Affiliations: Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, India. [email protected] | Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India. [email protected] | Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, India. [email protected]
Note: [] Address for correspondence: Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, India.
Abstract: Granular object segmentation is an important area of image processing, which has several practical applications in agriculture, food industry, geology, and forensics. In this paper, we present a simple algorithm for the analysis of granulometric images that consist of touching or overlapping convex objects such as coffee bean, food grain, nuts, blood cell, or cookies. The algorithm is based on certain underlying digital-geometric features embedded in their binary snapshots. The concept of an outer isothetic cover and the property of geometric convexity are used to extract the joining points (or concavity points) from the ensemble of objects. Next, a combinatorial technique is employed to determine the separator of two overlapping or neighboring objects. This technique is fully automated and it needs only integer-domain computation. The termination time of the algorithm can be traded-off with the quality of segmentation by changing the resolution parameter. Experimental results for a variety of objects chosen from different application domains such as cell image, coffee-bean image and others demonstrate the efficiency and robustness of the proposed method compared to earlier watershed-based algorithms.
