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Computer Graphics



                          A typical contour image representation is clearly depicted in the figure 1.2.

                                                    Figure 1.2: Contour Image Representation















                          A contour representation helps in easy recovery of the full image in bitmap form. It has been used
                          mainly for data compression of an image. The main idea is to program each level with the boundaries of
                          connected regions of pixels at levels superior than or equal to each level. It is easy to recreate an original
                          image from these boundaries.

                          One of the main issues in using contour image representations is how to save such representations in a
                          compact manner. In practice, the complete contour representation is hardly ever required. Its distinctive
                          use is in the form of a query, where it asks for the contours matching for a given gray level. To provide
                          efficient solutions, the current data structuring techniques should be used. However, they have not yet
                          been used, but attempts are being made in order to solve this.

                                           Consider  a typical scenario encountered  with this  kind of representation.
                                           Presume that you want to erase wrinkles around the eyes of a person in a

                                           digitized picture given in contour representation. As the image of the wrinkles
                                           may intersect contour lines, these may appear to be detached after removal of the
                                           wrinkles. It is not easy to reconnect these contour lines. Dynamic programming
                                           might be a normal approach to solve this problem.
                                           Note: The dynamic programming has been tried in numerous experiments
                                           effectively. The only drawback of dynamic programming is that it is very costly.
                          In fact, the total number of pixels in an image is a function of the size of the image and the number of
                          pixels per unit length, such as inch in the horizontal as well as the vertical direction. This number of
                          pixels per unit length is the resolution of the image.

                                             A 3×2 inch image at a resolution of 300 pixels per inch would have a total of 5,
                                             40,000 pixels.
                          In general, the image size is given as the total number of pixels in the horizontal direction multiplied by
                          the total number of pixels in the vertical  direction, such  as 512×512, 640×480, 800×600, or 1024×768.
                          Even though, this convention makes it comparatively easy to measure the total number of pixels in an
                          image, it does not specify the size of the image or its resolution.

                                             A 640×480 image would measure 62/31 inches by 5 inches when displayed or
                                             printed at 96 pixels per inch. Alternatively, the same image would measure 1.6

                                             inches × 1.2 inches at 400 pixels per inch.
                          The ratio or the proportion of an image’s width to its height, measured in unit length or number of
                          pixels, is referred to as aspect ratio.






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