Patent Publication Number: US-8988534-B2

Title: Characterizing point checking region setting apparatus and method, and image stabilizing apparatus including the same

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATION 
     This application claims priority from Korean Patent Application No. 10-2012-0003450, filed on Jan. 11, 2012 in the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference. 
     BACKGROUND 
     1. Field 
     Apparatuses and methods consistent with exemplary embodiments relate to image stabilization, and more particularly, to a characterizing point checking region setting apparatus and method, and an image stabilizing apparatus including the characterizing point checking region setting apparatus. 
     2. Description of the Related Art 
     In order to exactly detect a certain object, in particular, a moving object, by using a camera, each image has to be stabilized. However, it may be difficult to detect a certain object because captured images are shaken due to various external causes. For example, when a certain object is photographed in a state where a camera is exposed to an outside environment, the camera may slightly move due to, for example, wind or external shock. In addition, when the camera is mounted on a movable apparatus, the camera may be shaken according to movement of the movable apparatus. The shaking of images becomes severe as more external shock is applied to the camera, and eventually the object may not be detected exactly. An image stabilization technology is used to detect an object exactly by stabilizing the shaken images. 
     A Korean patent (KR 2008-0083525; Method for stabilizing digital image which can correct the horizontal shear distortion and vertical scale distortion) discloses a related art image stabilization method. According to the related art image stabilization method, a current frame is corrected by using characterizing points extracted from the current frame and characterizing points extracted from a previous frame. According to this image stabilization method, however, if a shaking degree of the image increases, image correction may not be stably performed. 
     SUMMARY 
     One or more exemplary embodiments provide an apparatus and method of setting an optimal characterizing point checking region, and an image stabilizing apparatus for correcting and stabilizing shaking images by using the characterizing point checking region. 
     According to an aspect of an exemplary embodiment, there is provided a characterizing point checking region setting unit including: a sample frame extract unit which extracts a plurality of image frames, obtained for a certain period of time, from image data obtained by photographing an object; and a frame analyzing unit which detects a plurality of characterizing points in the extracted plurality of image frames, and sets a characterizing point checking region which is used to check characterizing points in a currently input image frame. 
     The frame analyzing unit may include: a characterizing point detector which receives the plurality of image frames from the sample frame extract unit, and detects the plurality of characterizing points in the plurality of image frames; a characterizing point classification unit which classifies the plurality of characterizing points into a plurality of clusters for each of the image frames; a center point detector which detects a centroid point of the characterizing points in the plurality of image frames; and a check region determination unit which sets the characterizing point checking region including a major cluster among the plurality of clusters based on the centroid point. 
     According to an aspect of another exemplary embodiment, there is provided a characterizing point checking region setting method including: receiving a plurality of image frames captured for a certain period of time; and detecting a plurality of characterizing points in the plurality of image frames, and setting a characterizing point checking region for checking the detected characterizing points, in a currently input image frame. 
     The setting the characterizing point checking region may include: detecting a plurality of characterizing points in each of the plurality of image frames; classifying the plurality of characterizing points into a plurality of clusters for each of the image frames; detecting a representative centroid point representing the plurality of image frames; and setting the characterizing point checking region including a major cluster among the plurality of clusters, based on the representative centroid point. 
     According to an aspect still another exemplary embodiment, there is provided an image stabilizing apparatus including: the above characterizing point checking region setting unit; and an image adjusting unit which sets the characterizing point checking region in an image frame that is currently input, compares the currently input image frame with a reference image that is preset, and adjusts the currently input image frame as much as a shaking amount when it is determined that the current image frame is shaken. 
     The image stabilizing apparatus may further include: a reference image setting unit which extracts an image frame that is the least shaken among the plurality of image frames taken for a certain period of time, and sets the extracted image frame as the reference image. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other aspects will become more apparent by describing in detail exemplary embodiments with reference to the attached drawings, in which: 
         FIG. 1  is a block diagram of an image stabilizing apparatus according to an exemplary embodiment; 
         FIG. 2  is a detailed block diagram of a reference image setting unit shown in  FIG. 1 , according to an exemplary embodiment; 
         FIGS. 3A ,  3 B and  3 C show examples of shaken image frames and stabilized image frames; 
         FIG. 4  is a flowchart illustrating a method of setting a reference image performed by the reference image setting unit shown in  FIG. 2 , according to an exemplary embodiment; 
         FIG. 5  is a flowchart illustrating an operation of the method shown in  FIG. 4  in detail, according to an exemplary embodiment; 
         FIG. 6  is a detailed block diagram of a characterizing point checking region setting unit shown in  FIG. 1 , according to an exemplary embodiment; 
         FIG. 7  is a diagram showing examples of detected centroid points according to an exemplary embodiment; 
         FIGS. 8A and 8B  are diagrams illustrating a method of setting a characterizing point checking region, according to an exemplary embodiment; 
         FIGS. 9A and 9B  are diagrams of set optimal characterizing point checking regions according to an exemplary embodiment; 
         FIG. 10  is a flowchart illustrating a method of setting a characterizing point checking region performed by the characterizing point checking region setting unit shown in  FIG. 6 , according to an exemplary embodiment; 
         FIG. 11  is a flowchart illustrating a second operation of the method shown in  FIG. 10  in detail, according to an exemplary embodiment; 
         FIG. 12  is a detailed block diagram of an image adjusting apparatus shown in  FIG. 1 , according to an exemplary embodiment; 
         FIG. 13  is an image showing an example of an optical flow according to an exemplary embodiment; 
         FIG. 14  is a diagram showing representative directions of the optical flow according to an exemplary embodiment; 
         FIG. 15  is an image showing a state where an image is adjusted according to an exemplary embodiment; 
         FIGS. 16A and 16B  are graphs showing shaken degrees of image frames, according to an exemplary embodiment; and 
         FIG. 17  is a flowchart illustrating a method of adjusting an image performed by the image adjusting apparatus of  FIG. 12 , according to an exemplary embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS 
     Hereinafter, exemplary embodiments will be described in detail with reference to accompanying drawings. Like reference numerals denote like elements. 
       FIG. 1  is a block diagram of an image stabilizing apparatus  100  according to an exemplary embodiment. The image stabilizing apparatus  100  receives image data P 1  that is obtained by a camera (not shown) photographing an object, and stabilizes images included in the image data P 1 . When the object is continuously photographed by the camera in a state of being fixed, obtained images are stabilized. However, if the object is photographed in a state where the camera is shaken, obtained images are also shaken, and accordingly the photographed object may not exactly be distinguished from other objects or an environment. When the images are shaken as described above, the image stabilizing apparatus  100  stabilizes the image by moving the shaking object to an original position in the image. 
     Referring to  FIG. 1 , the image stabilizing apparatus  100  includes a reference image setting unit  111 , a characterizing point checking region setting unit  121 , and an image adjusting unit  131 . 
     The reference image setting unit  111  extracts an image frame that is shaken least among a plurality of image frames included in the image data P 1  obtained by photographing the object, and then sets the extracted image frame as a reference image. The reference image setting unit  111  outputs a signal P 2  indicating the reference image to the image adjusting unit  131 . The reference image setting unit  111  is described in more detail below with reference to  FIGS. 2 through 5 . 
     The characterizing point checking region setting unit  121  receives the image data P 1  input from outside, and sets a characterizing point checking region. The characterizing point checking region setting unit  121  generates a signal P 3  indicating the characterizing point checking region, and outputs the signal P 3  to the image adjusting unit  131 . The characterizing point checking region setting unit  121  will be described in detail with reference to  FIGS. 6 through 11 . 
     The image adjusting unit  131  receives the signals P 2  and P 3 . The image adjusting unit  131  sets the characterizing point checking region in the image included in the image data P 1  that is currently input, and compares the currently input image with the reference image so as to adjust and stabilize the currently input image according to a shaken degree of the image when the currently input image is shaken. The image adjusting unit  131  is described in more detail below with reference to  FIGS. 12 through 17 . 
       FIG. 2  is a detailed block diagram of the reference image setting unit  111  shown in  FIG. 1 . Referring to  FIG. 2 , the reference image setting unit  111  includes a sample frame extract unit  211  and a reference frame extract unit  221 . 
     The sample frame extract unit  211  receives the image data P 1  from outside. The image data P 1  is obtained by continuously photographing an object with the camera. The image data P 1  includes a plurality of image frames. For example, the image data P 1  includes a plurality of image frames as shown in  FIGS. 3A ,  3 B and  3 C, each including a building located on a right side of the image frame. The image frames in  FIG. 3A and 3C  show states where images are shaken vertically relative to the image frame in  FIG. 3B . The sample frame extract unit  211  extracts a plurality of image frames taken for a certain time period from the image data P 1 . The image data P 1  may include hundreds to tens of thousands of image frames per second according to performance of the camera. Therefore, the certain period of time may be set as 1 second or shorter if a shutter speed of the camera is fast, and may be set to be longer than 1 second if the shutter speed of the camera is slow. However, the present embodiment is not limited to this example. 
     The reference frame extract unit  221  receives the plurality of image frames extracted by the sample frame extract unit  211 , and compares the received image frames with each other to extract the most stabilized image frame and sets the most stabilized image frame as a reference image. The most stabilized image frame is an image frame of which a shaking degree is the least. 
     The reference frame extract unit  221  includes a center point detector  231 , a characterizing point detector  232 , a frame average calculator  233 , a frame comparison value calculator  234 , and a reference frame selector  235 . 
     The center point detector  231  receives the plurality of image frames from the sample frame extract unit  211 , and detects center points of the plurality of image frames. That is, the center point detector  231  detects one center point from each of the plurality of image frames. The center point is located at a center of the image frame and may be represented as coordinates. 
     The characterizing point detector  232  receives the plurality of image frames from the sample frame extract unit  211  and detects a plurality of characterizing points in the plurality of image frames. That is, the characterizing point detector  232  detects the plurality of characterizing points in each of the plurality of image frames. The plurality of characterizing points may be represented as coordinates. The image frame includes various characterizing elements, some of which may be detected as the characterizing points according to needs of a user. In order to detect the characterizing points of the image frame, a Harris&#39; corner detection method, a scale invariant feature transform (SIFT) algorithm, or a speeded-up robust feature (SURF) algorithm may be used. 
     The frame average calculator  233  receives the plurality of center points detected by the center point detector  231  and the plurality of characterizing points detected by the characterizing point detector  232 , and calculates a plurality of frame averages. The plurality of frame averages may be obtained by averaging distances between the center points and the plurality of characterizing points in corresponding image frames. When the number of image frames is N (N is an integer greater than zero), N frame averages may be calculated. 
     The frame comparison value calculator  234  receives the plurality of frame averages from the frame average calculator  233  and calculates a plurality of frame comparison values. The plurality of frame comparison values may be obtained by summing up absolute values, which are obtained by subtracting the frame averages of other image frames from the frame average of the corresponding image frame. If the number of the frame averages is N, the number of the frame comparison values is also N. The frame comparison value Pk (k is an integer) of each of the plurality of image frames may be calculated by the following equation 1.
 
 Pk= abs{ Rk−R 0}+abs{ Rk−R 1}+ . . . +abs{ Rk−Rn}   (1),
 
     where abs denotes an absolute value. 
     For example, if the number of extracted image frames for the certain period of time is 5, five frame averages R 0  to R 4  are calculated, and five frame comparison values P 0  to P 4  may be obtained as the following equation 2.
 
 P 0=abs{ R 0 −R 1}+abs{ R 0− R 2}+abs{ R 0 −R 3}+abs{ R 0 −R 4}
 
 P 1=abs{ R 1 −R 0}+abs{ R 1− R 2}+abs{ R 1 −R 3}+abs{ R 1 −R 4}
 
 P 2=abs{ R 2 −R 0}+abs{ R 2− R 1}+abs{ R 2 −R 3}+abs{ R 2 −R 4}
 
 P 3=abs{ R 3 −R 0}+abs{ R 3− R 1}+abs{ R 3 −R 2}+abs{ R 3 −R 4}
 
 P 4=abs{ R 4 −R 0}+abs{ R 4− R 1}+abs{ R 4 −R 2}+abs{ R 4 −R 3}  (2)
 
     The reference frame selector  235  receives the plurality of frame comparison values and selects an image frame having the smallest frame comparison value among the plurality of frame comparison values. The image frame having the smallest value is set as the reference image. The smallest frame comparison value represents that the image is least shaken. 
     As described above, the reference image setting unit  111  extracts the plurality of image frames for the certain period of time from the image data P 1  input from outside and detects an image frame having the least degree of shaking among the extracted image frames and sets this image frame as the reference image. 
       FIG. 4  is a flowchart illustrating a method of setting the reference frame by the reference image setting unit  111  of  FIG. 2 . Referring to  FIG. 2 , the method of setting the reference image includes operation S 411  and operation S 421 . 
     In operation S 411 , the reference image setting unit  111  extracts the plurality of image frames taken for a certain period of time among the plurality of image frames included in the image data P 1  input from outside. 
     In operation S 421 , the reference image setting unit  111  compares the plurality of extracted image frames with each other to detect and set the image frame that is the most stabilized as the reference image. The most stabilized image frame denotes an image frame, of which a shaking degree is the least among the image frames. 
       FIG. 5  is a flowchart illustrating the operation S 421  of  FIG. 4  in more detail. Referring to  FIG. 5 , the operation S 421  of  FIG. 4  includes four sub-operations S 511  through S 541 . 
     In operation S 511 , the reference image setting unit  111  extracts the center point and the plurality of characterizing points from each of the plurality of extracted image frames. 
     In operation S 521 , the reference image setting unit  111  calculates an average of distances between the center points and the plurality of characterizing points in each of the image frames, that is, a frame average. 
     In operation S 531 , the reference image setting unit  111  calculates a sum of absolute values that are obtained by subtracting other frame averages from the frame average of each image frame, that is, a frame comparison value. That is, the reference image setting unit  111  calculates the plurality of frame comparison values by using equation 1 above. 
     In operation S 541 , the reference image setting unit  111  detects an image frame having the smallest frame comparison value among the plurality of frame comparison values and sets the detected image frame as the reference image. 
     Therefore, the reference image setting unit  111  detects the image frame of which a shaking degree is the least among the plurality of image frames included in the image data P 1  and then sets the detected image frame as the reference image. 
       FIG. 6  is a detailed block diagram of the characterizing point checking region setting unit  121  shown in  FIG. 1 . Referring to  FIG. 6 , the characterizing point checking region setting unit  121  includes a sample frame extract unit  611  and a frame analyzing unit  621 . 
     The sample frame extract unit  611  receives the image data P 1  from outside. The image data P 1  includes a plurality of image frames that are obtained by photographing an object continuously. The sample frame extract unit  611  extracts a plurality of image frames taken for a certain time period among the plurality of image frames included in the image data P 1 . The image data P 1  obtained by photographing the object with the camera may include hundreds to tens of thousands of image frames per second according to performance of the camera. Therefore, the certain period of time may be set as 1 second or shorter if the shutter speed of the camera is fast, and may be set to be longer than 1 second if a shutter speed of the camera is slow. However, the present embodiment is not limited to this example. 
     The frame analyzing unit  621  receives the plurality of image frames that are extracted for the certain period of time from the sample frame extract unit  611 . The frame analyzing unit  621  detects a plurality of characterizing points in the plurality of image frames, and sets an optimal characterizing point checking region by using the plurality of characterizing points. The frame analyzing unit  621  outputs a signal P 3  representing the characterizing point checking region. 
     The frame analyzing unit  621  includes a characterizing point detector  631 , a characterizing point classification unit  632 , a center point detector  633 , a checking region setting unit  634 , and a checking region adjusting unit  635 . 
     The characterizing point detector  631  receives the plurality of image frames extracted for the certain period of time from the sample frame extract unit  611  and detects a plurality of characterizing points ( 723 ,  733  of  FIG. 7 ) in each of the plurality of image frames. That is, the characterizing point detector  631  detects a plurality of characterizing points ( 723 ,  733  of  FIG. 7 ) in each of the plurality of image frames. Each of the plurality of characterizing points ( 723 ,  733  of  FIG. 7 ) may be represented as coordinates. Each of the image frames includes various characterizing elements, some of which may be detected as the characterizing points ( 723 ,  733  of  FIG. 7 ) according to setting by a user. In order to detect the characterizing points ( 723 ,  733  of  FIG. 7 ) of the image frame, a Harris&#39; corner detection method, a SIFT algorithm, or an SURF algorithm may be used. 
     The characterizing point classification unit  632  classifies the plurality of characterizing points ( 723 ,  733  of  FIG. 7 ) detected by the characterizing point detector  631  as a plurality of clusters ( 721  and  731  of  FIG. 7 ), for example, a major cluster ( 721  of  FIG. 7 ) and a minor cluster ( 731  of  FIG. 7 ), for each of the image frames ( 711  of  FIG. 7 ). The major cluster ( 721  of  FIG. 7 ) includes 50% or more of the characterizing points, and the minor cluster ( 731  of  FIG. 7 ) includes less than 50% of the characterizing points. As described above, since the major cluster ( 721  of  FIG. 7 ) includes more characterizing points than the minor cluster ( 731  of  FIG. 7 ), the major cluster  721  may be wider than the minor cluster  731  as shown in  FIG. 7 . In order to classify the characterizing points ( 723 ,  733  of  FIG. 7 ) as a plurality of clusters ( 721  and  731  of  FIG. 7 ), a k-mean clustering method and a support vector machine (SVM) method may be used as an example. 
     The center point detector  633  detects a centroid point ( 741  of  FIG. 7 ) of the characterizing points in the plurality of image frames. To do this, the center point detector  633  detects a centroid point ( 723  of  FIG. 7 ) of the major cluster  721  and a centroid point ( 733  of  FIG. 7 ) of the minor cluster  731  that are classified by the characterizing point classification unit  632  in each of the image frames ( 711  of  FIG. 7 ). The center point detector  633  calculates an average between the centroid point ( 723  of  FIG. 7 ) of the major cluster  721  and the centroid point ( 733  of  FIG. 7 ) of the minor cluster  731  to detect the centroid point ( 741  of  FIG. 7 ) in each of the image frames. The centroid point ( 741  of  FIG. 7 ) in each of the image frames is generally adjacent to the major cluster ( 721  of  FIG. 7 ) as shown in  FIG. 7 . The center point detector  633  calculates an average of the centroid points ( 741  of  FIG. 7 ) of the plurality of image frames, and detects a representative centroid point ( 751  of  FIGS. 8A and 8B ) of the plurality of image frames, as shown in  FIGS. 8A and 8B . The average of the centroid points ( 741  of  FIG. 7 ) in the plurality of image frames may be calculated by summing the centroid points ( 741  of  FIG. 7 ) of the plurality of image frames, and dividing the sum by the number of image frames. The centroid points described above may be represented as coordinates. 
     The checking region setting unit  634  sets a characterizing point checking region ( 811  of  FIG. 8A  or  821  of  FIG. 8B ) including all of the major clusters ( 721  of  FIG. 7 ) of the plurality of image frames based on the representative centroid point ( 751  of  FIGS. 8A and 8B ) detected by the center point detector  633 , as shown in  FIGS. 8A and 8B . The characterizing point checking region  811  or  821  may be formed in various shapes, for example, may be formed as a region  811  denoted by a circle as shown in  FIG. 8A  or may be formed as a region  821  denoted by a square as shown in  FIG. 8B . 
     The checking region adjusting unit  635  determines whether the characterizing point checking region  811  or  821  includes a standard level or greater of the characterizing points ( 921  of  FIG. 9B ) of the image frames extracted for the certain period of time. The standard level may be set as 80% of the characterizing points  921 . The checking region adjusting unit  635  expands the characterizing point checking region  811  or  821  so as to include the standard level of characterizing points, if the characterizing points included in the characterizing point checking region  811  or  821  are less than the standard level.  FIG. 9B  shows a state where the adjustment is finished and an optimal characterizing point checking region  911  is set, and  FIG. 9A  shows one of the plurality of image frames. 
     As described above, since the characterizing point checking region setting unit  121  sets the optimal characterizing point checking region  911 , a time taken to test the characterizing points of the image stabilizing apparatus  100  may be greatly reduced. 
       FIG. 10  is a flowchart illustrating a method of setting the characterizing point checking region performed by the characterizing point checking region setting unit  121  shown in  FIG. 6 , according to an exemplary embodiment. Referring to  FIG. 10 , the method includes operation S 1011  and operation S 1021 . 
     In operation S 1011 , the characterizing point checking region setting unit ( 121  of  FIG. 6 ) extracts a plurality of image frames taken for a certain period of time among the plurality of image frames included in the image data (P 1  of  FIG. 6 ) input from outside. 
     In operation S 1021 , the characterizing point checking region setting unit  121  detects a plurality of characterizing points ( 921  of  FIG. 9B ) in the plurality of extracted image frames and sets the optimal characterizing point checking region ( 911  of  FIG. 9B ) by using the plurality of characterizing points  921 . 
       FIG. 11  is a flowchart illustrating the operation S 1021  shown in  FIG. 10  in more detail. Referring to  FIG. 11 , the operation S 1021  shown in  FIG. 10  includes sub-operations S 1111  through  1151 . 
     In operation S 1111 , the characterizing point setting unit  121  extracts a plurality of characterizing points ( 723 ,  733  of  FIG. 7 ) from each of the plurality of extracted image frames. 
     In operation S 1121 , the characterizing point checking region setting unit  121  classifies the plurality of detected polarizing points ( 723 ,  733  of  FIG. 7 ) as a plurality of clusters ( 721  and  731  of  FIG. 7 ), for example, the major cluster  721  and the minor cluster  731 , for each of the image frames. The major cluster  721  is set to include 50% or greater of the characterizing points, and the minor cluster  731  is set to include less than 50% of the characterizing points. 
     In operation S 1131 , the characterizing point checking region setting unit  121  detects the representative centroid point ( 751  of  FIGS. 8A and 8B ) of the plurality of image frames. That is, the characterizing point checking region setting unit  121  detects the centroid points ( 723  and  733  of  FIG. 7 ) from each of the plurality of clusters  721  and  731 , and calculates the average of the centroid points  723  and  733  of the plurality of clusters  721  and  731  for each of the image frames to detect the centroid point  741  of each of the image frames. In addition, the centroid points  741  of the plurality of image frames are summed, and the sum is divided by the number of image frames to detect the representative centroid point  751  of the plurality of image frames. 
     In operation S 1141 , the characterizing point checking region setting unit  121  sets the characterizing point checking region ( 811  of  FIG. 8A  or  821  of  FIG. 8B ) that includes all of the major clusters  721  based on the representative centroid point  751 . 
     In operation S 1151 , the characterizing point checking region setting unit  121  determines whether the characterizing point checking region  811  or  821  includes the standard level of characterizing points  921  of the image frames extracted for the certain period of time or greater. When an amount of the characterizing points included in the characterizing point checking region  811  or  821  is less than the standard level, the characterizing point checking region setting unit  121  expands the characterizing point checking region  811  or  821  to include the standard level of characterizing points. The standard level may be set as 80% of the characterizing points  921 . Therefore, the optimal characterizing point checking region  911  may be set. 
     As described above, the characterizing point checking region setting unit  121  sets the optimal characterizing point checking region  911  by using the plurality of image frames included in the image data P 1  input from outside, and thus, a time that is taken to check the characterizing points of the image frames is greatly reduced. 
       FIG. 12  is a detailed block diagram of the image adjusting unit  131  shown in  FIG. 1 . Referring to  FIG. 12 , the image adjusting unit  131  includes an image analyzing unit  1201  and an image moving unit  1241 . 
     The image analyzing unit  1201  compares a current image frame included in the image data P 1  input from outside with the predetermined reference image included in the reference image signal P 2  and extracts a representative direction and a representative magnitude of the shaking if the current image frame is shaken. 
     The image analyzing unit  1201  includes an optical flow calculator  1211 , a representative direction extractor  1221 , and a representative magnitude extractor  1231 . 
     The optical flow calculator  1211  compares the current image frame with the reference image to calculate an optical flow ( 1321  of  FIG. 13 ) in the characterizing point checking region  911 . As shown in  FIG. 13 , the optical flow  1321  has a direction and a magnitude. A method of calculating the optical flow  1321  is well known in the art, and thus detailed descriptions thereof are not provided here. The reference image is an image frame of which a shaking degree is the least among the plurality of image frames taken for the certain period of time. The optical flow calculator  1211  may receive the reference image from the reference image setting unit shown in  FIG. 2 . The method of setting the reference image is described above with reference to  FIGS. 2 through 5 . 
     The representative direction extractor  1221  inputs the optical flow  1321  calculated by the optical flow calculator  1211 . The representative direction extractor  1221  extracts a representative shaking direction of the currently input image frame from the optical flow  1321 . The shaking direction of the image may be set in eight (8) directions, for example, east direction, west direction, south direction, north direction, south-east direction, north-east direction, south-west direction, and north-west direction. The representative direction extractor  1221  determines which one of the eight directions is the representative direction of the optical flow  1321  and sets the direction as the representative direction of the currently input image frame. The shaking direction of the image may be divided in more detail, for example, 12 directions, 24 directions, or 36 directions. 
     The representative magnitude extractor  1231  inputs the optical flow  1321  calculated by the optical flow calculator  1211 . The representative magnitude extractor  1231  extracts a representative shaking magnitude of the currently input image frame from the optical flow  1321 . The representative shaking magnitude of the image frame may be obtained by converting magnitudes of the optical flow having the representative shaking direction into a histogram, and averaging vectors included in a range having the largest number of bins in the histogram. 
     The image moving unit  1241  moves the currently input image frame as much as the representative magnitude extracted by the representative magnitude extractor  1231  in an opposite direction to the representative direction extracted by the representative direction extractor  1221 . That is, the image moving unit  1241  moves the image frame as much as the magnitudes of Table 1 below in the directions shown in Table 1. In Table 1, minus (−) denotes the opposite direction, and the representative directions are the directions shown in  FIG. 14 . 
     
       
         
           
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Representative 
                 coordinates of moving current image frame 
               
               
                 direction 
                 (X-axis, Y-axis) 
               
               
                   
               
             
            
               
                 1 
                 −representative magnitude, 0 
               
               
                 2 
                 −(representative magnitude/{square root over (2 )}), 
               
               
                   
                 −(representative magnitude/{square root over (2)}) 
               
               
                 3 
                 0, −representative magnitude 
               
               
                 4 
                 (representative magnitude/{square root over (2 )}), 
               
               
                   
                 −(representative magnitude/{square root over (2)}) 
               
               
                 5 
                 representative magnitude, 0 
               
               
                 6 
                 (representative magnitude/{square root over (2)}), 
               
               
                   
                 (representative magnitude/{square root over (2)}) 
               
               
                 7 
                 0, representative magnitude 
               
               
                 8 
                 −(representative magnitude/{square root over (2)}), 
               
               
                   
                 (representative magnitude/{square root over (2)}) 
               
               
                   
               
            
           
         
       
     
     Referring to Table 1, the image moving unit  1241  moves the current image frame on the X-axis as much as the representative magnitude in the opposite direction to the representative direction, when the representative direction is an X-axis (1, 5). In addition, the image moving unit  1241  moves the current image frame on the Y-axis as much as the representative magnitude in the opposite direction to the representative direction when the representative direction is a Y-axis (3, 7). However, when the representative direction is a diagonal direction (2, 4, 6, 8), the image moving unit  1241  moves the current image frame in a diagonal line as much as (representative magnitude/√{square root over (2)}) in the opposite direction by using trigonometric functions. After that, four sides of the moved current image frame are trimmed in consideration of the representative direction and the representative magnitude. Therefore, the image may be stabilized as shown in  FIG. 15 . The image moving unit  1241  outputs a signal P 4  representing the stabilized image. 
       FIGS. 16A and 16B  are graphs showing shaken degrees of image frames. Specifically,  FIG. 16A  shows shaken degrees of image frames before stabilization, and  FIG. 16B  shows shaken degrees of the image frames after stabilization. 
     Referring to  FIG. 16A , large deviation is shown between locations of pixels in the image frames. That is,  FIG. 16A  shows a state where an image is severely shaken and is unstable. 
     Referring to  FIG. 16B , small deviation is shown between locations of the pixels in the image frames. That is,  FIG. 16B  shows a state where the image is stabilized. 
     As described above, the optical flow of the image input to the image adjusting unit  131  is calculated to extract the representative direction and the representative magnitude of the image. Then, if the image is shaken, the image is moved as much as the representative magnitude in the opposite direction to the representative direction. Thus, the shaking may be corrected and the image may be stabilized. 
       FIG. 17  is a flowchart illustrating a method of adjusting the image by the image adjusting unit  131  shown in  FIG. 12 , according to an embodiment. Referring to  FIG. 17 , the method of adjusting the image includes operations S 1711  through S 1731 . 
     In operation S 1711 , the image adjusting unit  131  compares the current image frame input from outside with the preset reference image to calculate the optical flow ( 1321  of  FIG. 13 ). The reference image is input from outside to the image adjusting unit  131 . 
     In operation S 1721 , the image adjusting unit  131  extracts the representative direction and the representative magnitude of the shaking of the currently input image from the optical flow  1321 . 
     In operation S 1731 , the image adjusting unit  131  moves the image frame that is currently input as much as the representative magnitude in the opposite direction to the representative direction. In more detail, if the representative direction is an X-axis direction, the image adjusting unit  131  moves the current image frame as much as the representative magnitude in the opposite direction to the representative direction on the X-axis. If the representative direction is a Y-axis direction, the image adjusting unit  131  moves the current image frame as much as the representative magnitude in the opposite direction to the representative direction on the Y-axis. However, when the representative direction is a diagonal direction, the image adjusting unit  131  moves the current image frame as much as (representative magnitude/√{square root over (2)}) in the opposite direction to the representative direction on the diagonal line. After that, the four sides of the moved current image frame are trimmed in consideration of the representative direction and the representative magnitude. Therefore, the shaking is corrected, and the stabilized image may be obtained as shown in  FIG. 15 . 
     According to the exemplary embodiments, the optimal characterizing point checking region is set by using the image frames extracted for a certain period of time, and thus a time to check the characterizing points of the currently input image frame may be reduced greatly. 
     In addition, since the shaking of the currently input image frame is corrected by using the optimal characterizing point checking region, the image correction time may be reduced and the image may be optimally stabilized. 
     While the inventive concept has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the inventive concept as defined by the following claims.