ABSTRACT:Machine vision based analysis provides a novel technology for froth flotation monitoring. Froth images collected are characterized by fully occupied bubbles with different size and shape under various illuminations. Convex bubbles lead to the formation of white spots that seriously affect froth color measurement. In this article, specular highlights are detected and preprocessed so as to estimate underlying color of white spots region. Because of the fact that color information is believed to be related to flotation performance, therefore, after the application of highlight inpainting, multivariate image analysis is proposed to extract color features, which are further related to mineral grades by a orthogonal least square regression model. The established relationship provides a promising empirical model to predict mineral grade, which is a significant indicator for flotation performance. Experimental results show that, when compared with traditional methods, the proposed algorithm can achieve a robust color measurement and predict mineral concentration effectively. V V C
The task of logical structure recovery is known to be of crucial importance, yet remains unsolved not only for image based document but also for born-digital document system. In this work, the modeling of contextual information based on 2D Conditional Random Fields is proposed to learn page structure for born-digital fixed-layout documents. Heuristic prior knowledge of Portable Document Format (PDF) content and layout are interpreted to construct neighborhood graphs and various pairwise clique templates for the modeling of multiple contexts. By integrating local and contextual observations obtained from PDF attributes, the ambiguities of semantic labels are better resolved. Experimental comparisons for six types of clique templates has demonstrated the benefits of contextual information in logical labeling of 16 finely defined categories.
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