本文選題：不完整信息 + Gabor局部顯著性��； 參考：《北京林業(yè)大學》2016年博士論文

【摘要】：作為世界著名的植物表型研究中心-澳大利亞表型工廠和南澳大學生物信息與表型研究中心將以計算機視覺技術為基礎的植物表型學應用在小麥育種中,培育具有環(huán)境適應性的優(yōu)良小麥品種。經(jīng)過多年的長期記錄積累了豐富的小麥生長的多光譜表型圖像。希望在現(xiàn)有小麥表型分析中添加麥穗的表型研究以實現(xiàn)培育高產(chǎn)小麥品種的目的。在小麥表型分析的圖像中,小麥是圖像的主體,其信息通過成像設備得到充分的表達。但麥穗作為小麥的組成部分,并不屬于成像的主體,其信息的表達嚴重不充分,因此屬于不完整信息。文章利用澳大利亞表型工廠和南澳大學生物信息與表型研究中心提供的小麥可見光表型圖像,以不完整信息下的麥穗識別為目標展開相關技術研究。研究內容包括不完整信息下麥穗圖像的處理方法；麥穗圖像特征的提��；麥穗識別模型的建立。文章對現(xiàn)有計算機視覺中的物體識別技術進行了分析,結果表明由于麥穗的不完整信息及小麥生長的自然特性,是無法應用目前的物體識別技術。在對小麥表型圖像的研究基礎上,提出了通過紋理分析的方法實現(xiàn)麥穗的識別。文章從空間域和頻率域兩個方面對麥穗的識別進行了研究。研究分析的結果表明在不完整信息背景下,無法用單一判別模型實現(xiàn)高準確性的麥穗識別,而必須采用層次式的模型結構：基于弱分類器的麥穗?yún)^(qū)域判別模型和基于強分類器的麥穗識別模型。針對小麥生長中由于相互遮擋、混雜、品種變化等復雜條件而造成麥穗無法識別的情況,文章在分析人類視覺注意機制的基礎上提出Gabor:城局部顯著性方法實現(xiàn)復雜條件下的麥穗識別。結果表明Gabor域局部顯著性方法可以很好的實現(xiàn)復雜條件下的麥穗?yún)^(qū)域判斷要求。在確定使用Gabor域局部顯著性作為第一級區(qū)域判斷模型的基礎上,文章又針對第二級強分類器模型進行研究。在分析研究目前區(qū)域特征描述方法的基礎上,確定利用局部二值特征(LBP)作為區(qū)域特征。經(jīng)過對傳統(tǒng)機器學習方法的研究,選擇支持向量機(SVM)作為強分類器模型。研究中將確定后的層次模型應用在所有測試圖像上,最終達到了91.37%的正確性。同時指出由于不完整信息的影響,在當前的小麥表型圖像中只有當麥穗長到6.5mm時才能被檢測出來。相對于傳統(tǒng)機器學習的淺層學習方法,文章提出將深層次的學習方法用于不完整環(huán)境下的麥穗識別中。用深度模型作為層次模型的強分類器模型。文章建立了三個不同深度的卷積網(wǎng)絡模型并進行研究,提出了對于具有千萬個以上未知參數(shù)的深度網(wǎng)絡的訓練方法(預訓練與Fine-Tune相結合)、訓練數(shù)據(jù)的均衡化的方法、訓練圖片預處理方法。最終僅使用八百多個數(shù)據(jù)完成了深度為13層,網(wǎng)絡未知參數(shù)為1000萬的網(wǎng)絡訓練并獲得麥穗判別模型,同時該模型的準確率達到98%,在GPU的平臺下單一區(qū)域處理時間為0.06ms。顯示了深度學習強大的分析判斷能力。
[Abstract]:As the world's famous plant phenotypic Research Center - the Australian phenotypic factory and the University of South Australia bioinformatics and phenotypic Research Center, plant phenotypes based on computer vision technology are applied to wheat breeding to breed excellent wheat varieties with environmental adaptability. After years of long-term records, rich wheat has been accumulated. The multispectral phenotypic image of the growing wheat is expected to be added to the phenotypic analysis of wheat phenotypes to achieve the purpose of cultivating high yield wheat varieties. In the image analysis of the wheat phenotype, wheat is the main body of the image, and its information is fully expressed through the imaging equipment. But the wheat ear is not a component part of the wheat. The main body of the image is not fully expressed, so it belongs to incomplete information. The article uses the Australian phenotypic factory and the University of South Australia bioinformatics and phenotypic research center to study the visible light phenotypic images of wheat under the incomplete information. The research includes incomplete information. The processing method of the image of the ear of wheat, the extraction of the feature of the ear of wheat and the establishment of the model of wheat ear recognition. The article analyses the object recognition technology in the existing computer vision. The results show that the incomplete information of the wheat ear and the natural characteristics of the wheat growth can not be applied to the present technology of object recognition. On the basis of the research, the recognition of wheat ear is realized by the method of texture analysis. The paper studies the recognition of wheat ear from two aspects of spatial domain and frequency domain. The results show that high accurate ear recognition can not be realized with a single discriminant model in the background of incomplete information, but the hierarchical type must be adopted. Model structure: wheat ear region discrimination model based on weak classifier and wheat ear recognition model based on strong classifier. In view of the situation that wheat ear can not be identified because of the complex conditions such as mutual occlusion, hybrid and variety change in wheat growth, the article puts forward the local saliency side of Gabor: city on the basis of the analysis of human visual attention mechanism. The method realizes the recognition of wheat ear under complex conditions. The results show that the local saliency method in the Gabor domain can well realize the requirement of wheat ear region judgment under complex conditions. On the basis of determining the local saliency in the Gabor domain as the first level regional judgment model, the paper also studies the second level strong classifier model. On the basis of the current regional feature description method, the local two value feature (LBP) is used as the regional feature. After the study of the traditional machine learning method, the support vector machine (SVM) is selected as the strong classifier model. The determined hierarchical model is applied to all the test images, and the correctness of the model is achieved at the same time, at the same time, 91.37%. It is pointed out that the current wheat phenotypic image can be detected only when the wheat ear is long to 6.5mm because of the influence of incomplete information. Compared with the traditional learning method, the deep learning method is applied to the ear recognition in the incomplete environment. The depth model is used as the strong classification of the hierarchical model. In this paper, three convolution networks with different depths are established and studied. A training method for deep networks with more than ten thousand unknown parameters (pre training and Fine-Tune), a method of balancing the training data, and the pre training method of the picture are trained. Finally, only more than 800 data are used to complete the depth of the network. At the same time, the accuracy of the model is 98%. The accuracy of the model is up to 98%. The single area processing time of the GPU platform shows a powerful analysis and judgment ability in depth learning under the GPU platform.

【學位授予單位】：北京林業(yè)大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：TP391.41

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