page_title: Document mosaicing
section_title: Images mosaicing
hierarchical_section_title: Document mosaicing / Images mosaicing
caption_attribution_description: English: Mosaicing of two document images. In the overlap region the pixels are blended, using an unweighted mean at the centre of the region and increasingly weighted means towards the edges. This blending operation eliminates any abrupt seams in the mosaic, but will result in a blurred composite if the registration is not accurate. Blurring is evident in the affine mosaic (b), but not in the mosaic constructed using a plane-to-plane projectivity (a). Close-ups of typical seams from (a) and (b) are shown in (c) and (d) respectively. Note the system’s ability to cope with mixtures of fonts and unaligned columns.
context_page_description: Document mosaicing is a process that stitches multiple, overlapping snapshot images of a document together to produce one large, high resolution composite. The document is slid under a stationary, over-the-desk camera by hand until all parts of the document are snapshotted by the camera’s field of view. As the document slid under the camera, all motion of the document is coarsely tracked by the vision system. The document is periodically snapshotted such that the successive snapshots are overlap by about 50%. The system then finds the overlapped pairs and stitches them together repeatedly until all pairs are stitched together as one piece of document.
  The document mosaicing can be divided into four main processes.
  Tracking
  Feature detecting
  Correspondences establishing
  Images mosaicing.
context_section_description: Given the list of corresponding points of the two images, finding the transformation of the overlapping portion of the images is the next process. Assuming a pinhole camera model, the transformation between pixels (u,v) of image 1 and pixels (u0, v0) of image 2 is demonstrated by a plane-to-plane projectivity.
  The parameters of the projectivity is found from four pairs of matching points. RANSAC regression technique is used to reject outlying matches and estimate the projectivity from the remaining good matches.
  The projectivity is fine-tuned using correlation at the corners of the overlapping portion to obtain four correspondences to sub-pixel accuracy. Therefore, image1 is then transformed into image2’s coordinate system using Eq.1. The typical result of the process is shown in Figure 5.
caption: Figure 5 : Mosaicing of two document images. Blurring is evident in the affine mosaic (b), but not in the mosaic constructed using a plane-to-plane projectivity (a). Close-ups of typical seams of (a) and (b) are shown in (c) and (d) respectively.[1]
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