Abstract
Scanning hardcopy non-metric images is one of the most important sources in digital mapping. Low-cost scanners are still widely used in many applications as they can produce digital images of comparable precisions to those produced by expensive professional scanners. Yet, inexpensive scanners introduce geometrical distortions in the measured image coordinates that must be assessed and compensated before using their products for further analysis. In this article, several 2D-to-2D transformation models were investigated to calibrate flatbed scanners with different resolutions and sizes. We evaluated the potential of each model using two gridded-crosses plotted on high-quality transparent sheets. Control coordinates were provided through a photogram-metric analytical plotter. After scanning the sheets, least squares matching was applied to determine the precise locations of the crosses. By comparing the control coordinates and those estimated from digitized images, it was found that the mathematical model based on the projective transformation gives the best results for standardizing the geometric properties of flatbed scanners. The results show that scanning resolution of 2400 dpi achieves the requirements for large-scale mapping applications.
Highlights
Scanners are an essential part of softcopy mapping systems
By comparing the control coordinates and those estimated from digitized images, it was found that the mathematical model based on the projective transformation gives the best results for standardizing the geometric properties of flatbed scanners
Several mathematical models are investigated to calibrate the flatbed scanners and to minimize the errors generated during the process
Summary
Scanners are an essential part of softcopy mapping systems. Despite the rapid dependence on digital cameras, scanners have been used for several years as a source of digital images. There are several aspects and requirements for scanners to be employed in digital mapping Their illumination must be adequate to reach the best radiometric quality and Signal-to-Noise-Ratio (SNR). An algorithm to correct for the parabola effect in flatbed scanners for dosimetry applications was outlined in [28] Besides these studies, other researchers addressed the geometric properties of digitized documents through different approaches. A content independent approach to compensate for the geometric distortions in document-images captured by digital cameras, which is very useful for scanned documents was proposed in [29]. They recover two spatial curves representing the 3D page surface. A book de-warping system to remove the perspective and geometric distortions automatically from single images by boundary-based 3D surface reconstruction was presented in [32]
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