發(fā)布時間：2018-03-02 10:28

  本文關(guān)鍵詞： 子孔徑圖像 像素重組 LBP算法 傅里葉重聚焦方法 人臉檢測　出處：《太原科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科技的發(fā)展,光場成像技術(shù)也扮演了越來越重要的角色。光場相機作為一種新型相機,也獲得了越來越多的關(guān)注。傳統(tǒng)相機拍攝的場景圖像是只記錄場景的二維信息,而光場相機則可以記錄場景的所有光場信息,通過計算后可得到場景的多視點圖像和不同聚焦面的像,甚至可以得到場景的全聚焦圖像。因為光場相機的多視點圖像獲取技術(shù)是不開源的,而多視點圖像的應(yīng)用需求和前景非常巨大,所以本文提出了一種基于微透鏡型光場相機的子孔徑圖像的提取方法;利用了光場相機在重聚焦方面的優(yōu)勢,結(jié)合LBP人臉檢測算法,證明了光場相機拍攝的人臉照片可以較好地檢測到在不同聚焦層的人臉。主要工作和創(chuàng)新點如下:1)提出了一種基于微透鏡型光場相機的子孔徑圖像提取方法。該方法中,首先根據(jù)光場相機所采集到的光場數(shù)據(jù),其主透鏡下的一個子孔徑在CCD上的成像與光場相機中所有微透鏡單元成像區(qū)域具有相同的坐標(biāo),光場相機所得到的子孔徑圖像就相當(dāng)于光場相機主鏡頭光圈減小后生成的像。同理,該像也等同于微透鏡陣列的等效像元陣列所成的像。所以我們要先利用峰值檢測法標(biāo)定出各微透鏡圖像的中心點;之后提取以中心像素點為半徑的一塊明亮區(qū)域,將區(qū)域內(nèi)相同位置的像素點全部提出;最后進(jìn)行重新排列拼成一副完整的具有像素級別的微小視差圖像,該圖像即為所需的子孔徑圖像。2)提出了一種基于光場相機重聚焦功能的人臉檢測應(yīng)用。該方案中,在結(jié)合光場相機數(shù)字對焦和數(shù)字重聚焦的原理下,利用LBP人臉識別算法在紋理清晰的照片上的獨到優(yōu)勢。先利用光場相機的調(diào)焦特性得到許多同一場景下不同焦距的照片;之后利用傅里葉切片定理將圖像中所有人臉全部進(jìn)行重聚焦,將得到的不同深度重聚焦圖進(jìn)行圖像融合后得到一副完整的全聚焦圖像;最后和單一焦距層的照片進(jìn)行人臉檢測結(jié)果對比,結(jié)果證明了經(jīng)過光場相機全聚焦后的圖片可以在不同深度上都檢測到人臉,而單一聚焦層的照片卻只能在聚焦點附近深度才能檢測到人臉,體現(xiàn)了光場相機在人臉檢測方面的潛力和優(yōu)勢。
[Abstract]:With the development of science and technology, optical field imaging technology also plays an increasingly important role. As a new type of camera, optical field camera has attracted more and more attention. The light field camera can record all the light field information of the scene, and the multi-view image of the scene and the image of different focusing plane can be obtained by calculation. The full focus image of the scene can even be obtained, because the multi-view image acquisition technology of the light field camera is not open source, and the application demand and prospect of the multi-view image is very great. Therefore, this paper proposes a subaperture image extraction method based on microlens light field camera, and combines the advantages of light field camera in refocusing and LBP face detection algorithm. It is proved that the face images taken by the light field camera can detect the faces in different focusing layers. The main work and innovation are as follows: 1) A subaperture image extraction method based on the microlens light field camera is proposed. Firstly, according to the light field data collected by the light field camera, the imaging of a sub-aperture under the main lens on the CCD has the same coordinates as all the imaging regions of the microlens unit in the light field camera. The sub-aperture image obtained by the light field camera is equivalent to the image generated after the aperture of the main lens of the light field camera is reduced. This image is also equivalent to the image of the equivalent pixel array of the microlens array. So we first calibrate the center points of each microlens image by using the peak detection method, and then extract a bright region with the radius of the central pixel. All pixels in the same position in the region are proposed. Finally, a complete set of tiny parallax images with pixel level is rearranged. A face detection application based on the refocusing function of a light field camera is proposed. In this scheme, the principle of digital focusing and digital refocusing of the optical field camera is combined. The unique advantage of LBP face recognition algorithm in textured images is presented. Firstly, many images with different focal lengths in the same scene are obtained by using the focusing characteristics of the light field camera. Then all the faces in the image are refocused by Fourier slicing theorem, and a complete set of full focus image is obtained by fusion of the different depth refocusing images. Finally, the results of face detection are compared with those of single focal layer. The results show that the images with full focus of the light field camera can detect the face at different depths. But the single focus layer can only detect the human face in the depth near the focal point, which shows the potential and advantage of the light field camera in face detection.
【學(xué)位授予單位】：太原科技大學(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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本文編號：1556140