Emanuel Gofman

Best Merge Region-Growing Segmentation With Integrated Nonadjacent Region Object Aggregation

Best merge region growing normally produces segmentations with closed connected region objects. Recognizing that spectrally similar objects often appear in spatially separate locations, we present an approach for tightly integrating best merge region growing with nonadjacent region object aggregation, which we call hierarchical segmentation or HSeg. However, the original implementation of nonadjacent region object aggregation in HSeg required excessive computing time even for moderately sized images because of the required intercomparison of each region with all other regions. This problem was previously addressed by a recursive approximation of HSeg, called RHSeg. In this paper, we introduce a refined implementation of nonadjacent region object aggregation in HSeg that reduces the computational requirements of HSeg without resorting to the recursive approximation. In this refinement, HSegs region intercomparisons among nonadjacent regions are limited to regions of a dynamically determined minimum size. We show that this refined version of HSeg can process moderately sized images in about the same amount of time as RHSeg incorporating the original HSeg. Nonetheless, RHSeg is still required for processing very large images due to its lower computer memory requirements and amenability to parallel processing. We then note a limitation of RHSeg with the original HSeg for high spatial resolution images and show how incorporating the refined HSeg into RHSeg overcomes this limitation. The quality of the image segmentations produced by the refined HSeg is then compared with other available best merge segmentation approaches. Finally, we comment on the unique nature of the hierarchical segmentations produced by HSeg.

Developing an Efficient Region Growing Engine for Image Segmentation

Image segmentation is a crucial part of image processing applications. Currently available approaches require significant computer power to handle large images. We present an efficient region growing algorithm for the segmentation of multi-spectral images in which the complexity of the most time-consuming operation in region growing, merging segment neighborhoods, is significantly reduced. In addition, considerable improvement is achieved by preprocessing, where adjacent pixels with close colors are gathered and used as initial segments. The preprocessing provides substantial memory savings and performance gain without a noticeable influence on segmentation results. In practice, there is an almost linear dependency between the runtime and image size. Experiments show that large satellite images can be processed using the new algorithm in a few minutes on a moderate desktop computer.

Injecting floating-point testing knowledge into test generators

Floating-point unit (FPU) verification is a known challenge, due to the variety of corner cases both in its data path and control flow. We have identified a gap in the coverage of FP corner cases that combine special data and control scenarios. We propose a solution based on combining the deep FP knowledge of a special FP test generator with the strength of a general-purpose test generator. We present a novel FP testing knowledge package (FPTK) that consists of a weighted set of FP scenarios. We explain the flow of combining the existing tools with the FPTK and demonstrate its effect.