Abstract:
According to one embodiment, a method is shown for improving image capturing ability, the method comprising electronically analyzing captured images to determine variations from accepted image criteria, electronically analyzing the determined variations to determine a pattern of determined variations, and providing an indication of relative image capturing performance.

Description:
FIELD OF THE INVENTION  
       [0001]     This invention relates to image capturing systems and more particularly to systems and methods for improving a user&#39;s image capture abilities.  
       DESCRIPTION OF RELATED ART  
       [0002]     One of the great advantages of digital photography is that the user can see the image in the camera&#39;s display, both before and after the exposure. Unfortunately, users continue making the same mistakes when capturing an image, resulting in amateurish and unsatisfying shots. Some of the most common mistakes are: dividing the image in half vertically with the horizon; centering the subject horizontally; making the shot too symmetrical; subject too far away; crooked horizon; no foreground framing; and back-lit scene.  
         [0003]     Casual point-and-shoot photographers make these same mistakes over and over again. They are often unsatisfied with their photographic efforts, but do not know the techniques to improve their image capturing ability.  
       BRIEF SUMMARY OF THE INVENTION  
       [0004]     According to one embodiment, a method is shown for improving image capturing ability, the method comprising electronically analyzing captured images to determine variations from accepted image criteria, electronically analyzing the determined variations to determine a pattern of determined variations, and providing an indication of relative image capturing performance.  
         [0005]     According to another embodiment, there is shown a system for providing image improvement assistance, the system comprising storage for storing captured images, analyzation capability for examining stored images against a set of image parameters, and reporting capability for providing image improvement assistance to a user based upon the analyzation of at least one stored image.  
         [0006]     According to still a further embodiment there is shown a method of providing network accessible services, the method comprising, over a network common to a plurality of potential users, receiving at least one image from a user, comparing received ones of the images against image criteria, and providing image improvement suggestions to the user of the network, the suggestions based, at least in part, by the comparisons. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0007]     For a more complete understanding of the present invention, reference is now made to the following descriptions taken in conjunction with the accompanying drawing, in which:  
         [0008]      FIG. 1  is a flow diagram of operation according to an embodiment;  
         [0009]      FIG. 2  is a flow diagram of steps involved in analyzing an image in a teach or a fix mode according to an embodiment;  
         [0010]      FIG. 3  is a flow diagram of an analysis module according to an embodiment; and  
         [0011]      FIG. 4  is an embodiment of a computer system interfaced with a digital camera to provide a system embodiment for using the present method. 
     
    
     DETAILED DESCRIPTION  
       [0012]     In one embodiment, a software application grades a set of photos to identify common composition mistakes. The user is given feedback and suggestions for improvement. The system and method identify and troubleshoot common camera/user problems, and provide help to assist the user in correcting these problems in subsequent images.  
         [0013]     In another embodiment, the system and method provide positive feedback on well-composed or well-executed images.  
         [0014]     In still another embodiment, the application has a “teach mode” and a “fix mode”. The teach mode provides multiple layers or levels of instruction, while the fix mode provides one or more alternatives that the user can select, automatically correcting the image.  
         [0015]      FIG. 1  shows flowchart  10  which is an overall view of operation according to an embodiment. Process  101  optionally establishes either teach mode  102 A or fix mode  102 B. In actual use, either or both modes may be used, if desired. Teach mode of the illustrated embodiment provides instruction to improve images and eliminate errors. Fix mode of the illustrated embodiment provides suggestions for the user to choose to improve the image, as well as actually correcting the mistakes. Either selection in the illustrated embodiment leads to the fetch and display next image process  103 . Once the image is fetched and/or displayed, it is analyzed at process  20  with different results available for different modes. Point A, just before process  20 , refers to  FIG. 2  which provides a detailed description of steps in analyzing the image in process  20  according to the embodiment.  
         [0016]     Point B, just after process  20 , indicates a return from the processes of  FIG. 2 . After analysis, process  104  queries whether all images have been examined. This may be accomplished by comparison to a user input of the number of pictures, a user response to a question, a computer generated count of images, or by some other method of comparing the current image number to the total number of images. If the current image was not the last image to be examined, the system returns to process  103  to fetch and display the next image. When all images have been examined, the score and final feedback will be displayed audibly, visually, textually, or in some combination thereof by process  105 . Final feedback may include comparing scores (grades) with previously saved scores to provide a comparison with previous attempts merely providing relative improvement data. Scores may also be dedicated to a particular user to provide a comparison between users and to personalize comparisons to each user.  
         [0017]     The aforementioned scores may provide an indication of relative skill level and/or may provide details with respect to areas of strengths and/or weaknesses. For example, a score may indicate a user has generally properly framed subject but has not achieved proper lighting in many of the images. Additionally, or alternatively, a score may include a summary ranking, such as a letter, number, or title (e.g., professional, amateur, etc.) to provide a quick guide to a users current ability and/or progress.  
         [0018]     It should be appreciated that the foregoing steps of  FIG. 1  and associated feedback can be provided via a computer or a digital camera, or combinations thereof. For example, a digital camera may operate under control of an instruction set as described herein to provide real time guidance, instruction, correction, and/or feedback with respect to photos being taken by a camera user. Additionally, or alternatively, some or all of the foregoing may be implemented on a computer system for post photography processing. Such an embodiment might be desired in addition to a realtime camera embodiment to compile greater historical information and better guide a user in improving their photographic skills. Of course, a camera and computer may interact to facilitate improvement, such as by the computer determining a user&#39;s deficiencies and programming the camera to correct for some or all such deficiencies.  
         [0019]      FIG. 2  shows flowchart  20 , which is a detailed description of an embodiment of the steps involved in analyzing the image in teach or fix mode. Point A, at the start of the flowchart, refers to  FIG. 1 , just before process  20 .  FIG. 2  is a depiction of the processes that occurs in one embodiment of process  20 . After being sent to process  20  of the illustrated embodiment, process  30   n  queries which analysis the user would like to invoke. In this example, horizontal analysis is the default setting. The horizontal analysis of process  30  and/or the other optional analysis of processes  30   a ,  30   b ,  30   c , or  30   d  may be chosen. Optionally, the system can select one or more of these processes, such as based upon an internal analysis.  
         [0020]      FIG. 3 , which will be discussed hereinafter, further describes the steps of the horizontal analysis of process  30  to provide but one example of image processing as may be performed according to the present invention. Process  30   a  optionally performs one or more other analysis (for example, a red eye check by finding the eyes and testing the coloring found there for high levels of red coloration). Finding the eyes in the picture may be found by comparison to a database of pictures or by some other method that estimates the placement of the eyes. Process  30   b  performs optional vertical analysis. This may be accomplished by dividing the picture into some number of sections vertically and determining if there are dominant objects in each divided section. Process  30   c  performs optional focus analysis. This may be accomplished by comparing the spatial contrast of various regions in the scene to determine their sharpness. Process  30   d  performs optional lighting analysis. This may be accomplished by checking the intensity of the color levels in the image against a set range. These and additional optional analyses may also be performed with the addition of software modules that allow for greater analysis as the user gains expertise and chooses to add additional testing routines. For example, one or more analysis modules may be implemented in a host system, e.g., the aforementioned computer or digital camera, to provide desired and/or appropriate analysis. According to one embodiment, modules providing analysis with respect to common beginner or novice photographer errors may be initially supplied for use. Additional individual modules and/or combinations of modules may be subsequently added, such as to provide analysis appropriate to the advancing skills of the photographer, to analyze particular subject matter and/or artistic aspects of a photographer&#39;s pictures, to correspond to particular equipment and/or media (e.g., lenses, filters, film speed, etcetera), and the like. Various ones of the aforementioned modules may replace previous modules while other ones of the aforementioned modules may supplement modules already being utilized.  
         [0021]     Returning now to  FIG. 2 , after initial analysis, process  200  checks across a picture set to determine a pattern of repetitive “errors.” For example, if a similar fault, (i.e., a violation of a rule) occurs in several pictures, it can be assumed that the user is not knowledgeable about the rule and this such repetitive pattern will be marked as a violation or error. Note that for different “rules” the tolerance for violations can be different, if desired. If there are no repetitive errors, or the number of errors have been reduced over previous picture sets, results query  201  is positive (good). If the result is good, positive feedback is provided audibly, visually, textually, or in some combination by process  204  through the computer or digital camera. The score is increased by process  210 , and if no further analysis is to be conducted (process  211 ) processing proceeds to point B, at the end of the flowchart, returning to  FIG. 1 , just after process  20 . Positive feedback may reference improvements in individual pictures and improvements in the overall score of a user or in a set of pictures.  
         [0022]     Note that “errors” in the above context are variations from accepted criterion and the degree of variation that shows an error can be adjusted, if desired. Also note that variations from normal can be determined on a picture by picture basis, if desired.  
         [0023]     If the result from the query at process  201  is bad, process  201   a  of the illustrated embodiment again compares across the picture set to check for camera problems that may be indicated by recurring problems over a set of pictures. Process  201   b  queries the result, and process  201   c  provides a suggestion audibly, visually, textually, or in some combination through the computer or digital camera for improvement of the camera problem if one has been found.  
         [0024]     If no camera problem is found at  201   b , or after the suggestion has been made for correction of one that is found at  201   c , process  202  fixes the image under user control, and process  203  displays the fixed image. Process  205  next queries the user as to whether the fixed image is better. If the fixed image is better, the score is increased by process  210  and, if no further analysis is to be conducted, point B returns to  FIG. 1 , just after process  20 . If the query at  205  finds that the fix is not better, the mode is queried by  206 . Fix mode results in process  208  replacing the image with the “fixed” image and process  209  decreasing the score before point B returns to  FIG. 1 , just after process  20 . Teach mode at process  206  causes a suggestion to be provided audibly, visually, textually, or in some combination through the computer or digital camera by process  207 . Then the score is decreased by process  209 .  
         [0025]     Process  211  queries whether the user desires to perform additional analysis. If the user desires additional analysis, e.g., analysis as performed by any of processes  30   a  and  30   d , the method of the illustrated embodiment returns to block  30   n  and repeats the method shown in  FIG. 2  for that analysis. If the user does not desire additional analysis, the method continues to point B and returns to  FIG. 1 , just after process  20 .  
         [0026]      FIG. 3  shows flowchart  30 , which depicts an embodiment of the horizontal analysis referenced by process  30  in  FIG. 2 . Although details with respect to horizontal analysis of process  30  are provided herein, it should be appreciated that image processing to provide additional, or alternative, image attribute analysis may be implemented by one of skill in the art, many using steps corresponding to those shown in  FIG. 3 .  
         [0027]     In the embodiment illustrated in  FIG. 3 , an image is scanned by process  301  and the horizontal features are identified by process  302 . The horizontal features are queried at process  303 . If none are found, a “good” response of the horizontal analysis system is returned to process  201  of  FIG. 2 .  
         [0028]     If horizontal features are found at process  303 , process  304  chooses the strongest one. This may be accomplished by estimating the area of large objects and comparing them to find the largest object. Process  305  queries whether that feature is near the center. If the response to process  305  is yes, process  307  identifies a “rule of thirds” problem and returns a “bad” response from the horizontal analysis system. If the response to process  305  is no, process  306  queries whether the feature is level. This may be accomplished by comparing the height of a horizontal feature at each side of the image. If the feature is not level, process  308  identifies a level problem and a “bad” result is returned from the horizontal analysis system to process  201  of  FIG. 2 .  
         [0029]     The rule of thirds is perhaps one of the most popular ‘rules’ in photography and yields pleasing compositions. The rule of thirds works by imaginary lines which divide the prospective image into thirds both horizontally and vertically. The most important elements of a composition are placed where these lines intersect. In addition to the intersections, the areas can be arranged into bands occupying a third of the image. Also, elements can be placed along the imaginary lines.  
         [0030]     If the process  306  query responds that the feature is level, process  309  queries whether the subject is too far away. This may be accomplished by calculating the ratio of the area of the main feature to the area of the total image and comparing it to an acceptable ratio. If process  309  finds that the image is too far away, a suggestion is offered by process  312  and a “bad” response is returned from the horizontal analysis system to process  201  of  FIG. 2 . If the subject is not found to be too far away at  309 , process  310  queries to see if the foreground framing is okay. This may be determined by checking a set frame of the image for dominant objects and determining the area of those objects to compare to an acceptable level. If there is a problem in the foreground framing, a suggestion is offered by process  312 , and a “bad” response is returned from the horizontal analysis system to process  201  of  FIG. 2 . If there is no problem in the foreground framing, process  311  queries if the scene is back-lit. This may be checked by examining the relative brightness of dominant objects in the image. If the scene is back-lit, process  312  offers a suggestion and a “bad” response is returned from the horizontal analysis system to process  201  in  FIG. 2 . If process  311  finds that the scene is not back-lit, a “good” response is returned from the horizontal analysis system.  
         [0031]      FIG. 4  depicts system  40 , a computer set-up configured to implement an embodiment. A computer central processing unit  41 , CPU, is connected to computer screen  42  for possible visual display of images, suggestions, or score comparisons. The CPU contains memory  401 , for processing and storage of images and scores, and provides speaker  405  for optional audio output of suggestions and score comparisons. The computer may be connected to network  44 , as may provide communication between processor based systems such as network server  45 , to provide general access to the system or method throughout an office or between several computers. The printer  407  is coupled to computer  40  for output of data and images, such as output of suggestions and score comparisons and/or output of the images. The user may also scan images to digital format using scanner  406  (or any other imager) connected to the computer to provide an image with which to work without the use of a digital camera. The scanner image could be a picture, or even text, and the system described herein could be used to improve faulty images and to teach a user how to improve scanner images. The computer system may be interfaced with a digital camera  43  that contains screen  402  to view the images, instructions, suggestions, or score reports and may contain speaker  404  to hear audible instructions, suggestions, or score reports provided during the picture taking process. The entire method may also take place solely on the digital camera without interface to a computer or computer network. Also, it should be noted that the systems and methods discussed herein could apply to analog images as well as digital.