Image Acquisition Clause Samples

Image Acquisition. Project Area: The area of interest for Orthophotography coverage is a total of 1129 square miles. Area includes 1008 square miles in Sedgwick County, KS, 28 square miles in Butler County, KS and 93 square miles in Harvey County, KS. An additional area includes an added 18 square miles in Butler County, KS and 16 square miles in Sumner County, KS identified as the Wichita Area Metropolitan Organization (WAMPO) area.

Image Acquisition. Acquisition-based footprints on still images can be studied from different perspectives. On the one hand, much of the research efforts have been focused on characterizing particular stages during the camera acquisition process for device identification, forgery detection or device linking purposes. On the other hand, image acquisition is also performed with digital scanners, and many of the techniques developed for camera footprint analysis have been translated to their scanner equivalents. Finally, rendering of photorealistic computer graphics (PRCG) requires application of a physical light transport and camera acquisition models, and can be thought of as a third acquisition modality. For digital camera image acquistion, the process can be summarized by the stages shown schemati- cally below: From the diagram above, the target scene will first be distorted by the capturing lense, before being mosaiced by an RGB Colour Filter Array (CFA). Pixel values are then stored on the internal CCD/CMOS array, and then post-processed for software-based gamma correction, edge enhancement and often JPEG compression. The captured image is then either displayed/projected on screen or printed and can then be recaptured either with a second camera setup or a digital scanner. In this case, geometric distortions due to the orientation of the flat photograph with respect to the second camera as well as the lighting source in the reacquisition setup will transform the recaptured image. While each of the stages above leaves a characteristic footprint on the captured image, so far each processing block has been considered in isolation, studying the digital footprints left regardless of the remaining processing stages. This is certainly useful as an initial study of the individual camera footprints that can be found within a digital image. However, it leaves scope for analysis of operator chains. To further corroborate this idea, several methods have been presented where cues from more than one stage are simultaneously taken into account, albeit based on either heuristics or black-box classifiers, rather than on a formal understanding of cascading operators. This approach has been proven to boost the accuracy of device identification algorithms [2] [3] [4]. In the following sections the state-of-the-art concerning digital footprints left by individual operators will be presented, followed by the work on scanned image analysis and PRCG image detection. A comprehensive survey on non...

Image Acquisition