A new approach to simulate characterization of particulate matter employing support vector machines

This paper, for the first time, applies the support vector machines (SVMs) paradigm to identify the optimal segmentation algorithm for physical characterization of particulate matter. Size of the particles is an essential component of physical characterization as larger particles get filtered through nose and throat while smaller particles have detrimental effect on human health. Typical particulate characterization processes involve image reading, preprocessing, segmentation, feature extraction, and representation. Of these various steps, knowledge based selection of optimal image segmentation algorithm (from existing segmentation algorithms) is the key for accurately analyzing the captured images of fine particulate matter. Motivated by the emerging machine-learning concepts, we present a new framework for automating the selection of optimal image segmentation algorithm employing SVMs trained and validated with image feature data. Results show that the SVM method accurately predicts the best segmentation algorithm. As well, an image processing algorithm based on Sobel edge detection is developed and illustrated.