1 Introduction
Image analysis based automated counting approaches can be found in many fields, such as medical imaging, horticulture & crop industry and soil research [1], [2] and [3]. They show the efficacy of automated counting systems, which are sufficiently reliable, consistent, fast and also more convenient than manual counting. However there are challenges in developing robust, automated counting techniques and they are unique to the specific problem. Some of the challenges for counting are uneven illumination, noise, occluded objects and clumped objects. For example, Sio, S. W. S. et al. developed a clump splitting technique to address the issue of counting clumped red blood cells [3]. The clumped cells adversely affect the accuracy of the parasitimia, because one clumped cell contains a few cells, but it is counted only once.