EP0718788A2

EP0718788A2 - Method and apparatus for the formation of standardized image templates

Info

Publication number: EP0718788A2
Application number: EP95420342A
Authority: EP
Inventors: Lawrence A. C/O Eastman Kodak Co. Ray; Richard N. C/O Eastman Kodak Co. Ellson; Maxime c/o Eastman Kodak Co. Elbaz
Original assignee: Eastman Kodak Co
Current assignee: Eastman Kodak Co
Priority date: 1994-12-21
Filing date: 1995-12-05
Publication date: 1996-06-26
Also published as: BR9505966A; ZA959492B; CN1150283A; AR000239A1; JPH08249469A; EP0718788A3

Abstract

The present technique facilitates the formation of an image feature template that finds particular utility in the compression and decompression of like-featured images. More specifically, the feature template enables the compression and decompression of large collections of images which have consistent sets of like image features that can be aligned and scaled to position these features into well correlated regions. The feature template includes a plurality of template elements each representing specific features of an image, and data representing the attributes of each template element. The preferred methodology for forming the feature template comprises the steps of:
establishing the dimensions of a feature template to accommodate a standardized image;
partitioning the feature template into a plurality of feature types to accommodate like features in the standardized image;
assigning at least one template element to each feature type; and
recording the position of all assigned template elements within the dimensions of said feature template to facilitate the reconstruction of the so formed feature template.

Description

Cross-Reference To Related Applications

The present application is related to:
EP-A-651,355, entitled "Method And Apparatus For Image Compression, Storage and Retrieval On Magnetic Transaction Cards".
EP-A-651,354 entitled "Compression Method For A Standardized Image Library".
US-A-5,473,327 entitled "Method And Apparatus For Data Encoding With Reserved Values".
US 08/361,368 filed December 21, 1994, by Ray, Ellson, and Elbaz, entitled "Method For Compressing And Decompressing Standardized Portrait Images".
So far as permitted the teachings of the above referenced Applications being incorporated by reference as if set forth in full herein.

Field Of The Invention

The present invention relates to the field of digital image processing and more particularly to a method and associated apparatus for forming digital standardized image feature templates for facilitating a reduction in the number of bits needed to adequately represent an image.

Background Of The Invention

Consider a library of pictures with similar image content, such as a collection of portraits of missing children. In this collection of images, there exists a large degree of image-to-image correlation based upon pixel location as faces share certain common features. This correlation across different images, just like the spatial correlation within a given image, can be exploited to improve compression.
Analysis of some image libraries yields knowledge of the relative importance of image fidelity based on location in the images. If the images were to be used for identification of missing children, then the image fidelity on the facial region would be more important than fidelity in the hair or shoulders which in turn would be more important than the background. Image compression can be more aggressive in regions where visual image fidelity is less important to the application.
In many applications, preserving the orientation and quantization of the original image is less important than maintaining the visual information contained within the image. In particular, for images in the missing children library, if the identity of the child in the portrait can be ascertained with equal ease from either the original image or an image processed to aid in compression, then there is no loss in putting the processed image into the library. This principle can be applied to build the library of processed images by putting the original images into a standardized format. For missing children portraits this might include orienting the head of each child to make the eyes horizontal, centering the head relative to the image boundaries. Once constructed, these standardized images will be well compressed as the knowledge of their standardization adds image-to-image correlation.
Techniques from a compression method known as vector quantization (VQ) are useful in finding correlation between portions of an image. Compression by vector quantization VQ is well suited for fixed-rate, lossy, high-ratio compression applications (see R. M. Gray, "Vector Quantization", IEEE ASSP Magazine, Vol. 1, April, 1984, pp. 4-29). This method breaks the image into small patches of "image blocks." These blocks are then matched against other image blocks in a predetermined set of image blocks, commonly known as the codebook. The matching algorithm is commonly the minimum-squared-error (MSE). Since the set of image blocks is predetermined, one of the entries of the set can be referenced by a simple index. As a result a multi-pixel block can be referenced by a single number. Using such a method the number of bits for an image can be budgeted. When a greater number of bits is allocated per image block, the size of the codebook can be increased. Similarly, if a greater number of bits is allocated to the image, then the number of image blocks can be increased (and hence the size of each block reduced).
Codebooks are determined by first forming a collection of representative images, known as the training image set. Next, images are partitioned into image blocks, and the image blocks are then considered as vectors in a high-dimensional vector space, i.e., for an 8 x 8 image block, the space has 64 dimensions. Image blocks are selected from predetermined regions within each image of the training set of images. Once all the vectors are determined from the training set, clusters are found and representative elements assigned to each cluster. The clusters are selected to minimize the overall combined distances between a member of the training set and the representative for the cluster the member is assigned to. A selection technique is the Linde-Buzo-Gray (LBG) algorithm (see Y. Linde, et. al., "An Algorithm For Vector Quantizer Design", IEEE Transactions On Communications, Vol. COM-28, No. 1, January, 1980, pp. 84-95). The number of clusters is determined by the number of bits budgeted for describing the image block. Given n bits, the codebook can contain up to 2ⁿ cluster representatives or code vectors.
The above referenced patent applications, U.S. Application Serial Number 08/145,051 by Ray, Ellson and Gandhi, and U.S. Application Serial Number 08/145,284 by Ray and Ellson, both describe a system for enabling very high-compression ratios with minimal loss in image quality by taking advantage of standardized features in a library of images. This method of compression takes advantage of the built-in image-to-image correlation produced by the standardization to improve predictability and, therefore, improve compressibility by training on the standardized images and forming multiple codebooks consisting of 8x8 pixel codevectors.
These applications describe a process for extracting the common features of images in the library and using this as a basis for image standardization. Once standardized into a standardized library image, the image can be compressed and subsequently decompressed into lossy representations of the original library image.
An overview of the prior art as described by the above referenced patent applications consist of:

Standardization:

Select the image features of greatest importance.
Process a set of representative images from the library to enhance the selected features.
Locate selected features in the representative images.
Determine constraints for locations of image features.
Process the image to meet the image feature location constraints.
Assign regions of the image based on presence of features or a desired level of image quality.
Determine the image-to-image correlation of the images for each subregion.
Allocate capacity for storage of image information for each subregion based on a partition subregions into image blocks and codebook size.
Construct codebooks to take advantage of the correlation.
Process the image to enhance features.
Locate selected features in the image.
Standardize the image by processing the image to meet the image feature location constraints.
Partition the image based on the subregions and their image blocks.
For each region, determine the entry in the codebook which best approximates the image content.
Store the series of codebook values for each image block as this is the compressed image.

Decompression:

Extract the codebook values from the series of codebook value.
Determine the codebook based on corresponding subregion position in the codebook value series.
Extract an image block based on the codebook value from the above determined codebook.
Copy the image block into the appropriate image block location in the subregion.
Continue inserting image blocks until all image block locations are filled in the entire image.
In order to store a compressed facial image in a manner consistent with international standards for a single track of a magnetic transaction card, the available data capacity is below 500 bits (see ISO 7811/2).
When the target number of bits is extremely small, as in the case of facial image storage in under 500 bits, the above described compression/decompression process fails to provide facial images of consistent quality for use in certain verification and identification applications. Additional techniques are necessary to further improve the quality of the compressed images for more demanding verification systems. Opportunities for improvement exist in image standardization, specialized codebook formation, and image block symmetry.
Even with standardization of the location and orientation of the face within an image, the lighting conditions for portraits may be highly asymmetric. This results in a luminance imbalance between the left and right sides of a human facial portrait. What is needed is a method for balancing the lightness of a human facial portrait in order to achieve a higher degree of facial image portrait standardization and enhance the natural symmetry of the human facial image.
With the standardization of both the luminance and the location of image features, codebooks can be developed to better represent the expected image content at a specified location in an image. A method of codebook specialization by Sexton, U. S. Patent No. 5,086,480, entitled "Video Image Processing", which describes the use of two codebooks. This compression method finds the best codevector from amongst two codebooks by exhaustively searching both codebooks, and then it flags the codebook in which the best match was found. The net result is a "super-codebook" containing two codebooks of possibly different numbers of codevectors where the flag indicates the codebook selected. Codebook selection does not arise from a priori knowledge of the contents of a region of the image; Sexton calculates which codebook to use for every codevector in every image. An opportunity for greater compression is to eliminate the need to store the codebook flag.
It should be noted that the method of Sexton requires all codevectors in both codebooks to have the same dimensions. Also, the prior art of Ray referred to above partitions images into equal sized image blocks.
Another opportunity to improve the quality of compressed portraits is to use the correlation within facial images is the approximate mirror image symmetry between the left and right sides of the face. Frequently in near front perspective portraits, a large degree of correlation exists between the portions of the face close to the centerline. In particular, the image blocks used to render the part of the face above and below the eyes exhibit a high degree of symmetric correlation. However, along the centerline of the face the level of symmetry drops off due to the variability of the appearance of the nose when viewed from slightly different angles. What is needed is a method to further reduce the number of bits necessary to store a compressed portrait image by exploiting the nature symmetry of the human face in the regions around the facial centerline without imposing deleterious symmetry constraints on the nose.
Some areas of the image do not contribute any significant value to identifying an individual. For instance, shoulder regions are of minimal value to the identification process, and moreover, this region is usually covered by clothing which is highly variable even for the same individual. Since little value is placed in such regions the allocation of bits to encode the image should also be reduced. In the present invention some of these areas have been allocated few if any bits, and the image data is synthesized from image data of neighboring blocks. This permits a greater allocation of bits to encode more important regions.

Summary Of The Invention

The present technique facilitates the formation of an image feature template that finds particular utility in the compression and decompression of like-featured images. More specifically, the feature template enables the compression and decompression of large collections of images which have consistent sets of like image features that can be aligned and scaled to position these features into well correlated regions.
The feature template of the present invention comprises:

a plurality of template elements each representing a feature of an object; and
data representing the attributes of each template element.

The preferred methodology for forming a feature template comprises the steps of:

establishing the dimensions of a feature template to accommodate a standardized image;
partitioning said feature template into a plurality of feature types to accommodate like features in the standardized image;
assigning at least one template element to each feature type; and
recording the position of all assigned template elements within the dimensions of said feature template to facilitate the reconstruction of the so formed feature template.

From the foregoing it can be seen that it is a primary object of the present invention to provide a feature template that is usable in a system for reducing the data storage requirements for associated sets of images.
The above and other objects of the present invention will become more apparent when taken in conjunction with the following description and drawings wherein like characters indicate like parts and which drawings form a part of the present invention.

Brief Description Of The Drawings

Figures 1A, 1B, and 1C, illustrate a frontal facial portrait that is tilted, rotated and translated to a standardized position, and sized to a standardized size, respectively.
Figure 2 illustrates, in flow chart form, the method for standardizing an image.
Figure 3A shows the positions and sizes of the template elements that form a template.
Figure 3B illustrates, by darker shaded areas, the positions and sizes of the template elements of the template which have a left-to-right flip property.
Figure 3C illustrates, by darker shaded areas, the positions and sizes of the template elements of the template which have a top-to-bottom flip property.
Figure 3D illustrates, by darker shaded areas, the positions and sizes of the template elements of the template which are linked.
Figure 4 illustrates, in table form, the portrait features, their associated labels, and their characteristics.
Figures 5A and 5B illustrate the template element data record for the elements in the template illustrated in Figures 3A-3D.
Figure 6 illustrates a collection of tiles associated with each of the feature types A-M used in the specific embodiment of the present invention.
Figure 7 illustrates the tile numbering and labeling for a compressed image.
Figure 8 illustrates the tiles as extracted from the feature type tile collections with the lighter shaded tiles having at least one flip property.
Figure 9 illustrates the tiles after execution of all flip properties.
Figure 10 illustrates the final image.
Figure 11 illustrates a preferred apparatus arrangement on which the method of the present invention may be executed.

Detailed Description Of The Invention

Figure 1A, represents an image that is a front facial portrait. In this example of an image the face is tilted and translated with respect to the center of the image. Depending on the source of images, other variations in the positioning and sizing of the face within the borders of the image may be encounted. To achieve maximum results with the present invention the size, position and orientation of the face is to be standardized. In order to operate upon the image the image is placed into a digital format, generally as a matrix of pixel values. The digital format (pixel values) of the image is derived by scanning the original image to convert the original image into electrical signal values that are digitized. The digital image format is then used to replicate the image on a display to facilitate the application of a standardization process to the displayed image and to the pixel values forming the displayed image to form a standardized geometric image. The images are standardized to provide a quality match with the template elements associated with the template (to be described in detail later in the description of the invention). The process starts at Figure 1A by locating the center of the left and right eyes of the face in the image. In Figure 1B a new digital image of the face image, representing a partially standardized geometric image, is formed by rotating and translating the face image of Figure 1A, as necessary, by well known image processing operations, so as to position the left and right eye centers along a predetermined horizontal axis and equally spaced about a central vertical axis. Figure 1C illustrates the face image of Figure 1B sized to form a standardized geometric face image by scaling the image to a standard size.
Referring now to Figure 2, the method of forming the standardized geometrical image is set forth in the left column of flow blocks commencing with the block labeled "select an image". The selection process is based upon the availability of a front facial image of the person that is to have their image processed with the template of the invention. Included in the selection process is the creation of a digital matrix representation of the available image. The digital matrix, is next loaded into a system (shown in Figure 11) for display to an operator. As previously discussed the operator locates the left and right eye and performs any needed rotation, translation and rescaling the image to form the standardized geometrical image.
More specifically, with regards to the standard image of Figure 1C and the flow chart of Figure 2, in this embodiment of the invention, the image standard was set to an image size of width of 56 pixels and a height of 64 pixels with the eye centers located 28 pixels from the top border of the image and 8 pixels on either side of a vertical center line. Identifying the centers of the left and right eyes is done by displaying the initial image to a human operator who points to the centers with a locating device such as a mouse, tablet, light pen or touch sensitive screen. An alternate approach would be to automate the process using a feature search program. The human operator localize the eye positions, and a processor fine-tunes the location through an eye-finding search method restricted to a small neighborhood around the operator-specified location. The next step in standardization is to alter the image to standardize the position of the eyes to a predetermined location. In general, this consists of the standard image processing operations of image translation, scaling and rotation.
With the image size and eye position adjustments made, the standardized geometric image is stored, and the luminance standardization procedure takes place. The procedure is represented by the flow blocks evenly labeled 40-52. There are three spatial scales used to standardize for variations in the luminance of the digitized images; large for light level/direction, medium for correcting asymmetric shadows from side lights, and small for reduction of specular highlights from glasses, jewelry and skin. These procedures change the mean luminance level in the digitized image. Certain features which are useful in identification of an individual tend to get subdued in gray-scale portraits. Hence, variations in luminance level, referred to as contrast, are also adjusted in order to enhance these features.
The functional operation represented by block 50 shifts the facial mean luminance, i.e., the average lightness found in the general vicinity of the nose, to a preset value. In the preferred embodiment the preset value for a light skin toned person is 165, for medium skin 155, for dark skin the value is 135. The formed standardized digital image from block 50 is now represented by a storable matrix of pixel values that is stored in response to function block 52.
Figure 3A, illustrates the layout of a template 30 that is to be used with the standardized image of Figure 2. The template 30 is partitioned into 64 template elements labeled A through M. The elements are arranged in accordance with 13 corresponding features of a human face, for example, the template elements labeled A correspond to the hair feature at the top of the head and the template elements labeled G correspond to the eyes. Template elements with like labels share in the representation of a feature. The tables of Figures 4, 5A, and 5B provide a further description of the remaining template elements. Although the preferred embodiment of the invention is implemented with 64 template elements and 13 features it is to be understood that these numbers may be varied to suit the situation and are not to be construed as limiting the method of this invention. Also to be noted is that some of the regions of the template are not assigned to any element. These unassigned regions will not have their image content based on retrieval of information from codebooks. The method for assigning image content to these regions will be based on the assignment of adjoining regions which will be described below. The template size matches that of the standardized image with 56 pixels in width and 64 in height. The size of the template elements are based upon the sizes of facial features that they intend to represent. For example, G is the relative size of an eye in a standardized image and both instances of elements assigned to G are positioned in the locations of the eyes in a standardized image.
In Figure 3B, the darker shaded template elements are assigned a left-to-right flip property that will be described in detail later.
In Figure 3C, the darker shaded template elements are assigned a top-to-bottom flip property that will also be described later.
Another property of template elements is linkage. Figure 3D represents, with the darker shaded region, the location of template elements which are part of a link. In this specific embodiment, there exist 7 linked pairs of elements. The linkage is horizontal between each pair of darkened template elements, for example, G at the left of center is linked to G at the right of center. Although 7 linked pairs are shown as the preferred embodiment, linkages can occur in groups larger than two and between any set of like labeled elements.
The template 30 is in fact a sequence of data records where each record, in the preferred embodiment, describes the location, size, label, left-to-right property, top-to-bottom property, and linkage of each template element. Data records with other and/or additional factors may be created as the need arises.
The template 30 records the distribution and size of template elements. Each template element has assigned to it a codebook and a spatial location in the image. (Note that some portions of the template have no template element; these regions will be described in detail later.) The template shown in Figure 3A consists of 64 template elements composed of rectangular pixel regions. These template elements are assigned to one of 13 different codebooks (labeled A - M). The codebooks are collections of uniformly-sized codevectors of either 4x16, 8x8, 8x5, 4x10, 4x6, or 8x4 pixels. The codevectors which populate the codebooks are derived from a library of image features.
Referring to Figure 4, the labels A through M represent feature types for human faces. The human feature associated with each of the labels A-M in the label row is set forth in the row directly below the label row. The remainder of Figure 4 provides information as to the width and height of template elements for each of the associated labels along with the number of occurrences and the number of unique occurrences for each feature. A unique occurrence indicates the number of independent template elements that are linked (linked elements count as only a single unique occurrence).
Figures 5A and 5B illustrate the template element data records. These data records represent the attributes of each template element and include data record fields for the upper left hand pixel coordinates, the width, height, left-to-right flip property, the top-to-bottom flip property, the record of the linkage group, and the feature type. If the record of the linkage group is -1 then no linkage occurs. Other values of the linkage group identify the template elements of that group. For example, the top two template elements D of Figure 3D are linked and are given the same linkage group number O in the linkage group column of the table of Figures 5A and 5B.
For the following discussion reference will be made to Figures 4, 5A, 5B, and Figure 6. The feature types referenced in Figure 4 are shown in Figure 6 as collections of tiles. For example, the tile 1 within the collection for the feature type G, the eye feature, is a picture of an eye represented as an array of pixels. The other tiles 2 through 2ⁿ in the collection, for feature type G, are other pictures of eyes. In the preferred embodiment the number of tiles in each collection for each feature type is 2ⁿ for some positive integer n. It should be noted that tiles within a collection share visually similar properties as they represent the image features. A comparison of tiles from different feature types will, in general, be visually dissimilar.
Figure 7 represents an image as an assignment of template elements to tiles. Each of the template elements of Figure 7 has a number associated with it, and this number corresponds to a tile for the feature type of the template element. For example, the template element 60 is for feature type A, and has the associated tile with number of 46 in the collection of hair feature type tiles A in Figure 6. Similarly, the template element 62 for the eye feature type is numbered 123, and it corresponds to the tile with number 123 in the eye feature type collection labeled G in Figure 6. Note that template elements within the same linked group (such as the eye feature type template elements 62 and 64) have identical tile numbers. For ease of identification of linked elements, they appear in bold number printing in Figure 7.
The tile numbers assigned to each template element in Figure 7 are used to retrieve the like numbered tile from the like labeled feature type collection of tiles. The retrieved tile is positioned in the same location as the template element containing the tile number. The resulting assembly of tiles produces the mosaic of Figure 8.
Next, selected tiles are flipped. Figures 3B and 3C indicated which template elements possessed the left-to-right and top-to-bottom flipping property, respectively. The template elements with these flip properties are also indicated with the TRUE/FALSE flags in the table of Figures 5A and 5B. The tiles in Figure 8 that are to be flipped are identified by diagonal lines through the boxes representing pixels. Figure 9 represents the application of the flipping properties to the tiles in Figure 8, where all tiles in Figure 8 which correspond to the darkened template elements in Figure 3B are flipped left-to-right and all tiles in Figure 8 which correspond to the darkened template elements in Figure 3C are flipped top-to-bottom. It should be noted that some template elements undergo both flips in the transformation of the tiles from Figure 8 into the tile orientation of Figure 9 and that the flips take place within the associated element.
The next step is the formation, by image processing operations, of a final image based on the oriented tile mosaic of Figure 9. The mosaic of Figure 9 may have certain visually objectionable artifacts as a result of its construction from tiles. These artifacts can be diminished with some combination of image processing algorithms. In the preferred embodiment, a combination of well known image processing operations are applied including smoothing across the tile boundaries, contrast enhancement, linear interpolation to fill missing image regions and addition of spatially dependent random noise. The smoothing operation is described by considering the situation where three successive pixels, P₁, P₂, and P₃, where P₁ and P₂ are in one tile and P₃ is in an adjoining tile. The pixel P₂ is replaced by result of ${(P}_{1} {+ 2 *P}_{2} {+ P}_{3}) / 4.$
The contrast enhancement is achieved by determining the minimum pixel value, min, and the maximum pixel value, max, for the mosaic. Each pixel value, P_cur, of the mosaic is replaced by P_new according to the formula: $P_{new} {= 255*(P}_{cur} - min) / (max - min).$
The regions of the feature template not corresponding to any template element are filled using linear interpolation. For each region, the known values of the boundary pixels are used to calculate an average pixel value. The unknown corner opposite the known boundary is set to this average value. The remainder of the unassigned interior pixels are calculated by linear interpolation. In the preferred embodiment of the present invention, there are four such unassigned regions, each located in a corner of the feature template.
The spatially random noise, to be added, is determined by: $n(i,j) = v * sqrt((i-28)**2 + (j-32)**2) * rand$
v = noise magnitude
where i = column of the affected pixel, j = row of the affected pixel, and
rand is a pseudo-random, floating-point number in the range (-1 to 1). The value n(i,j) is added to pixel at location (i,j). If the resultant pixel is greater than 255 it is set to 255, and if it is less than zero it is set to 0. Figure 10 represents an image after processing by these operations. It should be understood that other image processing operations may be used in other situations, and the preferred embodiment should not be considered limiting.
Figure 11, illustrates an apparatus 100 on which the present method may be implemented. The apparatus 100 is comprised of a means 102 for converting a non-digital image, such as a photo print 80, or a negative image 82, into a digital representation of an image. Generally the conversion is performed in a scanner 104 which outputs signals representing pixel values in analog form. An analog-to-digital converter 106 is then used to convert the analog pixel values to digital values representative of the scanned image. Other sources of digital images may be directly inputted into a workstation 200. In the preferred apparatus embodiment of the invention the workstation 200 is a SUN SPARC 10, running UNIX as the operating system and encoded using standard C programming language. The program portion of the present invention is set forth in full in the attached Appendices A and B. Display of the digital images is by way of the display 202 operating under software, keyboard 204 and mouse 206 control. Digital images may also be introduced into the system by means of a CD reader 208 or other like device. The templates created by the present method and apparatus may be downloaded to a CD writer 210 for storage on a CD, hard copy printed by printer 212 written onto a storage card (such as a transaction card), or transmitted for further processing or storage at remote locations by means of a modem 214 and transmission lines.
Other uses for the present invention include compression of images other than portrait. Other feature types can be represented, for example, the features associated with banking checks such as the bank and account numbers along with signatures and dollar amounts, addresses and the like. Like the human face these features tend to be positioned at the same locations for each check.
While there has been shown what are considered to be the preferred embodiment of the invention, it will be manifest that many changes and modifications may be made therein without departing from the essential spirit of the invention. It is intended, therefore, in the annexed claims, to cover all such changes and modifications as may fall within the true scope of the invention.

Claims

A feature template comprising:
a plurality of template elements each representing a feature of an object; and

data representing the attributes of each template element.
The feature template according to Claim 1 wherein the template elements share representation of a feature of an object.
The feature template according to Claim 1 wherein the data representing the attributes of each template includes data indicating the linkage of one template element to another template element.
A feature template comprising:
regions representing features of objects in code form; and

code bits representing the orientation of each template element with respect to other template elements of the same feature type.
A method for forming a feature template comprising the steps of:
establishing the dimensions of a feature template to accommodate a standardized image;

partitioning said feature template into a plurality of feature types to accommodate like features in the standardized image;

assigning at least one template element to each feature type; and

recording the position of all assigned template elements within the dimensions of said feature template to facilitate the reconstruction of the so formed feature template.
The method for forming a feature template of an image according to Claim 5 wherein the standardized image is formed by the steps of:
acquiring an image in digital form; and

aligning and scaling features in the acquired image to pre-determined parameters.
The method of Claim 5 or 6 further comprising the step of:
associating a vertical symmetry property with at least one template element.
The method of any of Claims 5 to 7 further comprising the step of:
associating a horizontal symmetry property with at least one template element.
The method of any of Claims 5 to 8, wherein each template element represents a feature of an object in the standardized image such that a representation of the standardized image is formable from the feature template.
A storage media for use in a digital signal processing system having recorded thereon the feature template formed by the method of Claim 1 for facilitating the formation of images for display.