an acceptable photo album or photocollage can be created automatically from customers &# 39 ; exposed film , negatives , or digital images . referring to fig1 which is the method as envisioned by the present invention designed to automatically produce photocollages including albums 17 , cds 15 and other image - based keepsakes 11 . the process of transforming the supplied image material into a photocollage is referred to as story preparation services . story preparation services applies the necessary amount of image understanding embodied in a collection of processing modules executed in a non - linear sequence to create a story representation of the images . in a preferred embodiment these processing modules include : collecting , sorting , culling , annotating , grouping , enhancing , associating and , composing . these steps can be performed in the order listed or they may be rearranged in different sequences depending upon the desired output product . the production of a particular photocollage product may include one , two or , more of the available steps . which steps are used and in what sequence is determined by the system based upon the desired product . it is important to remember that the present invention envisions providing sufficient image understanding of the subject images with the ultimate goal of teaching the system to understand that there are pervasive themes that exist within various sets of images and recognizing these themes thereby creating an image story product , or photocollage . as shown in fig2 the collection step begins when a customer , having completed picture taking for one or more events delivers one or more exposed film strips or cartridges 10 , digital still camera memory cards 12 , photographic prints 6 or video camera media 8 to a processing facility 14 . at the time that the customer delivers the exposed film cartridge ( s ) to the processing facility the customer &# 39 ; s identity is recorded and associated with the suite of film cartridges and other image sources . alternatively the customer identity may be encoded on the film by exposing a machine - readable sequence of marks along the edge of the film or by using the magnetic coating on the advanced photo system film . the conventional film processing takes place with conventional correction of exposed photographs to balance color and brightness and contrast . the exposed film images are chemically processed 16 to generate an optical image . these optical images are then scanned 18 to produce a high - resolution digital file that is archived in a data store 20 . in general , to produce a high - resolution printed image of 8 × 10 inch size , a resolution of 1538 × 1024 pixels is required . digital still camera images from digital still camera memory cards that are delivered to the processing facility are digitally processed 22 to produce an equivalent digital image file of similar size and stored on the data store 20 . analog video camera images on media 8 delivered to the processing facility are digitized and processed before storage on the data store 20 . at the conclusion of this step in the process there exists a collection of digital image files associated with a customer by means of a customer identification . this collection of digital image data , or pixel data 32 , is now available as a source of data for the story services processing modules . data attached or associated with individual images or groups of images provided by the customer such as date , time , location , sound , camera identification , customer identification , exposure parameters , advanced photo system ix data , are all examples of meta - data . this meta - data 34 is stored on image store 20 and associated with the available pixel data 32 . in addition the customer selects the desired output product or product type from a listing of available choices . this selection may be indicated by the customer marking the photo processing bag 24 , verbally indicating the desired product to a clerk 26 or by input at a computer terminal or kiosk 28 . the order description 36 generated from any one of the indicated sources provided by the customer is stored on the system and is associated , by means of a customer identification , with an existing customer profile 30 in the data store 20 . at the conclusion of the collection step the pixel data , meta - data , customer profile and order description are available as input data for the subsequent processing steps . these steps can be performed in the order listed or they may be rearranged in different sequences depending upon the desired output product . the production of a particular photocollage product may include one , two or , more of the available steps . which steps are used and in what sequence is determined by the system based upon the desired product . following the collection step but before subsequent processing steps are performed , the specific processing to be applied to the collected set of image material is determined in a process goal generation step 37 and embodied in a set of photocollage processing goals 38 . for each of the output products requested the system determines an optimum sequence of processing steps to be applied . the processing of the output product or products will follow this system determined processing sequence , unless the sequence is modified by one of the processing modules . the processing output for each module is stated as one or more processing step goals . the processing in each step uses the stated processing step goals to control the logic , rules , and parameters applied during the processing of the collected image material to satisfy the stated processing step goal or goals . in addition to accomplishing processing goals each processing step can also create goals for itself or future processing steps . new goals , which result from executing a particular processing step , reflect necessary adjustments of desired parameters because the original goal is impossible to achieve or the processing step may generate new goals for subsequent processing steps . the initial set of processing goals 38 is determined by retrieving a default processing description from a database of available products 40 maintained by the system . once the initial set of processing goals is determined the photocollage processing is initiated by the system . as described , the automatic processing of photocollages is carried out in processing modules employed to perform these image understanding and interpretation steps . each processing module relies on data from a number of sources in order to satisfy the processing goal . each processing module has access to five sources of data : pixel information contained in the individual images , meta - data attached to images or groups of images , the original product or service order description , the list of active processing goals maintained by the system and , a customer profile containing information about the customer . the pixel data 32 for each image provides each module with the opportunity to apply image processing and image understanding algorithms . the meta - data 34 as used herein refers to the information attached to the individual images and to groups of images will contain information about the image or group of images which originated at the time of capture or was generated in prior processing of the image or group of images . date , time , location , sound , camera identification , customer identification , exposure parameters , advanced photo system ix data , are all examples of meta - data that are , in a preferred embodiment , be attached to the original input images . the original product or service order description 36 contains the specific product request from the customer and any notes that were captured in the ordering process . at any time in the processing of the image or group of images the system will have a list of active processing goals 38 . active goals are the processing goals for each processing module , which have yet to be accomplished by the system . at the start of processing this list will include the specific product or products requested by the customer , along with the translation of these product goals into system and module goals . the customer profile includes both factual and uncertain information related to the customer . the factual data would include general items such as name , address , names and ages of individuals in the household , important dates , anniversaries , product preferences and purchase history . in addition factual data such as face recognition feature vectors of the immediate family and extended family , voice training sets , handwriting samples , would also be included in the customer profile database . uncertain information would include processing goals recommended by processing modules from previous order processing , image understanding assertions about the contents of the image or groups of images which have not been verified or other unverified information or assertions . information and data contained in the customer profile is updated with every order processed to reflect changes in order preferences , order history and update uncertain information . in order to supply the system with the necessary amount of image understanding required to arrange the images into set in accordance with themes relating to predetermined criteria that must be provided for the system to have the capability to identify attributes within the images . for example , in a preferred embodiment , if a customer requests a birthday photocollage the system will retrieve the default processing goals which indicate that the steps of collecting , sorting , culling , annotating , and composing will be involved in the processing of the requested product . in addition the processing goals for each of the modules will reflect the default attributes necessary to process a birthday product . in this example the sorting module processing goals will include a sort by date goal and a sort by content goal . the sort by date processing goal is further refined to sort the images that occur on or near a target list of dates which are determined from a list of known birthdays retrieved from the customer profile into the photocollage . in a preferred embodiment each processing module performs processing on two distinct levels : objective and subjective . the objective processing deals with factual information about the images or groups of images or with data calculated deterministic algorithms . examples of factual data include the size of the image , capture parameters , histograms , image transforms , etc . commercially available software programs such as adobe photoshop ® and corel draw ® process images using objective data . subjective processing deals with information that is uncertain or data is the result of non - deterministic algorithms . often subjective results carry with them a confidence factor which allows subsequent processing steps to interpret the results . usually subjective results occur when attempting to determine abstract information about an image or group of images . examples of this type of the processing would be face detection , face recognition , facial expression determination , location determination , assertions , interpretations , etc . commercially available software programs such as faceit © by visionics corp . process images to associate faces in images with names and other information . some processing modules process only subjective information , others process only objective information and still others process both . as shown in fig3 the processing of a photocollage is directed by the system using the active processing goals 38 , 48 , and 49 . because the processing goals are non - deterministic , vary by requested product and may be modified during the processing there exist a large number of possible processing sequences . the progression of processing applied to the collected image material is applied in sequential steps . each step implements a single processing module that is intended to satisfy or partially satisfy an active processing goal . at each step in the process any one of the available processing modules ( culling 62 , grouping 64 , enhancing 66 , annotating 68 , associating 70 , or composing 72 ) may be executed . at the conclusion of the processing of a particular step the active processing goals 38 that existed before the step processing are updated to reflect the changes in goals that resulted from the execution of the processing step 52 . these updated active processing goals 48 serve as the input processing goals for the subsequent processing step 53 . each of the available processing modules will be described . in addition to the processing goals 38 , 48 , and 49 , the processing modules at each processing step have access to four sources of information : pixel data 32 contained in the individual images , metadata 34 attached to images or groups of images , the original product or service order description 36 and , a customer profile 30 containing information about the customer . this process can iterate as many times as necessary to complete the desired product objectives . the processing of the collection of digital image material is reviewed to remove unwanted images . this step is called culling . culling is performed on images that are deemed unwanted due to poor quality , or if there are several images that are similar in composition and only one is desired . to accomplish this culling process involves the computation of several image based metrics using the pixel data 32 . these metrics fall into two basic categories : technical quality measures and abstract feature metrics . the technical quality measures would involve calculations to assess the overall sharpness of the image , exposure quality , grain quality of the image . algorithms used to calculate technical quality measures are common in the art , examples would include : “ estimation of noise in images : an evaluation ” by s . i . olson in , 55 ( 4 ), 1993 , pp . 319 - 323 and “ refined filtering of image noise using local statistics ” by j . s . lee in computer vision graphics and image processing , 15 , 1981 , pp . 380 - 389 . images are then given an overall rating of technical quality based on the technical quality metrics . the overall image quality metric is then compared to a threshold associated with the processing goal . images whose quality rating falls below the threshold are flagged as unwanted . the specific threshold applied to a given image or set of images is parametrically determined using the processing goal 38 and the customer profile 30 as inputs . in addition to the determination of specific objective image features , abstract subjective image features are also calculated . these subjective measures of the image allow the module to cull unwanted or unnecessary images . a typical example of images which require culling occurs when several images in the group are similarly composed and contain nearly identical content such as a group photo which is repeated several times . in order to choose the best of this group of images the digital image is analyzed to determine a quality metric composed of both objective and subjective quality features . the basis for this subjective assessment would be the presence or absence of faces , the identification of the faces in the image , the number of faces present , if the eyes are open on each face , if the subject is smiling , or orientation of the face to the camera . examples of algorithms used to determine subjective features of an image are described in proceedings of the ieee computer society conference on computer vision and pattern recognition , june 1997 . these subjective features provide clues about the subjective quality of the image . the objective and subjective features calculated for each image are combined and the images are ranked according to the relative importance of each feature analyzed to the processing goal 38 and which matches the stored customer preferences 30 . the ranking , because it is partially based upon subjective information also carries a probability weighting factor . the combination of the ranking and the probability weighting factor is used to assert a decision about the quality of the image . this quality assertion is retained with the image as metadata 34 and is passed to the next processing step . in the grouping step images are grouped according to criteria derived from the analysis of the customer profile 30 , the active processing goals 38 and the requested product or service 36 . the goal of grouping images is to associate images that are a part of a common theme or story in the mind of the customer . typical groupings derived from customer profiles 30 would be to associate images according to the preferred organization scheme of the customer . the attributes of typical organization schemes would include organizing by events such as birthday party , vacation , holiday , graduation , organizing by time , and organizing by people . customer requested products 36 could also dictate grouping parameters in accordance with the product design . examples of these grouping schemes include grouping by location in a theme park , by common life - cycle event such as graduation , by image content type such as outdoor , indoor , nature , person , group , etc . a variety of software algorithms exist which are applicable to grouping . one class of software algorithms operates on metadata 34 associated with the image where the grouping process is similar to searching a media database . several commercial examples of media databases that allow searching are picture network incorporated , publishers depot ( www . publishersdepot . com ). a second class of algorithms employed for this grouping process would include image processing algorithms which identify objects and feature sets within an image by way of the pixel information 32 . these objects could represent typical cultural icons such as birthday cakes , christmas trees , graduation caps , wedding dresses , etc . an example of such algorithms is reported by j . edwards and h . murase , “ appearance matching of occluded objects using course - to - fine adaptive masks ” in proceedings of the ieee computer society conference on computer vision and pattern recognition , june 1997 , pp . 533 - 546 . the product order information 36 can also be used in this grouping by simply stating the event or location or person on which to base the final product . the processing goals 38 can also be used as a grouping tool where the specific products offered mandate a specific grouping . the annotation step seeks to generate annotation that can be composed on the story product or photocollage . annotation is designed to provide context information about the image or group of images and assist the customer in communicating the story surrounding the images . a very common form of annotation is text associated with each image and with a group of images which explains the “ who , what , when , where , and why ”. such context information is generally derived from metadata 34 generated in previous processing steps , user profiles 30 or , image understanding algorithms applied in the annotation module . “ who ” information may be determined using face recognition algorithms applied to the pixel data 32 tuned by the training data contained in the customer profile 30 . using a commercial product such as “ facelt ” by visionics , a database of known faces can be retrieved from the customer profile 30 and used to guide the face recognition software . “ what ” information can be asserted by correlating date and time of the image capture available in the image metadata 34 with key dates contained in the customer profile 30 . in addition more “ what ” data can also be asserted by looking within the image 32 for specific cultural icons , derived from object recognition algorithms , such as birthday cakes , christmas trees or , graduation robes . “ when ” information for annotation is easily determined using metadata 34 such as date and time recorded at the time of capture . “ where ” information may be provided from the capture device integrated with gps ( global positioning system ) technology and then added to the metadata 34 for the image or it can be guessed at using image understanding algorithms which correlate known location scenes with the image content from available images 32 . “ why ” information is very difficult to determine without some input from the image owner via the product order information 36 . the annotation , once determined , can be rendered as text , graphics , images , video or sound and associated with the individual images or with the group of images in either a traditional photocollage or digital product . the image enhancement module applies image processing to improve the image for the intended story based purpose . two categories of image enhancement are considered for each image . basic image enhancements are applied to all images , as necessary , would include : red - eye removal , brightness and contrast adjustment , color adjustment , and crop and zoom to improve composition . image enhancements are applied directly to the pixel data 32 and recorded in the image metadata 34 from the images . a second category of image enhancement is applied to the images in order to enhance their use within a story context to communicate the emotion or to support the story theme as described in the processing goal 38 . for instance , selected images from a wedding story would be softened using a blur algorithm or the colors of an image could be made to match those found in a comic strip . the application of this type of enhancement processing is either specified in the product description 36 generated from the customer profile 30 . all of these operations are available in image editing software programs such as adobe photoshop , with the exception of red - eye removal and red - eye removal can be accomplished via the kodak imaging workstation and other software such as picture - it from microsoft . some product specifications will include a processing goal to associate external content that is relevant to the product and the image content . this associated content provides additional context and interest in the final rendered product . external content takes several forms including weather reports , newspaper headlines , stock photographs , advertisements , historical references , travel brochure copy , popular music clips , video segments , etc . in order to locate and find appropriate content for the image story / photocollage the metadata 34 attached to each image and image group and is interrogated to derive searchable topics or specific information located in the customer profile 30 . these search topics would focus on the when , where , what aspects of the image story . once a series of potential search topics have been assembled they are formatted into queries and searches are performed on a variety of databases . the results of these content queries is then refined by applying priority rules from the product description database and customer preferences stored in the customer profile 30 . these types of searches are common in information databases such as yahoo ® on the internet ( www . yahoo . com ) or from dialog corp . if there are items within the image that can be identified with specific events ( such as a wedding or birthday ) or if a person can be identified from imaging algorithms , the pixel information 32 is used to determining the types of additional content that are added . the processing goals 38 dictate the forms and types of associated content that is added . the product order information 36 is also a source of information regarding the association of content by a special request on the form ( such as a theme park or wedding motif ). the layout processing module places the image data , annotations , and associated content into an output ready format . this layout step must be repeated for each product that was requested by the customer and for each layout goal that was generated during the processing steps . the main task involved in the layout of a multi - page photocollage is the determination of which images are to be placed in specific locations on specific pages . in addition the external content which has been associated with the individual images or groups of images must be composited onto the page . the layout process may be accomplished in numerous ways . one means of performing this processing step is to employ a parametric photocollage description . this description specifies the general structure of the photocollage but is not sufficient , of itself , to specify a final photocollage product . the description file includes photocollage features such as number of sections , size and format of individual pages , maximum and minimum number of pages allowable , maximum and minimum number of customer images allowable per page , section descriptions , cover designs , location of allowable stock content , etc . by employing a photocollage description file a variety of tailored photocollage products may be designed incorporating features specific to the design goals . the layout step begins by reading the photocollage description file to initialize a layout algorithm . the layout algorithm then works page by page and section by section to apply specific layout rules guided by the values in the photocollage description file . a variety of page / section layout algorithms could be employed which embody different design philosophies for photocollage products . the final photocollage product can be rendered on a variety of output media types which would include paper , fabric , as a digital file or on any one of a number of digital media forms such as a cd . the processing goals 38 are used to determine the capabilities of the specific devices being used . customer profiles 30 are used to determine color preferences , layout preferences , and design considerations . metadata 34 is used to determine the placement of associated content with specific images . in a preferred embodiment , the first cut version of the photocollage is rendered as a scaled replica of the final version . this scaled replica is also identified using a template id in the form of a bar code . fig4 shows the system diagram for story preparation services . input to the system comes from personal computers 90 ( using albuming software such as microsoft picture it , family base from micro dynamics or a myriad of others ), an interactive kiosk , via the phone or over - the - counter order forms 84 , a retail outlet 82 with links to the system , or digitization services 80 specializing in converting analog material into digital information . the information needed to perform the story services is communicated via traditional means including mail , phone , modem , internet or other on - line services . the components required by such as system include a digitization system for pictures , video and audio 104 , a storage facility 102 , an operator interface 96 , algorithms and software 98 for the analysis of the data and to provide the necessary steps to complete the product , and an output delivery system 100 for printing or other media . fig5 is a flow chart showing the steps performed by the present invention towards preparation of a photocollage that allows alterations after automatic creation . the automatic creation of a first cut version of a photocollage proceeds as described above . after the first cut version is complete , the album is reviewed by the customer to determine if any changes are desired . in a preferred embodiment the user is also supplied with a set of scaled image stickers and templates to be used to modify the first cut version of the photocollage . the sticker images are generated at the time of the original photocollage processing as described below . these stickers and templates are illustrated and described in fig7 and 9 and the discussion related , thereto . a customer wishing to construct additional pages for a photocollage or to modify the page layout of the first cut version selects one or more images by peeling the sticker image from the sticker sheet 118 and placing it on the page layout form 160 as shown in fig9 . the page layout form 160 includes one or more scaled graphic depictions of the final page each comprising a unique page layout . in one embodiment each scaled page representation includes one or more graphic image boxes each of which is an allowable location to place a sticker . different scale page layout representations depict the available album page layouts made available by the service . normally this variety of page layout choices would include variety along the dimensions of : the number of images per page , the resulting size of the images on the final printed page , arrangements accommodating both portrait ( vertical ) and landscape ( horizontal ) captured original images , different final printed page sizes , provision for annotation text , different size original images including standard 35 mm , advanced photo system classic , hdtv and panoramic images and , digital still camera formats . the scale of the sticker images and the scale of the graphic image boxes on the page layout order form do not have to be the same . if the page layout form image box is larger than the sticker image the image is enlarged to fill the box when producing the final printed album page . if the page layout form image box is smaller than the sticker image the image is reduced to fill the box when producing the final printed album page . in this way a variety of final output sized images can be made available to the user without the need to produce multiple sizes of sticker images . the page layout sheet and cover sheet would have identifiers 164 on them so that the page type can be identified during the scanning process . bar code or alphanumeric characters can be used for this identification . other means to alter the placement of images in the photocollage can also be used including the use of a marking means using an “ x ” over the image to indicate deletion and a circle around the image and an arrow showing where the image is being moved on the photocollage . photocollage templates can also be modified by other means such as the selection via id numbers representing layouts in a catalogue showing the layout choices . after alteration , the modified photocollage is brought or sent to a location , such as a retailer , to be scanned and printed in final form . fig6 is a flow chart showing the steps performed by the present invention ; towards preparation of a photocollage that is in a digital file format that can be sent across the internet . the process is similar to that shown in fig5 except that all steps are performed on a digital computer with images being transmitted as digital files . it is envisioned that any alterations , both for changing the images within a page and changing the templates describing the layout of the pages , are performed by employing traditional software methodologies like drag - and - drop or choosing from a list for replacement of images and templates . in this digital case , the information needed to convey the changes can be sent electronically to the retail site for printing of the final photocollage . scaled image stickers used by the consumer to correct the first cut version of the photocollage are generated by using the original high - resolution images archived in processing facility 14 as seen in fig2 . this archive of digital images would , in most cases , comprise a software database application and a large collection of optical or magnetic storage media . in order to allow for the future association of the high - resolution images with their corresponding reduced resolution thumbnail images , each image is assigned a unique identifier . there are a variety of means , known in the art , for the generation of this unique identification . one such means is disclosed in townsend and shaffer , u . s . pat . no . 5 , 784 , 461 , in which a combination of the customer identification information and the photofinishing location , equipment , and date and time are combined . other means of unique identification could be employed so long as they provide a one - to - one association of a person to a single image . the assigned unique image identification is associated with both the high and reduced resolution versions of the image . the unique identifier embodiment can be any of either : embedded data contained within the pixels themselves , invisible ink , visible codes , histogram , color information , or the like . the reduced resolution images are further processed to generate a formatted array or sticker sheet 118 as seen in fig8 . referring to fig8 the sticker sheet 118 also includes an alpha numeric human readable image identifier 119 which allows the user to locate the corresponding photographic print of the sticker sheet image . in addition a unique machine - readable image identification is also included in the printed sticker sheet . the machine readable image identification could take the form of a bar code , machine readable characters or any one of a number of data - encoding patterns . a preferred implementation this unique machine readable image identification is to embed it directly into the pixels of the thumbnail image using signal processing techniques . an example of such techniques can be seen in daly et al . ( u . s . patent application ser . no . 08 / 565 , 804 ). the method of embedding digital data in an image can be best considered in two stages ; an encoding process and a decoding process . first , a multi - level data image is generated from digital data . the multi - level data image as described in detail below is an image having a constant background value and an array of spots on the background representing the digital data . the data image may be produced using digital image processing techniques , or may be produced optically for example by exposing spots on a photographic film . next , the data image is convolved with an encoding carrier image to form a frequency dispersed data image . the encoding carrier image is preferably a rotationally symmetric , low amplitude , high frequency pattern employed to spatially disperse the data image to mask its visibility when added to the source image . preferably the convolution is performed on a digital computer using a well known fourier transform method and digital versions of the data image and the encoding carrier image . the convolution may also be performed using a direct convolution algorithm on the digital computer . alternatively , the convolution may be performed optically using well known optical convolution techniques and optical versions of the data image and the encoding carrier image . the frequency dispersed data image is then added to the source image to form a source image with embedded data . as described in more detail below , the addition may be performed either optically using traditional photographic processes , or digitally using a digital computer and digital versions of the source image and the frequency dispersed data image . if the addition is performed digitally , a hard copy version of the digital source image having embedded data may be produced using a digital printer such as a thermal , ink - jet , electrophotographic , or silver halide printer . once the generation of the final photocollage from the modified first cut version of the photocollage has been accepted or modified by the user the modified version is rendered into a final photocollage . these modified photocollage orders are scanned to generate a final version processing order . if the modified photocollage contains image stickers the photocollage processing identifies the original high resolution data by recovering the embedded digital identification data . the digital identification data is recovered from the source image having embedded data by first cross correlating the source image having embedded data with a decoding carrier image to produce a recovered data image . preferably , the decoding carrier image is identical to the encoding carrier image and the cross correlation is performed by a digital computer on digital versions of the source image having embedded data and the decoding carrier image . if a hard copy of the source image having embedded data is used , the hard copy is first scanned as described in detail below to produce a digital version . alternatively , the cross correlation may be performed using well known optical techniques and optical versions of the source image having embedded data and the decoding carrier image . finally , the digital data is extracted from the recovered data image . preferably , the digital data is extracted from a digital version of the recovered data image using a pattern recognition process in a digital computer . alternative methods for encoding data within an image can be found in issued u . s . pat . nos . : 5 , 636 , 292 ; 5 , 128 , 525 ; 5 , 285 , 438 ; and 5 , 126 , 779 which can also be used with the present invention . referring again to fig2 after scanning the modified first version photocollage and template forms the processing facility uses the embedded image identification to generate the list of images needed to generate the final photocollage . using the list of images the processing facility 14 finds and retrieves the corresponding high resolution images that were previously stored in the image storage 24 which are associated with the unique image identification numbers that were supplied by the scanned layout sheets 160 . these high - resolution images are then assembled into a photocollage using a page description language or template following graphical rules . these graphical rules incorporate customer preferences such as size of the pages , background styles and colors , etc . in this page layout process it may be necessary to zoom and crop the images to optimize the layout or to match customer preferences . fig7 illustrates the process steps that are performed from developing photographs to generation of photocollages by the processing facility of fig2 . the customer brings in exposed film 142 or a memory card from a digital camera 143 containing images that are to be made into a photocollage . the photofinisher generates traditional prints 146 for the customer . in the case of film , the film is scanned 145 to create a digital image . once acquired , the digital images are assigned a unique id 149 and placed into digital storage 150 . a sticker sheet 118 and a first cut version of a photocollage is generated 148 and sent to customer for review and modification . the sticker sheet 118 , first cut version of the photocollage and the layout pages 160 can be sent to the customer along with their prints . if modifications to the first cut photocollage are desired the customer selects images from the sticker sheet 118 places them on the first cut photocollage or on new page layout sheets or cover sheets 160 as they wish 151 . they would also select the type of product 153 from a standard order form supplied with sticker sheet 118 . the customer then returns the modified first cut photocollage , new cover and layout sheets 160 to the retailer 155 where the pages are scanned with the cover and layout sheet scanner 126 to create a photocollage order . the photocollage order is processed to determine which images are to be used in the photocollage , the cover , the layout and the product type 157 . the unique identifiers from the reduced resolution images are then associated with the images stored in the online storage facility and these full scale images are retrieved 152 . the photocollage is then constructed and printed 154 and sent to the customer 156 . depending upon the method employed for rendering the image identification number within the sticker sheet a variety of image processing techniques would be employed . in the case of human readable image identification numbers optical character recognition software is employed that translates the scanned image of the characters to a digital number representation . in the case where the image identification was hidden within the pixels of the reduced resolution image , a reverse of the data embedding signal processing technique is employed . the images are processed to remove any rotation and scale variations introduced in the printing and scanning steps . the result of this processing is to generate a list of the identification numbers of the images which the customer desires to have placed into the photocollage . the invention has been described with reference to a preferred embodiment however , it will be appreciated that variations and modifications can be effected by a person of ordinary skill in the art without departing from the scope of the invention .