The effect of the sequence of image distortion correction on the accuracy of connection point generation

In this paper, the UAV photogrammetry data of two hills and two flat sampling areas are selected. The image data are processed by pixel Grid software through different operation methods, and the effect of the two methods is compared. The results show that: after the distortion correction, the image is rotated manually or the flight angle in the pos data is directly changed, which has no obvious effect on the error in the number of iterations of the automatic connection point. When the distortion correction is not corrected, the photo is rotated, and the angle of the Omega, Phi and Kappa angles is obtained from the UAV autopilot system, and makes the corner element higher precision when the automatic connection point is generated. Add Kappa to the POS data by 90, and then correct distortion at the same time choice of rotating pictures; Set the Omega, Phi is 0, Kappa angle from the IMU, this method avoids the influence of the angle deviation caused by the tilt or jitter of the flight process, so it is better to line elements when the automatic connection point is generated.

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SPECTRA: A program for processing electron images of crystals

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100121065 ◽

1992 ◽

Vol 50 (1) ◽

pp. 132-133

Author(s):

M.F. Schmid ◽

R. Dargahi ◽

M. W. Tam

Keyword(s):

Lattice Distortion ◽

Data Transfer ◽

Reciprocal Lattice ◽

Data Entry ◽

Image Data ◽

Distortion Correction ◽

Specimen Preparation ◽

Electron Image ◽

Computer Controlled ◽

Program Modules

Electron crystallography is an emerging field for structure determination as evidenced by a number of membrane proteins that have been solved to near-atomic resolution. Advances in specimen preparation and in data acquisition with a 400kV microscope by computer controlled spot scanning mean that our ability to record electron image data will outstrip our capacity to analyze it. The computed fourier transform of these images must be processed in order to provide a direct measurement of amplitudes and phases needed for 3-D reconstruction.In anticipation of this processing bottleneck, we have written a program that incorporates a menu-and mouse-driven procedure for auto-indexing and refining the reciprocal lattice parameters in the computed transform from an image of a crystal. It is linked to subsequent steps of image processing by a system of data bases and spawned child processes; data transfer between different program modules no longer requires manual data entry. The progress of the reciprocal lattice refinement is monitored visually and quantitatively. If desired, the processing is carried through the lattice distortion correction (unbending) steps automatically.

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Using POS Data for Price Promotions Evaluation

Surveillance Technologies and Early Warning Systems ◽

10.4018/978-1-61692-865-0.ch014 ◽

2011 ◽

pp. 267-285

Author(s):

Danijel Bratina ◽

Armand Faganel

Keyword(s):

Panel Data ◽

Marketing Research ◽

Sales Promotion ◽

Communication Tool ◽

Price Promotions ◽

Obvious Effect ◽

Consumer Panel ◽

Pos Data ◽

High Level ◽

Analysis Models

Price promotions have been largely dealt with in the literature. Yet there are just a few generalizations made so far about this powerful marketing communication tool. The obvious effect, that all authors who have studied price promotions emphasize, is quantity increase during price promotions. Inference studies about the decomposition of the sales promotion bump do not converge to a generalization or a law, but end in radically different results. Most of these studies use consumer panel data, rich of demographical characteristics and consumers’ purchasing history. Companies that use such data, available from marketing research industry, usually complain that data is old and expensive. The authors start with literature review on price promotions in which they present existing models based on consumer panel data (Bell, et al., 1999; Mela, et al., 1998; Moriarty, 1985; Walters, 1991; Yeshin, 2006). Next they present existing POS analysis models and compare their findings to show the high level of heterogeneity among results. All existing models are based on powerful databases provided by professional research institutions (i.e. Nielsen or IRI) that usually cover the whole market for the analysed brand category geographically. The authors next apply existing models to find which best suits data available for Slovenian FMCG market. They show two models analysis – quantity (SCAN*PRO) and market share (MCI) and their power for explanatory and forecasting research using POS data. Having dealt with more than 30 brand categories within a wider research, they conclude that the models developed are usable for a fast decision making process within a company, but their exploratory power is still poor compared to panel data.

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