Machine Vision Algorithms for Automated Inspection Thin-Film Disk Heads
IEEE Transactions on Pattern Analysis and Machine Intelligence
Parallel Analysis of Clusters in Landscape Ecology
IEEE Computational Science & Engineering
Computer simulations with Mathematica: explorations in complex physical and biological systems
Computer simulations with Mathematica: explorations in complex physical and biological systems
On the effectiveness of superconcurrent computations on heterogeneous networks
Journal of Parallel and Distributed Computing
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Mathematical Morphology and Its Applications to Image and Signal Processing
Mathematical Morphology and Its Applications to Image and Signal Processing
Image Segmentation with Directed Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid object labelling in digital images
Machine Vision and Applications
| Hi-index | 0.10 |
The enhanced Hoshen-Kopelman (EHK) algorithm is introduced for a single pass analysis of spatial clusters in large images. The EHK algorithm is a generalisation of the algorithm known in the statistical physics literature as the Hoshen-Kopelman (HK) algorithm. While the HK algorithm was designed to compute cluster sizes in a binary image, the EHK algorithm enables the computation of cluster shape parameters such as spatial clusters moments, perimeters and bounding boxes in a multiple class image. An example of spatial cluster analysis for a simulated image containing 2 x 10^9 pixels is given.