Multi-Dimensional Signal-Processing Research Program
Abstract
In the area of image segmentation and classification, we have been developing a hierarchical segmentation scheme for processing images with several region classes. This approach appears to offer improvements over a direct multiclass segmentation. Adaptive contrast enhancement techniques have proved useful in several areas. We have used adaptive contrast enhancement as a preprocessor for image segmentation based on texture rather than gray level. We have also applied these techniques to aerial images degraded by light cloud cover and haze. The primary effects of this type of degradation are a reduction in contrast and an increase in the local average intensity of the image. We also discuss two aspects of the iterative implementation of multi-dimensional digital filters. This first concerns spatial truncation effects which occur during the iteration because the image frame buffer has a finite storage capability. The errors caused by this truncation are closely related to the solution of a boundary value problem with boundary conditions specified on the frame edges. These errors can be eliminated by including the boundary conditions in the spatially truncated iteration. Potential multiprocessor architectures are discussed for realizing multi-dimensional signal-processing operations such as those needed in the iterative implementation. An underlying problem is the efficient partitioning of multi-dimensional signal-processing problems among the several processors in a multiprocessor architecture.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 31, 1981
- Accession Number
- ADA108641
Entities
People
- Dan E. Dudgeon
Organizations
- Massachusetts Institute of Technology