18-06-2013, 11:37 AM
Image Mosaicing
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ABSTRACT
There are situations where it is not possible to capture large documents with the given imaging media such as scanners or copying machines in a single stretch because of their inherent limitations. This results in capture of a large document in terms of split components of a document image. Hence, the need is to mosaic the split components into the original and put together the document image. In this paper, we present a novel and simple approach to mosaic two split images of a large document based on pixel value matching. The method compares the values of pixels in the columns of split images to identify the common or overlapping region (OR) in them, which helps in mosaicing of split images of a large document.
EXISTING SYSTEM :
The usage of flatbed scanners to capture large utility maps. The method selects the control points in different utility maps to find the displacement required for shifting from one map to the next. These control points are found from the pair of edges common to both the maps. However, the process requires human intervention to mask out the region not common to both the split images in image mosaicing.).They have exploited domain knowledge, instead of using generic corner features to extract a more organized set of features. The exhaustive search adopted is computationally expensive because of the rotation of an image employed during matching. In addition, the method demands 50% OR in the split images to produce the mosaic image. However, the approaches are limited to only text documents and are prone to failure in case of general documents containing pictures. However, in practice, a typical document contains text, pictures and tables.
PROPOSED SYSTEM:
In this section, a novel approach based on comparing the values of pixels in the columns of the split images to mosaic them in order to produce a single, large document image is presented. Mosaicing is achieved by identifying the positions of OR in the split images. The OR are obtained by comparing the values of pixels in the columns of the split images. If the columns match, then the pointers j (where j is the pointer to columns in the split images) of both split images 1 and 2 are incremented by one. If there is no match, the pointer j of the split image 1 is moved to the next column, while the pointer j of the image 2 remains unaltered. This procedure is repeated till an overlapping region is found. The method works based on the assumption that the OR is present at the right and the left ends of the split images 1 and 2 respectively.