ 1), it can be considered by replacing “first” in the following description with “Kth”. Also, the difference in illumination conditions need not be expressed by only one principal component, and a plurality of principal components may explain the difference in illumination conditions.

The mathematical model M of the face is generated by the above steps # 1 to # 5. That is, the mathematical model M is expressed by a plurality of eigenvectors p _i representing the face shape and an eigenvector q _i representing the pixel value of the face under the average face shape, and the total number of each eigenvector is the face It is dimensionally compressed, which is significantly smaller than the number of pixels forming an image. In the embodiment described in Reference 1, the above processing is performed by setting 122 feature points on a face image formed by about 10,000 pixels, so that 23 eigenvectors for the face shape and the face A mathematical model of the face image represented by 114 eigenvectors for pixel values was generated, showing that by changing the weighting coefficient for each eigenvector, approximately 98% face shape and pixel value variations can be expressed. Yes.

  Next, the flow of white balance adjustment processing based on the AAM technique using this mathematical model M will be described with reference to FIG.

  First, the face detection unit 31 reads the image data P0 ′ and detects a face portion P0f ′ in the image P0 ′. Specifically, a knowledge base, feature extraction, skin color detection, template, as well as a method using a correlation score between a unique face expression and the image itself as described in JP-T-2004-527863 (reference 2) Various known methods such as matching, graph matching, and statistical methods (neural network, SVM, HMM) can be used. When the image P0 'is displayed on the display 55, the face shape P0f' may be manually designated by operating the mouse 57 or the keyboard 56, or the result of automatic detection is manually corrected. You may make it do.

Next, the parameter acquisition unit 32 performs processing for adapting the face portion P0f ′ to the mathematical model M. Specifically, while changing the values of the coefficients in order from the weighting coefficients b _i and λ _i for the eigenvectors p _i and q _i of the upper principal components of the above expressions (1) and (6), the above expressions (1) and (1) ( 6) The image is reconstructed based on 6), and weighting coefficients b _i and λ _i when the difference between the reconstructed image and the face portion P0f ′ is minimized are obtained (refer to Reference 2 etc. for details). . The weighting coefficient λ _{1 at} this time is a parameter C representing the illumination condition. The values of the weighting coefficients b _i and λ _i are based on the standard deviation sd of the distribution of b _i and λ _i when the sample image at the time of model generation is expressed by the above (1) and (6), for example, −3sd It is preferable to allow only a value in the range from +3 to +3 sd and to set an average value in the above distribution when exceeding the range. As a result, erroneous adaptation of the model can be avoided.

  First, the white balance adjustment unit 33 refers to the reference table T based on the obtained parameter C and acquires the white balance adjustment amount W. Here, the reference table T will be described. FIG. 10 shows an example of the configuration and values of the reference table T. As shown in the figure, parameter C, color temperature, and white balance adjustment amount are associated with each other. This table is a combination of the correspondence between the value of the parameter C and the color temperature, which has been experimentally and statistically obtained in advance, and the correspondence between the color temperature of the light source and the adjustment amount W of the white balance. It is. Here, for example, the white balance adjustment amount W may be determined so that the chromaticity point in the average value of the color temperature corresponding to the parameter C and the color temperature of the output medium is gray (Japanese Patent Laid-Open No. 2000-244940). No. publication). Note that the color temperature item in the figure is added for the sake of explanation, and the reference table T can be configured with only two items of the parameter C and the white balance adjustment amount W in terms of mounting. Alternatively, a function for outputting the white balance adjustment amount W by defining the parameter C as an input without using the reference table may be defined, and the white balance adjustment amount W may be obtained based on the function.

In the above description, it is assumed that there is only one principal component representing the illumination condition. However, when there are a plurality of principal components, weighting coefficients (for example, λ ₁ , λ ₂ ,. The color temperature and white balance adjustment amount W may be determined based on the combination of the values of λ _J ). That is, the parameter C may be obtained as a linear combination of each weighting coefficient as in the following equation (7) (α _i is a contribution degree of the principal component corresponding to the weighting coefficient λ _i to the illumination condition) ),

You may make it obtain | require by the high-dimensional reference table which matches the combination of the value of each weighting coefficient, and the value of the parameter C. FIG. Further, weighting coefficient lambda ₁ item parameter C of the reference table of FIG. 10, lambda _2, is divided ..., a plurality of items and so lambda _J, the color for the combination of the values of each weighting coefficient The reference table may be configured to associate the temperature and white balance adjustment amount W with each other. Further, a function for outputting the white balance adjustment amount W is defined with the weighting coefficients λ ₁ , λ ₂ ,..., Λ _J as inputs, and the white balance adjustment amount W is obtained based on the function. It may be.

Next, the white balance adjustment unit 33 performs a known white balance adjustment process on the face portion P0f ′ based on the white balance adjustment amount W. Specifically, the conversion process shown in the following equation (8) is performed.

Here, R ′, G ′, B ′ are RGB values of each pixel of the face portion P1f ′ after white balance adjustment, R, G, B are RGB values of each pixel of the face portion P0f ′ before adjustment, and W is W It is a matrix showing the amount of white balance adjustment.

  Further, the white balance adjustment unit 33 generates an image P1 ′ by replacing the face portion P0f ′ of the image P0 ′ with the converted face portion P1f ′.

  Note that this conversion process may be applied not only to the face portion P0f 'but also to the entire image P0' to generate the image P1 '. Further, a skin color area (for example, a neck part) around the face part P0f ′ is detected by a known technique (for example, the technique described in Patent Document 2), and the same conversion process is performed on the detected skin color area. Also good. Further, when this conversion process is performed only on a partial area of the image P0 ′, the adjustment amount W is changed so that the white balance adjustment amount decreases as the area near which the conversion process is not performed is approached. However, this conversion process may be performed. In this case, the image P1 ′ is generated by replacing the conversion target area of the image P0 ′ with the converted area.

  As described above, according to the white balance adjustment processing according to the embodiment of the present invention, the parameter acquisition unit 32 illuminates the face portion P0f ′ in the image P0 ′ detected by the face detection unit 31 with illumination of different color temperatures. By applying a mathematical model M generated by the AAM method based on a plurality of sample images representing a human face part under conditions, a weighting parameter C for a principal component representing the illumination condition in the face part P0f ' The white balance adjusting unit 33 acquires the white balance adjustment amount W by referring to the reference table T based on the acquired parameter C, and the face portion P0f ′ of the face portion P0f ′ based on the acquired adjustment amount W. Adjust the white balance. Here, paying attention to the parameter C corresponding to the main component representing the illumination condition, and adjusting the white balance at an appropriate degree (processing intensity) according to the value of this parameter, illumination such as shading due to shadows and signal jumps It is possible to perform white balance adjustment processing that reflects the illumination conditions with high accuracy, with less influence of elements other than the conditions, and it is possible to reproduce an image with higher color reproducibility.

In the above embodiment, only one mathematical model M exists, but a plurality of mathematical models M _i (i = 1, 2,... ) May be generated. FIG. 11 is a block diagram showing details of the white balance adjustment processing in this case. As shown, on the basis of the image data P0 ', the attribute acquiring unit 34 for acquiring attribute information A _K of the object in the image, based on the acquired attribute information A _K, representing a subject having that attribute This is different from the above-described embodiment (FIG. 4) in that it includes a model selection unit 35 that selects a mathematical model M _K generated only from a sample image.

Here, the plurality of mathematical models M _i are respectively generated based on the above-described method (see FIG. 5) from only sample images representing subjects having the same race, age, gender, and the like. Are stored in association with attribute information A _i representing common attributes.

Attribute acquisition unit 34, a known recognition process for the image P0 '(e.g., JP-A-11-175724 JP) by performing, to determine the attributes of the object, to the attribute information A _K may be obtained, At the time of shooting, the attribute of the subject may be recorded in the header or the like as supplementary information of the image data P0 ′, and the recorded information may be acquired. Further, the attribute of the subject may be estimated based on the accompanying information. For example, if there is GPS information of the shooting location, it is possible to specify the country or region corresponding to the GPS information, and therefore pay attention to the fact that the race of the shot subject can be estimated to some extent. An image obtained by a digital camera (for example, described in Japanese Patent Application Laid-Open No. 2004-153428) that prepares a reference table for associating seed information in advance and acquires GPS information at the time of shooting and records it in the header area of image data P0 ′ The GPS information recorded in the header area of the image data P0 ′ is acquired with the data P0 ′ as an input, the reference table is referred to based on the acquired GPS information, and the race information associated with the GPS information is obtained. Can be estimated as the race of the subject.

Model selection unit 35 obtains the mathematical model M _K associated with the obtained attribute information A _K by the attribute acquiring unit 34, the parameter obtaining unit 32, adapted to face portion P0f 'of the image P1 in the mathematical model M _K Let

Thus, prepared in advance mathematical model M _i corresponding to a plurality of attributes, the model selection unit 35, mathematical model M associated with the attribute A _K of the image of the subject P0 'acquired by the attribute acquiring unit 34 select _K, the parameter acquiring unit 32, when so adapt the face shape P0f 'the mathematical model M _K which is selected, the mathematical model M _K Ya face shape due to the difference in the attribute a _K Since there is no eigenvector that explains the variation in luminance, it becomes possible to express the face shape P0f ′ based only on the eigenvector representing other factors that determine the face shape and luminance, and the processing accuracy is improved. High quality images can be obtained.

  From the viewpoint of improving processing accuracy, it is preferable to further specialize a mathematical model for each attribute and construct a mathematical model for each subject. In this case, it is necessary to associate the image P0f ′ with information for identifying an individual.

  In the above embodiment, the mathematical model is preinstalled in the digital photographic printer. However, a mathematical model for each person is prepared in advance and installed depending on the country or region where the printer is shipped. Changing the mathematical model is also preferable from the viewpoint of improving processing accuracy.

  Further, a function for generating this mathematical model may be implemented in a digital photo printer. Specifically, a program that performs the processing described based on the flowchart of FIG. 5 may be installed in the arithmetic / control device 50. In addition, a default mathematical model is installed at the time of shipment, and the mathematical model can be customized (changed) using the input image to the digital photo printer, or a new model different from the default mathematical model can be created. It is also possible to generate them. This is particularly effective when generating an individual model as described above.

In the above-described embodiment, individual face images are represented by the face shape and the separate weighting coefficients b _i and λ _i for the R, G, and B primary color pixel values. Since the variations of the pixel values of the three primary colors G and B are correlated, vectors (b ₁ , b ₂ ,..., B _i ,...) Obtained by combining the weighting coefficients b _i and λ _i. , λ ₁ , λ ₂ ,..., λ _i ,...)), and as shown in the following equations (9) and (10), the face shape and R, G , B, a new appearance parameter c that controls both pixel values of the three primary colors can be obtained.

Here, the variation of the shape from the average face shape is expressed by the appearance parameter c and the vector Q _S , and the variation component of the pixel value from the average of the face pixel values is expressed by the appearance parameter c and the vector Q _A. .

  When this model is used, the parameter acquisition unit 32 obtains the pixel value of the face under the average face shape based on the above equation (10) while changing the value of the appearance parameter c. By converting from the average face shape based on (9), the face image is reconstructed, and the appearance parameter c when the difference between the reconstructed image and the face portion P0f ′ is minimized is obtained. Become.

  As another embodiment of the present invention, it is conceivable to implement the above white balance adjustment processing in a digital camera. In other words, this is a case where the digital camera is implemented as an auto white balance (AWB) function. FIG. 12 schematically shows the configuration of such a digital camera. As shown in the figure, this digital camera includes a lens, an aperture, a shutter, a CCD, and the like, and digitizes analog signals based on charges accumulated in the CCD of the imaging unit 71 and the imaging unit 71 for imaging a subject. An A / D conversion unit 72 that obtains image data P0, an image processing unit 73 that performs various image processing on the image data P0, etc., compression processing of image data to be recorded on a memory card, and compressed image data read from the memory card A compression / decompression unit 74 that performs decompression processing on the image, a strobe, etc., a strobe unit 75 that emits strobe light, various operation buttons, etc. It consists of a media recording unit 77 that serves as an interface with the memory card to be used, a liquid crystal display, etc. Image display unit 78 for displaying various setting menus and the like, the control unit 70 for controlling the processing by the respective units, and has an internal memory 79 for storing a control program and image data.

  Here, the image input means 1 in FIG. 2 is the imaging unit 71 and the A / D conversion unit 72, the image correction unit 2 is the image processing unit 73, the image processing unit 3 is the image processing unit 73, the operation unit 76, and the display unit 78, Each function of the image output unit 4 is realized by the media recording unit 77 while using the internal memory 79 under the control of the control unit 70.

  Next, the operation and processing flow of this digital camera will be described.

  First, when the photographer fully presses the shutter, the imaging unit 71 forms an image of the subject light incident on the lens on the photoelectric surface of the CCD, outputs an analog image signal after photoelectric conversion, and the A / D conversion unit 72. However, it functions as the image input means 1 by converting the output analog image signal into a digital image signal and outputting it as digital image data P0.

  Next, the image processing unit 73 performs gradation correction processing, density correction processing, color correction processing, sharpness processing, etc., and also performs white balance adjustment processing according to the present invention, and outputs processed image data P1. , Function as the image correction means 2. Here, in the white balance adjustment processing, the control unit 70 activates a white balance adjustment program stored in the internal memory 79 and uses the mathematical model M and the reference table T stored in the internal memory 79 in advance. This is realized by causing the image processing unit 73 to perform the above-described white balance adjustment processing (see FIG. 4 and the like).

  Here, the image P 1 is displayed on the liquid crystal display by the display unit 78. As a display layout, display of a plurality of images in the thumbnail format shown in FIG. The photographer selects and enlarges an image to be processed by operating the operation button of the operation unit 76, performs further manual correction and processing of the image by menu selection, and outputs processed image data P2. As described above, the function of the image processing means 3 is realized.

  Then, the compression / decompression unit 74 performs compression processing on the image data P2 based on a compression format such as JPEG, and writes the compressed image data to the memory card loaded in the digital camera via the media recording unit 77. The function of the image output means 4 is realized.

  As described above, by implementing the white balance adjustment processing of the present invention as an image processing function of a digital camera, it is possible to obtain the same effect as that of the above-described digital photo printer.

  Note that manual correction and processing may be performed on images once recorded on the memory card. Specifically, after the compression / decompression unit 74 decompresses (decompresses) the image data stored in the memory card, an image based on the decompressed image data is displayed on the liquid crystal display of the display unit 78, and the photographer The desired image processing is selected by the same operation as described above, and the image processing unit 73 performs the selected image processing.

  Further, the mathematical model for each subject attribute described with reference to FIG. 11 or the like may be mounted on the digital camera, or the mathematical model generation processing illustrated in FIG. 5 may be mounted. Here, since the person who is the subject of photography with each digital camera is often limited to some extent, it is recommended to generate an individual mathematical model for the face of the person who is mainly the subject with that digital camera. For example, it is possible to generate a model that does not vary due to individual differences in the face, and therefore white balance adjustment processing for the face of the person can be performed with extremely high accuracy.

  In addition to the above-described embodiment, a program for causing a personal computer or the like to perform the white balance adjustment processing of the present invention may be incorporated into image editing software. As a result, the user can install the software from a storage medium such as a CD-ROM in which the software is stored, or download and install the software from a predetermined site on the Internet. It is possible to use the white balance adjustment processing of the present invention as one pattern for image editing on a personal computer.

The figure which showed typically the hardware constitutions of the digital photo printer which becomes embodiment of this invention 1 is a block diagram showing functions and processing flow of a digital photo printer and a digital camera according to an embodiment of the present invention. The figure which shows an example of the screen displayed on the display of the digital photograph printer and digital camera which become embodiment of this invention The block diagram showing the detail of the white balance adjustment process which becomes 1 aspect of this invention The flowchart showing the flow of the process which produces | generates the mathematical model of the face image in this invention A diagram showing an example of setting facial feature points A diagram that schematically shows how the face shape changes when the value of the weighting coefficient for the eigenvector of the principal component obtained by principal component analysis on the face shape is changed. A figure showing the brightness under the average face shape after converting the face shape in the sample image to the average face shape A diagram schematically showing how the face pixel value changes when the weighting coefficient value for the eigenvector of the principal component obtained by principal component analysis for the face pixel value is changed The figure which shows an example of a structure and value of a reference table used by the white balance adjustment process of this invention. The block diagram showing the development aspect of the white balance adjustment process which becomes one aspect | mode of this invention The figure which showed typically the structure of the digital camera which becomes other embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image input means 2 Image correction means 3 Image processing means 4 Image output means 31 Face detection part 32 Parameter acquisition part 33 White balance adjustment part 34 Attribute acquisition part 35 Model selection part 51 Film scanner 52 Flat head scanner 53 Media drive 54 Network adapter 55 Display 56 Keyboard 57 Mouse 58 Hard disk 59 Photo print output device 70 Control unit 71 Imaging unit 72 A / D conversion unit 73 Image processing unit 74 Compression / decompression unit 75 Strobe unit 76 Operation unit 77 Media recording unit 78 Display unit 79 Internal memory

Claims (9)

One or more statistical feature quantities including a statistical feature quantity representing a light source condition, obtained by performing a predetermined statistical process on a plurality of images representing a predetermined structure under different light source conditions; By adapting the structure in the input image to a model representing the structure with a weighting parameter that weights the statistical feature according to individual features of the structure. Obtaining a weighting parameter for a statistical feature amount representing the light source condition in the structure in the image;
An image processing method comprising adjusting at least a white balance of a region of the structure in the input image based on the acquired value of the weighting parameter. One or more statistical feature quantities including a statistical feature quantity representing a light source condition, obtained by performing a predetermined statistical process on a plurality of images representing a predetermined structure under different light source conditions; By adapting the structure in the input image to a model representing the structure with a weighting parameter that weights the statistical feature according to individual features of the structure. Parameter acquisition means for acquiring a weighting parameter for a statistical feature amount representing the light source condition in the structure in the image;
Image processing comprising: white balance adjusting means for adjusting at least white balance of the region of the structure in the input image based on the value of the weighting parameter acquired by the parameter acquiring means apparatus.   The white balance adjusting unit applies a weighting parameter for a statistical feature amount representing the light source condition acquired by the parameter acquiring unit to an area of the same color as the structure adjacent to the predetermined structure. 3. The image processing apparatus according to claim 2, wherein the white balance is adjusted.   The white balance adjusting means adjusts the white balance so that the degree of the adjustment becomes smaller in the region where the white balance is adjusted, as the region approaches the region where the white balance is not adjusted. The image processing apparatus according to claim 2 or 3.   The white balance adjustment means adjusts the white balance for the entire input image based on a weighting parameter for a statistical feature amount representing the light source condition acquired by the parameter acquisition means. The image processing apparatus according to claim 2, wherein: It further comprises detection means for detecting the structure in the input image,
The said parameter acquisition means acquires the said weighting parameter by adapting the said detected structure to the said model, The any one of Claim 2 to 5 characterized by the above-mentioned. Image processing apparatus. Selection means for acquiring an attribute of the structure in the input image and selecting the model according to the acquired attribute from a plurality of the models in which the structure is represented for each attribute of the predetermined structure Further comprising
The image according to any one of claims 2 to 6, wherein the parameter acquisition means acquires the weighting parameter by adapting the structure to a selected model. Processing equipment.   The image processing device according to any one of claims 2 to 7, wherein the predetermined structure is a human face. On the computer,
One or more statistical feature quantities including a statistical feature quantity representing a light source condition, obtained by performing a predetermined statistical process on a plurality of images representing a predetermined structure under different light source conditions; By adapting the structure in the input image to a model representing the structure with a weighting parameter that weights the statistical feature according to individual features of the structure. Parameter acquisition means for acquiring a weighting parameter for a statistical feature amount representing the light source condition in the structure in the image;
An image processing program that functions as a white balance adjustment unit that adjusts at least a white balance of a region of the structure in the input image based on the value of the weighting parameter acquired by the parameter acquisition unit. .

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