Separable Image Warping with Spatial Lookup Tables

Image warping refers to the 2-D resampling of a source image onto a target image. In the general case, this requires costly 2-D filtering operations. Simplifications are possible when the warp can be expressed as a cascade of orthogonall-D transformations. In these cases, separable transformations have been introduced to realize large performance gains. The central ideas in this area were formulated in the 2-pass algorithm by Catmull and Smith. Although that method applies over an important class of transformations, there are intrinsic problems which limit its usefulness. The goal of this work is to extend the 2-pass approach to handle arbitrary spatial mapping functions. We address the difficulties intrinsic to 2-pass scanline algorithms: bottlenecking, foldovers, and the lack of closed-form inverse solutions. These problems are shown to be resolved in a general, efficient, separable technique, with graceful degradation for transformations of increasing complexity

Image Understanding and Robotics Research at Columbia University

Over the past year, the research investigations of the Vision/Robotics Laboratory at Columbia University have reflected the interests of its four faculty members, two staff programmers, and 16 Ph.D. students. Several of the projects involve other faculty members in the department or the university, or researchers at AT&T, IBM, or Philips. We list below a summary of our interests and results, together with the principal researchers associated with them. Since it is difficult to separate those aspects of robotic research that are purely visual from those that are vision-like (for example, tactile sensing) or vision-related (for example, integrated vision-robotic systems), we have listed all robotic research that is not purely manipulative. The majority of our current investigations are deepenings of work reported last year; this was the second year of both our basic Image Understanding contract and our Strategic Computing contract. Therefore, the form of this year's report closely resembles last year's. Although there are a few new initiatives, mainly we report the new results we have obtained in the same five basic research areas. Much of this work is summarized on a video tape that is available on request. We also note two service contributions this past year. The Special Issue on Computer Vision of the Proceedings of the IEEE, August, 1988, was co-edited by one of us (John Kender [27]). And, the upcoming IEEE Computer Society Conference on Computer Vision and Pattem Recognition, June, 1989, is co-program chaired by one of us (John Kender [23])

Fast data-parallel rendering of digital volume images.

by Song Zou.Year shown on spine: 1997.Thesis (M.Phil.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 69-[72]).Chapter 1 --- Introduction --- p.1Chapter 2 --- Related works --- p.7Chapter 2.1 --- Spatial domain methods --- p.8Chapter 2.2 --- Transformation based methods --- p.9Chapter 2.3 --- Parallel Implement ation --- p.10Chapter 3 --- Parallel computation model --- p.12Chapter 3.1 --- Introduction --- p.12Chapter 3.2 --- Classifications of Parallel Computers --- p.13Chapter 3.3 --- The SIMD machine architectures --- p.15Chapter 3.4 --- The communication within the parallel processors --- p.16Chapter 3.5 --- The parallel display mechanisms --- p.17Chapter 4 --- Data preparation --- p.20Chapter 4.1 --- Introduction --- p.20Chapter 4.2 --- Original data layout in the processor array --- p.21Chapter 4.3 --- Shading --- p.21Chapter 4.4 --- Classification --- p.23Chapter 5 --- Fast data parallel rotation and resampling algorithms --- p.25Chapter 5.1 --- Introduction --- p.25Chapter 5.2 --- Affine Transformation --- p.26Chapter 5.3 --- Related works --- p.28Chapter 5.3.1 --- Resampling in ray tracing --- p.28Chapter 5.3.2 --- Direct Rotation --- p.28Chapter 5.3.3 --- General resampling approaches --- p.29Chapter 5.3.4 --- Rotation by shear --- p.29Chapter 5.4 --- The minimum mismatch rotation --- p.31Chapter 5.5 --- Load balancing --- p.33Chapter 5.6 --- Resampling algorithm --- p.35Chapter 5.6.1 --- Nearest neighbor --- p.36Chapter 5.6.2 --- Linear Interpolation --- p.36Chapter 5.6.3 --- Aitken's Algorithm --- p.38Chapter 5.6.4 --- Polynomial resampling in 3D --- p.40Chapter 5.7 --- A comparison between the resampling algorithms --- p.40Chapter 5.7.1 --- The quality --- p.42Chapter 5.7.2 --- Implement ation and cost --- p.44Chapter 6 --- Data reordering using binary swap --- p.47Chapter 6.1 --- The sorting algorithm --- p.48Chapter 6.2 --- The communication cost --- p.51Chapter 7 --- Ray composition --- p.53Chapter 7.1 --- Introduction --- p.53Chapter 7.2 --- Ray Composition by Monte Carlo Method --- p.54Chapter 7.3 --- The Associative Color Model --- p.56Chapter 7.4 --- Parallel Implementation --- p.60Chapter 7.5 --- Discussion and further improvement --- p.63Chapter 8 --- Conclusion and further work --- p.67Bibliography --- p.6