本文選題：浮選圖像 + 特征提取�。� 參考：《昆明理工大學(xué)》2017年碩士論文

【摘要】：浮選在金屬生產(chǎn)中是一個(gè)重要的一環(huán),在浮選車間中,通過(guò)一定條件下,然后加入適量的藥劑及適量的空氣,然后可以開(kāi)始攪拌使浮選藥劑與金屬進(jìn)行充分的接觸,這樣才能在攪拌的過(guò)程中出現(xiàn)很多的浮選泡沫,最后通過(guò)對(duì)這些泡沫的回收利用,從泡沫當(dāng)中提取出原礦品。浮選泡沫的特征如大小,形狀,顏色。浮選泡沫大小不一致,浮選泡沫之間黏連性較強(qiáng),表面紋理復(fù)雜等特點(diǎn)。浮選過(guò)程大多依靠人工觀察浮選泡沫狀態(tài),但因此也會(huì)對(duì)人體產(chǎn)生不必要的傷害,導(dǎo)致浮選過(guò)程難以優(yōu)化。本論文主要介紹了人工控制下的浮選會(huì)造成對(duì)資源的浪費(fèi),人體的危害,以及縮小資源回收率等問(wèn)題,為解決這些問(wèn)題,我們考慮通過(guò)計(jì)算機(jī)圖像處理技術(shù)來(lái)代替人工控制,這就需要將圖像處理技術(shù)應(yīng)用到浮選泡沫圖像處理中。同時(shí)介紹了浮選泡沫圖像處理過(guò)程的復(fù)雜性,及泡沫圖像處理的困難性,如果處理成功,將大大提高金屬的回收率,降低生產(chǎn)成本,減少資源浪費(fèi),同時(shí)降低對(duì)人工的需求,這樣也可以避免浮選車間環(huán)境對(duì)人體的危害,提高整體的生產(chǎn)效率,提高整個(gè)浮選,有色金屬回收行業(yè)的生產(chǎn)效率。從而促進(jìn)整個(gè)國(guó)民經(jīng)濟(jì)的發(fā)展,進(jìn)而也能擴(kuò)大計(jì)算機(jī)圖像處理技術(shù)的應(yīng)用發(fā)展空間,促進(jìn)計(jì)算機(jī)圖像處理技術(shù)超前邁進(jìn)。在論文中我們主要從浮選泡沫圖像采集,到浮選泡沫圖像去噪,緊接著浮選泡沫圖像分割然后對(duì)進(jìn)行浮選泡沫圖像特征提取這一過(guò)程進(jìn)行了詳細(xì)的描述,并針對(duì)浮選泡沫圖像特征提取的三個(gè)方面:大小,形狀,顏色來(lái)做詳細(xì)的解釋。同時(shí)又由于浮選過(guò)程對(duì)實(shí)時(shí)性的要求,我們考慮引入GPU及多線程來(lái)提高浮選泡沫圖像處理技術(shù)的效率。經(jīng)過(guò)兩年的努力,在老師及同學(xué)的幫助下主要實(shí)現(xiàn)了以下工作:本文針對(duì)浮選泡沫的特點(diǎn),分析提取浮選泡沫的大小,形狀及顏色等特征。本論文主要研究工作如下:(1)通過(guò)小波分析及多尺度理論,對(duì)浮選泡沫的尺寸特征進(jìn)行提取。(2)通過(guò)研究浮選泡沫圖像分割技術(shù),例如分水嶺分割算法,來(lái)提取浮選圖像形狀特征。(3)通過(guò)分析比較浮選圖像RGB顏色值分布,提取浮選圖像顏色特征。(4)對(duì)相關(guān)程序進(jìn)行優(yōu)化,利用GPU基于CUDA平臺(tái)增強(qiáng)圖像處理的實(shí)時(shí)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.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TD923;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 劉小波;;浮選泡沫圖像主成分的分類與識(shí)別[J];云南冶金;2014年03期

2 許景波;袁怡寶;劉泊;林海軍;李建新;;時(shí)域測(cè)量中高斯低通濾波器的有理逼近設(shè)計(jì)方法[J];固體電子學(xué)研究與進(jìn)展;2011年06期

3 陳志強(qiáng);郭永亮;陳詩(shī)哲;;基于小波變換與改進(jìn)的Roberts算子融合的圖像邊緣檢測(cè)[J];傳感器世界;2011年04期

4 何桂春;馮金妮;吳藝鵬;鄭錫聯(lián);;浮選泡沫圖像處理技術(shù)研究現(xiàn)狀與進(jìn)展[J];有色金屬科學(xué)與工程;2011年02期

5 劉金平;桂衛(wèi)華;牟學(xué)民;唐朝暉;李建奇;;基于Gabor小波的浮選泡沫圖像紋理特征提取[J];儀器儀表學(xué)報(bào);2010年08期

6 周開(kāi)軍;陽(yáng)春華;牟學(xué)民;桂衛(wèi)華;;基于泡沫特征與LS-SVM的浮選回收率預(yù)測(cè)[J];儀器儀表學(xué)報(bào);2009年06期

7 陽(yáng)春華;周開(kāi)軍;牟學(xué)民;桂衛(wèi)華;;基于計(jì)算機(jī)視覺(jué)的浮選泡沫顏色及尺寸測(cè)量方法[J];儀器儀表學(xué)報(bào);2009年04期

8 楊衛(wèi)莉;郭雷;;基于分水嶺算法和圖論的圖像分割[J];計(jì)算機(jī)工程與應(yīng)用;2007年07期

9 劉明艷;趙景秀;孫寧;;用Prewitt算子細(xì)化邊緣[J];光電子技術(shù);2006年04期

10 顧欽;基于GPU計(jì)算的虛擬現(xiàn)實(shí)仿真系統(tǒng)設(shè)計(jì)模型[J];微處理機(jī);2005年02期

相關(guān)碩士學(xué)位論文 前3條

1 趙彥;基于GPU技術(shù)的增強(qiáng)現(xiàn)實(shí)系統(tǒng)[D];電子科技大學(xué);2009年

2 胡珊;圖像檢索中基于SVM的相關(guān)反饋技術(shù)研究[D];西北工業(yè)大學(xué);2007年

3 侯黎黎;利用圖像處理技術(shù)實(shí)現(xiàn)浮選泡沫分類的研究[D];吉林大學(xué);2006年

，

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