Affiliations: [a] Faculty of Engineering, Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia | [b] Faculty of Computer Science and Information Technology, University Malaya, Kuala Lumpur, Malaysia | [c] Faculty of Engineering, Computing and Science, Swinburne University of Technology, Sarawak Campus, Kuching, Malaysia | [d] Kingsley Education Group, Kuala Lumpur, Malaysia
Correspondence:
[*]
Corresponding author: Weng Kin Lai, Department of Electrical and Electronic Engineering, Faculty of Engineering, Tunku Abdul Rahman University College, 53300 Kuala Lumpur, Malaysia. Tel.: +603 4145 0125; Fax: +603 4142 3166; E-mail: [email protected].
Abstract: Natural rubber (NR) latex is sensitive to mechanical influences which can occur at almost every stage of its manufacturing process. Moreover its mechanical stability can also change during storage or be modified with the addition of suitable soaps such as oleates, laureates, and stearates [1]. Hence the Mechanical Stability Test (MST) is of vital importance to the rubber industry as it gives an indication of the quality of the latex. Currently the assessment is performed manually by trained laboratory technicians, following the procedures as defined by the ISO 35 standard mechanical stability test. However, the test is highly dependent on the human visual capability and the experience of the laboratory technician performing the test, potentially leading to either inconsistent or inaccurate results. In this paper, we proposed a computer vision-based mechanical stability classification system to minimise the potential for biasness in the current standard test. We investigated this with a novel feature descriptor – Histogram of Size Distribution (HSD) that is based on the coagulum count and size. Experimental results demonstrated that the proposed system was able to provide essentially good classification accuracies on the data tested.
