Simulations of flatbed scanners can shorten the development cycle of new designs, estimate image quality, and lower manufacturing costs. In this paper, we present a flatbed scanner simulation a strobe RGB scanning method that investigates the effect of the sensor height on color artifacts. The image chain model from the remote sensing community was adapted and tailored to fit flatbed scanning applications. This model allows the user to study the relationship between various internal elements of the scanner and the final image quality. Modeled parameters include: sensor height, intensity and duration of illuminant, scanning rate, sensor aperture, detector modulation transfer function (MTF), and motion blur created by the movement of the sensor during the scanning process. These variables are also modeled mathematically by utilizing Fourier analysis, functions that model the physical components, convolutions, sampling theorems, and gamma corrections. Special targets were used to validate the simulation include single frequency pattern, a radial chirp-like pattern, or a high resolution scanned document. The simulation is demonstrated to model the scanning process effectively both on a theoretical and experimental level.
Developing precise and low-cost spatial localization algorithms is an essential component for autonomous
navigation systems. Data collection must be of sufficient detail to distinguish unique locations, yet coarse enough to
enable real-time processing. Active proximity sensors such as sonar and rangefinders have been used for interior
localization, but sonar sensors are generally coarse and rangefinders are generally expensive. Passive sensors such as
video cameras are low cost and feature-rich, but suffer from high dimensions and excessive bandwidth. This paper
presents a novel approach to indoor localization using a low cost video camera and spherical mirror. Omnidirectional
captured images undergo normalization and unwarping to a canonical representation more suitable for processing.
Training images along with indoor maps are fed into a semi-supervised linear extension of graph embedding manifold
learning algorithm to learn a low dimensional surface which represents the interior of a building. The manifold surface
descriptor is used as a semantic signature for particle filter localization. Test frames are conditioned, mapped to a low
dimensional surface, and then localized via an adaptive particle filter algorithm. These particles are temporally filtered
for the final localization estimate. The proposed method, termed omnivision-based manifold particle filters, reduces
convergence lag and increases overall efficiency.
Three-dimensional textural and volumetric image analysis holds great potential in understanding the image data produced by multi-photon microscopy. In this paper, an algorithm that quantitatively analyzes the texture and the morphology of vasculature in engineered tissues is proposed. The investigated 3D artificial tissues consist of Human Umbilical Vein Endothelial Cells (HUVEC) embedded in collagen exposed to two regimes of ultrasound standing wave fields under different pressure conditions. Textural features were evaluated using the normalized Gray-Scale Cooccurrence Matrix (GLCM) combined with Gray-Level Run Length Matrix (GLRLM) analysis. To minimize error resulting from any possible volume rotation and to provide a comprehensive textural analysis, an averaged version of nine GLCM and GLRLM orientations is used. To evaluate volumetric features, an automatic threshold using the gray level mean value is utilized. Results show that our analysis is able to differentiate among the exposed samples, due to morphological changes induced by the standing wave fields. Furthermore, we demonstrate that providing more textural parameters than what is currently being reported in the literature, enhances the quantitative understanding of the heterogeneity of artificial tissues.