Generalized Image Acquisition and Analysis

Computational Plenoptic Imaging

The plenoptic function is a ray-based model for light that includes the color spectrum as well as spatial, temporal, and directional variation. Although digital light sensors have greatly evolved in the last years, one fundamental limitation remains: all standard CCD and CMOS sensors integrate over the dimensions of the plenoptic function as they convert photons into electrons; in the process, all visual information is irreversibly lost, except for a two-dimensional, spatially-varying subset - the common photograph. In this state of the art report, we review approaches that optically encode the dimensions of the plenpotic function transcending those captured by traditional photography and reconstruct the recorded information computationally.

Projects

Interactive Geometry-Aware Segmentation for the Decomposition of Kaleidoscopic Images

Oliver Klehm, Ilya Reshetouski, Elmar Eisemann, Hans-Peter Seidel, Ivo Ihrke
VMV 2012



Abstract

Mirror systems have recently emerged as an alternative low-cost multi-view imaging solution. The use of these systems critically depends on the ability to compute the background of a multiply mirrored object. The images taken in such systems show a fractured, patterned view, making edge-guided segmentation difficult. Further, global illumination and light attenuation due to the mirrors make standard segmentation techniques fail. We therefore propose a system that allows a user to do the segmentation manually. We provide convenient tools that enable an interactive segmentation of kaleidoscopic images containing three-dimensional objects. Hereby, we explore suitable interaction and visualization schemes to guide the user. To achieve interactivity, we employ the GPU in all stages of the application, such as 2D/3D rendering as well as segmentation.
Video Demo Data Set

Bibtex

@InProceedings{Klehm12,
author = {Oliver Klehm, Ilya Reshetouski, Elmar Eisemann, Hans-Peter Seidel, and Ivo Ihrke},
title = "{Interactive Geometry-Aware Segmentation for the Decomposition of Kaleidoscopic Images }",
booktitle = {Proceedings of VMV},
pages = "xx--yy",
year = {2012},
}
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