浮选泡沫特征提取的图像处理技术的研究

发布时间：2018-04-13 19:56

本文选题：浮选图像 + 特征提取　；参考：《昆明理工大学》2017年硕士论文

【摘要】：浮选在金属生产中是一个重要的一环,在浮选车间中,通过一定条件下,然后加入适量的药剂及适量的空气,然后可以开始搅拌使浮选药剂与金属进行充分的接触,这样才能在搅拌的过程中出现很多的浮选泡沫,最后通过对这些泡沫的回收利用,从泡沫当中提取出原矿品。浮选泡沫的特征如大小,形状,颜色。浮选泡沫大小不一致,浮选泡沫之间黏连性较强,表面纹理复杂等特点。浮选过程大多依靠人工观察浮选泡沫状态,但因此也会对人体产生不必要的伤害,导致浮选过程难以优化。本论文主要介绍了人工控制下的浮选会造成对资源的浪费,人体的危害,以及缩小资源回收率等问题,为解决这些问题,我们考虑通过计算机图像处理技术来代替人工控制,这就需要将图像处理技术应用到浮选泡沫图像处理中。同时介绍了浮选泡沫图像处理过程的复杂性,及泡沫图像处理的困难性,如果处理成功,将大大提高金属的回收率,降低生产成本,减少资源浪费,同时降低对人工的需求,这样也可以避免浮选车间环境对人体的危害,提高整体的生产效率,提高整个浮选,有色金属回收行业的生产效率。从而促进整个国民经济的发展,进而也能扩大计算机图像处理技术的应用发展空间,促进计算机图像处理技术超前迈进。在论文中我们主要从浮选泡沫图像采集,到浮选泡沫图像去噪,紧接着浮选泡沫图像分割然后对进行浮选泡沫图像特征提取这一过程进行了详细的描述,并针对浮选泡沫图像特征提取的三个方面:大小,形状,颜色来做详细的解释。同时又由于浮选过程对实时性的要求,我们考虑引入GPU及多线程来提高浮选泡沫图像处理技术的效率。经过两年的努力,在老师及同学的帮助下主要实现了以下工作:本文针对浮选泡沫的特点,分析提取浮选泡沫的大小,形状及颜色等特征。本论文主要研究工作如下:(1)通过小波分析及多尺度理论,对浮选泡沫的尺寸特征进行提取。(2)通过研究浮选泡沫图像分割技术,例如分水岭分割算法,来提取浮选图像形状特征。(3)通过分析比较浮选图像RGB颜色值分布,提取浮选图像颜色特征。(4)对相关程序进行优化,利用GPU基于CUDA平台增强图像处理的实时性。
[Abstract]:Floatation is an important ring in metal production. In the floatation workshop, under certain conditions, appropriate amount of reagents and proper air are added, and then stirring can be started to make the flotation reagents in full contact with the metal.In this way, a lot of floatation foam can appear in the process of stirring. Finally, through the recovery and utilization of these foams, the raw ore can be extracted from the foam.Flotation foam features such as size, shape, color.The size of flotation foam is different, the adhesion between flotation foam is strong, the surface texture is complex and so on.The flotation process mostly depends on the artificial observation of the flotation foam state, but it will also cause unnecessary harm to the human body, resulting in the flotation process is difficult to optimize.This paper mainly introduces the problems that floatation under manual control will cause waste of resources, harm to human body, and reduce the recovery rate of resources. In order to solve these problems, we consider replacing manual control with computer image processing technology.Therefore, it is necessary to apply image processing technology to flotation foam image processing.At the same time, the complexity of flotation foam image processing and the difficulty of foam image processing are introduced. If the processing is successful, the recovery rate of metals will be greatly increased, the production cost will be reduced, the waste of resources will be reduced, and the demand for manpower will be reduced.In this way, the environment of floatation workshop can avoid the harm to human body, improve the overall production efficiency, and improve the production efficiency of the whole floatation and non-ferrous metal recovery industry.Thus, the development of the whole national economy can be promoted, and the application and development space of the computer image processing technology can also be expanded, and the computer image processing technology can be advanced.In the paper we mainly from flotation foam image acquisition to flotation foam image de-noising followed by flotation foam image segmentation and then the flotation foam image feature extraction process is described in detail.Three aspects of feature extraction of flotation foam image: size, shape and color are explained in detail.At the same time, due to the real-time requirement of flotation process, we consider introducing GPU and multi-thread to improve the efficiency of flotation foam image processing technology.With the help of teachers and students, the following work has been realized: according to the characteristics of flotation foam, the size, shape and color of flotation foam are analyzed and extracted in this paper.The main research work of this thesis is as follows: (1) by wavelet analysis and multi-scale theory, the size characteristics of flotation foam are extracted. (2) by studying flotation foam image segmentation techniques, such as watershed segmentation algorithm,By analyzing and comparing the distribution of RGB color value of flotation image, extracting the color feature of flotation image, we optimize the relevant program, and use GPU to enhance the real-time performance of image processing based on CUDA platform.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TD923;TP391.41

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