Abstract:
A method for automatically classifying a digital image as a dud, the method comprises the steps of receiving the digital image; determining individually or any combination of sharpness,;contrast, noise, and exposure of the digital image; determining a threshold individually for sharpness, contrast, noise, and exposure, or a threshold for any combination of sharpness, contrast, noise, and exposure which determined threshold or thresholds determines if the image is classified as a dud; and classifying the image as a dud based on the determination of the previous step.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is related to U. S. application Ser. No. 09/163,618, filed Sep. 30, 1998, by Alexander C. Loui et. al., and entitled, “A METHOD FOR AUTOMATICALLY CLASSIFYING IMAGES INTO EVENTS”, and also related to. U. S. application Ser. No. 09/211,762, filed Nov. 20, 1998, by Alexander C. Loui et. al., and entitled, “A METHOD FOR AUTOMATICALLY COMPARING CONTENT OF IMAGES FOR CLASSIFICATION INTO EVENTS.” 
    
    
     FIELD OF THE INVENTION 
     The invention relates to the field of digital image processing and, more particularly, to digital image processing having automatic detection of images that are undesirable for placing in albums, i.e., duds. 
     BACKGROUND OF THE INVENTION 
     Pictorial images are often placed in albums manually by individuals. When the pictorial images are available in digital form, the albuming process can be automated by computer software. During the automated albuming process, it is desirable to detect dud photographs for possible elimination from the album. Duds are defined as images that are not worth keeping, and therefore not worth putting in an album. Current methods for detecting duds include visually inspecting each photograph. However, this method is obviously time consuming and labor intensive. 
     Consequently, a need exists for detecting duds in automatic albuming. 
     SUMMARY OF THE INVENTION 
     The present invention is directed to overcoming one or more of the problems set forth above. Briefly summarized, according to one aspect of the present invention, the invention resides in a method for automatically classifying a digital image as a dud, the method comprising the steps of receiving the digital image; determining individually or any combination of sharpness, contrast, noise, and exposure of the digital image; determining a threshold individually for sharpness, contrast, noise, and exposure, or a threshold for any combination of sharpness, contrast, noise, and exposure which determined threshold or thresholds determines if the image is classified as a dud; and classifying the image as a dud based on the determination of the previous step. 
     These and other aspects, objects, features and advantages of the present invention will be more clearly understood and appreciated from a review of the following detailed description of the preferred embodiments and appended claims, and by reference to the accompanying drawings. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a perspective view of a computer system for implementing the present invention; 
     FIG. 2 is a flowchart illustrating an overview of a software program of the present invention; 
     FIG. 3 is a flowchart illustrating a detailed portion of FIG. 2; 
     FIG. 4 is a flowchart illustrating another detailed portion of FIG. 2; 
     FIG. 5 is a flowchart illustrating still another detailed portion of FIG. 2; 
     FIG. 6 is also a flowchart illustrating a detailed portion of FIG. 2; and 
     FIG. 7 is still further a flowchart illustrating a detailed portion of FIG.  2 . 
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description, the present invention will be described in the preferred embodiment as a software program. Those skilled in the art will readily recognize that the equivalent of such software may also be constructed in hardware. 
     Still further, as used herein, computer readable storage medium may comprise, for example; magnetic storage media such as a magnetic disk (such as a floppy disk) or magnetic tape; optical storage media such as an optical disc, optical tape, or machine readable bar code; solid state electronic storage devices such as random access memory (RAM), or read only memory (ROM); or any other physical device or medium employed to store a computer program. 
     Referring to FIG. 1, there is illustrated a computer system  10  for implementing the present invention. Although the computer system  10  is shown for the purpose of illustrating a preferred embodiment, the present invention is not limited to the computer system  10  shown, but may be used on any electronic processing system. The computer system  10  includes a microprocessor-based unit  20  for receiving and processing software programs and for performing other processing functions. A display  30  is electrically connected to the microprocessor-based unit  20  for displaying user-related information associated with the software. A keyboard  40  is also connected to the microprocessor based unit  20  for permitting a user to input information to the software. As an alternative to using the keyboard  40  for input, a mouse  50  may be used for moving a selector  52  on the display  30  and for selecting an item on which the selector  52  overlays, as is well known in the art. 
     A compact disk-read only memory (CD-ROM)  55  is connected to the microprocessor based unit  20  for receiving software programs and for providing a means of inputting the software programs and other information to the microprocessor based unit  20  via a compact disk  57 , which typically includes a software program. In addition, a floppy disk  61  may also include a software program, and is inserted into the microprocessor-based unit  20  for inputting the software program. Still further, the microprocessor-based unit  20  may be programmed, as is well know in the art, for storing the software program internally. A printer  56  is connected to the microprocessor-based unit  20  for printing a hardcopy of the output of the computer system  10 . 
     Images may also be displayed on the display  30  via a personal computer card (PC card)  62  or, as it was formerly known, a personal computer memory card international association card (PCMCIA card) which contains digitized images electronically embodied in the card  62 . The PC card  62  is ultimately inserted into the microprocessor based unit  20  for permitting visual display of the image on the display  30 . 
     Referring to FIG. 2, a flowchart is shown for illustrating an overview of the software program of the present invention. In this regard, the software is initiated S 2  and several factors are analyzed. It is instructive to note that the four steps described below need not be performed in the exact order as described below, as those skilled in the art will readily recognize. Still further, each of the steps will be described in detail in after the describing the overview. First, the sharpness of the image is analyzed S 4  for determining its sharpness. Then the digital image is analyzed for determining its contrast S 6 , noise S 8  and a exposure S 10 . 
     In regard to step S 4  of analyzing the sharpness, sharpness is the opposite of defocus and can be characterized by the point spread function (PSF) or the modulation transfer function (MTF) of the imaging system. First, an edge profile of the image is obtained S 4   a  by computing the absolute value of the Laplacian. Then, a histogram of the edge profile is formed S 4   b  and the values that lie above the 90 th  percentile represent the sharpest edges of the image S 4   c . Next, an average (centroid) of the gradients of the sharpest edges, as determined in the previous step, is computed S 4   d . The average gradient is the measure of sharpness. 
     This measure is appropriate only if the entire image is out of focus. If only part of the image is out-of-focus and another part is in focus, the results will be affected by the image area that is in focus. 
     In regard to step S 6  of analyzing the contrast, the details of this step may by found in U.S. Pat. No. 5,822,453. Referring to FIG. 4, these steps are summarized as the following. First, the edge profile of the image is obtained S 6   a  by computing the absolute value of the Laplacian. An image intensity histogram from pixels on the edges of the image is formed S 6   b . The standard deviation of the histogram is calculated S 6   c . The contrast measure is the standard deviation of the above histogram. 
     In regard to step S 8  of analyzing the noise, the details are disclosed in U.S. pat. application Ser. No. 08/822,722. Referring to FIG. 6, these steps are summarized as the following. First, an edge profile of the image is obtained by computing the absolute value of the Laplacian S 8   a . Then, a histogram of the edge profile is formed S 8   b  and the values that lie below the 10 th  percentile represent the flat regions of the image S 8   c . Next the standard deviation of the image intensity in the flat regions, as determined in the previous step, is computed S 8   d . The noise measure is the standard deviation over the entire image. 
     In regard to the step S 10  of analyzing the exposure, the overall brightness of an image is an indication of overexposure or underexposure. Referring to FIG. 6, Each pixel of the image is compared to an underexposure threshold (preferably 50) S 10   a . The percentage of pixels in the image that are below the underexposure threshold is computed S 10   b . If the above percentage is larger than a percentage threshold (preferably 90%) S 10   c , then the image is underexposed. 
     Referring to FIG. 7, Each pixel of the image is compared to an overexposure threshold (preferably 220) S 10   d . The percentage of pixels in the image that are above the overexposure threshold is computed S 10   e . If the above percentage is larger than a percentage threshold (preferably 90%) S 10   f , then the image is overexposed. 
     Dud detection may be based on any of the above-described measures using either of three different approaches. First, in a single feature classifier, each measure is thresholded independently to obtain the duds based on that measure. Preferably, the thresholds are 650 for sharpness, 75 for contrast, 30 for noise, and 90 for exposure. A second multi-feature regression technique is to combine all of the objective measures to obtain an overall image quality estimate, which is then thresholded, preferably at 40, for dud detection. Finally, the objective measures may be used as inputs to a multi-feature classifier that is trained to detect duds. 
     The overall image quality of an image is represented by a parameter Q a . Since the objective measures are computed and available, they are used as predictors for Q a  by forming a linear combination of them where the linear coefficients are preferably (0.0097, −0.349, 2.6417, −0.2587) for sharpness, contrast, noise and exposure respectively.