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Research bauxite image sequence selection method based on foam texture analysis

Author: SunYuanYuan
Tutor: TangChaoZuo
School: Central South University
Course: Control Science and Engineering
Keywords: flotation froth texture analysis tree-structured wavelet transform SVM
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 51
Quote: 0
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Abstract


During the mineral flotation process, foam layer can be a direct response to the quality indicators. At present, the control of flotation process is often based on the visual appearance of the froth phase, and to a large extent depends on the experience and ability of a human operator. These types of process are consequently often controlled sub-optimally owing to the inaccuracy and unreliability of manual control. Therefore, researching the identification of the foam state and applying them into practice are great significance for optimizing flotation process, maximum using resource, reducing resource consume and maintaining enterprise sustainable development.The cleaning froth shows palpable texture property, as its bubbles stack and collapse seriously, which is different from other froth. In order to accurately identify the froth state, the work aims to extract texture features of the cleaning froth image sequence, analyzes the relationship between texture parameter and flotation technical data, and achieve the identification by the application of SVM (support vector machine). Main research work are as follows:Firstly, considering the limitation of the existing texture feature which is hard to establish contact with the technical data, an effective texture feature extraction method is proposed based on statistics of wavelet coefficients using tree-structured wavelet transform. The qualitative analysis between extracted features and process parameters indicates that the characteristics can describe different froth status.Secondly, in view of single froth image parameters which cannot accurately reflect flotation state, the paper proposes the ARMA (Auto-Regressive and Moving Average Model) dynamic texture model to describe the correlation between the froth images, and establishes probability density distribution of the image sequence and extracts dynamic texture feature.Finally, owing to multi-classification issue, a model of froth state recognition based on one against one SVM was proposed. Utilizing the extracted texture characteristic vector as the input, the article realized the classification and recognition of the cleaning froth image sequence. Experimental results prove that the model can reflect different froth state with an excellent performance, which provided the foundation for optimal control of flotation process.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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