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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. [ABSTRACT FROM AUTHOR]

Abstract: Image segmentation is one of the fundamental and important steps that is needed to prepare an image for further processing in many computer vision applications. Over the last few decades, many image segmentation methods have been proposed, as accurate image segmentation is vitally important for many image, video and computer vision applications. A common approach is to look at the grey level colours of the image to perform multi-level-thresholding. The ability to quantify and compare the resulting segmented images is of vital importance even though it can be a major challenge. One measure used here computes the total distances of the pixels to its centroid for each region to provide a quantifiable measure of the segmented images. We also suggest an improved Zhang''s entropy measure for image segmentation based on computing the entropy of the image and segmented regions. In this paper, we will present the results from both of these approaches. [Copyright &y& Elsevier]