Application of the Hough transform for the automatic determination of soot aggregate morphology

Vision-based technique for circle detection and measurement using lookup table and bitwise center accumulator

In this paper, we proposed a fast algorithm for circle detection and measurement, which uses a lookup table and bitwise center accumulator (LUT-BCA). The LUT-BCA is a two-stage procedure. The first stage determines the candidate circle centers with normal lines produced by the lookup table and recorded by the bitwise center accumulator. The second stage computes the radius of each candidate circle center and validates these centers, which eliminates the false detections. The proposed LUT-BCA can also be easily implemented on massively parallel high-performance graphics hardware using the compute unified device architecture. Experimental results indicate that the LUT-BCA can locate circle centers and measure their radii accurately and is very suitable for real-time circle detection and measurement.

An automatic algorithm for determination of the nanoparticles from TEM images using circular hough transform

Nanoparticles have a wide range of applications in science and technology, and the size distribution of nanoparticles is one of the most important statistical properties. Transmission electron microscopy (TEM) or X-ray diffraction is commonly used for the characterization and measuring particle size distributions, but manual analysis of the micrographs is extremely labor-intensive. Here, we have developed an image processing algorithm for measuring particle size distributions from TEM images in the presence of overlapped particles and uneven background. The approach is based on the modified circular Hough transform, and pre and post processing techniques on TEM image to improve the accuracy and increase the detection rate of the nano particles. Its application is presented through several images with different noises, uneven backgrounds and over lapped particles. The merits of this robust quantifying method are demonstrated by comparing the results with the data obtained through manual measurement. The algorithm allows particles to be detected and characterized with high accuracy.