本文關(guān)鍵詞： 高光譜波段選擇 壓力識別 多頭絨泡菌算法 線性預(yù)測 禁忌搜索　出處：《西南大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：針對生理信號非接觸識別壓力的研究是情感計(jì)算的一項(xiàng)重要內(nèi)容,而基于高光譜技術(shù)提取的人臉血氧含量可用于壓力的非接觸識別。高光譜圖像在包含豐富信息的同時(shí),其龐大的數(shù)據(jù)量、眾多的波段、相鄰波段極強(qiáng)的相關(guān)性等也給壓力識別的實(shí)時(shí)處理帶來了很大的困難,解決這個(gè)問題的關(guān)鍵就是減少高光譜圖像的波段數(shù)量,波段選擇是最為常用的波段降維方式,故本文在基于高光譜技術(shù)提取的人臉血氧含量用于壓力識別的基礎(chǔ)上,針對高光譜圖像的波段選擇算法進(jìn)行研究。主要工作如下:(1)提出了一種改進(jìn)的基于多頭絨泡菌模型的高光譜圖像波段選擇算法。多頭絨泡菌模型能夠解決最短路徑問題,該問題與高光譜波段選擇中尋找最小冗余波段組合的問題相似。本文將多頭絨泡菌算法應(yīng)用到高光譜波段選擇中,通過絨泡菌網(wǎng)絡(luò)自組織、自優(yōu)化的特性對波段進(jìn)行選擇。此外,為了實(shí)現(xiàn)波段的自動(dòng)選擇,提出了基于互信息的非均勻子空間劃分方式,同時(shí)采用了最佳指數(shù)倒數(shù)作為適應(yīng)函數(shù)參與波段選擇。實(shí)驗(yàn)結(jié)果表明使用非均勻劃分子空間方式比傳統(tǒng)的均勻劃分方式的效果更好,采用最佳指數(shù)倒數(shù)這種適應(yīng)函數(shù)選擇的波段效果比采用傳統(tǒng)的相關(guān)性好,使用多頭絨泡菌算法選擇的波段組合的壓力識別效果接近于全部波段,證明了算法的有效性。(2)提出結(jié)合線性預(yù)測與禁忌搜索的高光譜圖像波段選擇算法。結(jié)合壓力狀態(tài)下的人臉高光譜數(shù)據(jù),將線性預(yù)測獲取的較優(yōu)組合作為禁忌搜索的初始解,以壓力的分類準(zhǔn)確率作為適應(yīng)函數(shù),利用禁忌搜索算法優(yōu)化波段組合使之具有更高的壓力分類準(zhǔn)確率。實(shí)驗(yàn)結(jié)果表明結(jié)合禁忌搜索算法選擇的波段對壓力的識別更精確,證明了算法的有效性。(3)設(shè)計(jì)心理壓力誘發(fā)實(shí)驗(yàn),獲取了25名普通大學(xué)生的面部高光譜圖像數(shù)據(jù),包括心理壓力和平靜狀態(tài)。此外,對采集的數(shù)據(jù)進(jìn)行預(yù)處理,提出了一種基于血氧直方圖的端元自動(dòng)提取方法,將該方法產(chǎn)生的樣本光譜曲線數(shù)據(jù)用于波段選擇;采用了最小噪聲分離法去除高光譜數(shù)據(jù)中的噪聲,實(shí)驗(yàn)結(jié)果表明去噪后的數(shù)據(jù)比未去噪數(shù)據(jù)產(chǎn)生的面部血氧圖更清晰,更利于波段選擇后的壓力識別。本文利用采集的高光譜數(shù)據(jù)對提出的兩種算法做了驗(yàn)證。通過驗(yàn)證分析表明,本文提出的兩種波段算法在保持壓力識別準(zhǔn)確率的情況下,能夠大量的減少波段數(shù)量,對基于高光譜技術(shù)的壓力識別來說是有效的波段選擇方法。
[Abstract]:The research on non-contact recognition pressure of physiological signals is an important part of emotional calculation. However, the extraction of blood oxygen content based on hyperspectral technology can be used in the non-contact recognition of pressure. Hyperspectral images contain rich information, its huge amount of data, numerous bands. The extremely strong correlation of adjacent bands also brings great difficulties to the real-time processing of pressure recognition. The key to solve this problem is to reduce the number of bands of hyperspectral images. Band selection is the most commonly used wave band dimension reduction method, so this paper based on the extraction of facial blood oxygen content based on hyperspectral technology for pressure recognition. The band selection algorithm of hyperspectral images is studied. The main work is as follows: 1). An improved band selection algorithm for hyperspectral image based on multi-headed actinomycetes model is proposed, which can solve the shortest path problem. This problem is similar to the problem of finding the least redundant band combination in the hyperspectral band selection. In this paper, we apply the multi-headed actinomycetes algorithm to the hyperspectral band selection, and self-organize through the chorionic bacteria network. In addition, in order to realize the automatic band selection, a non-uniform subspace partition method based on mutual information is proposed. At the same time, the optimal exponent reciprocal is used as the adaptive function to participate in the band selection. The experimental results show that the non-uniform partition subspace method is more effective than the traditional uniform partition method. The effect of the band selection using the best index reciprocal fitness function is better than that of the traditional correlation, and the pressure recognition effect of the band combination selected by the multi-headed actinomycetes algorithm is close to that of the whole band. Proved the effectiveness of the algorithm. 2) proposed a combination of linear prediction and Tabu search hyperspectral image band selection algorithm. Combined with the pressure of the hyperspectral face data. The optimal combination obtained by linear prediction is regarded as the initial solution of Tabu search, and the classification accuracy of pressure is taken as the fitness function. The Tabu search algorithm is used to optimize the combination of bands to achieve higher accuracy of pressure classification. The experimental results show that the band selected by Tabu search algorithm is more accurate to identify the pressure. Proved the validity of the algorithm. 3) designed the psychological stress induced experiment, obtained the facial hyperspectral image data of 25 ordinary college students, including psychological stress and calm state. Based on the preprocessing of the collected data, a method based on the blood oxygen histogram is proposed to extract the endcomponents automatically. The sample spectral curve data generated by this method are used to select the band. The minimum noise separation method is used to remove the noise from the hyperspectral data. The experimental results show that the data after denoising is more clear than the facial blood oxygen map produced by the non-denoising data. In this paper, we use the collected hyperspectral data to verify the proposed two algorithms. The two band algorithms proposed in this paper can greatly reduce the number of bands while maintaining the accuracy of pressure recognition, which is an effective band selection method for pressure recognition based on hyperspectral technology.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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