發(fā)布時(shí)間：2018-06-10 03:58

  本文選題：LOC + PPA�。� 參考：《天津大學(xué)》2016年碩士論文

【摘要】：利用計(jì)算機(jī)視覺(jué)技術(shù)看懂圖像仍是一件非常復(fù)雜的事情,而利用計(jì)算機(jī)完成對(duì)場(chǎng)景的識(shí)別更是一項(xiàng)極具困難的挑戰(zhàn),但人腦卻能夠輕松看懂圖片、識(shí)別場(chǎng)景。因此,通過(guò)借鑒人腦的視覺(jué)信息加工機(jī)制,為計(jì)算機(jī)構(gòu)建模擬人類視覺(jué)和腦部工作機(jī)制的算法和模型,將有助于推動(dòng)計(jì)算機(jī)視覺(jué)領(lǐng)域的發(fā)展和進(jìn)步。本文旨在通過(guò)研究人腦視覺(jué)場(chǎng)景認(rèn)知過(guò)程中,場(chǎng)景內(nèi)、外物體和場(chǎng)景之間的關(guān)聯(lián)機(jī)制,為計(jì)算機(jī)視覺(jué)的發(fā)展提供理論和方法上的依據(jù)。本文使用四類場(chǎng)景圖片(包括兩類室內(nèi)場(chǎng)景和兩類室外場(chǎng)景)、每類場(chǎng)景內(nèi)的兩個(gè)顯著性物體圖片、不在場(chǎng)景中出現(xiàn)但和場(chǎng)景語(yǔ)義相關(guān)的兩個(gè)物體圖片作為刺激材料。利用功能磁共振(fMRI)記錄受試在觀看這些刺激材料時(shí)的大腦活動(dòng)信號(hào)。通過(guò)對(duì)場(chǎng)景內(nèi)、外物體和場(chǎng)景的激活模式在不同感興趣區(qū)域進(jìn)行多體素模式相關(guān)分析和回歸分析發(fā)現(xiàn),在LOC(兩側(cè)枕部復(fù)雜區(qū)域)的兩個(gè)子區(qū)LO(兩側(cè)枕部區(qū)域)、pF(梭狀回后部區(qū)域)和PPA(海馬旁回區(qū)域)腦區(qū),物體與場(chǎng)景的激活模式顯著正相關(guān),且場(chǎng)景外物體與場(chǎng)景的關(guān)聯(lián)強(qiáng)度顯著小于場(chǎng)景內(nèi)物體與場(chǎng)景的關(guān)聯(lián)強(qiáng)度。對(duì)比腦區(qū)之間關(guān)聯(lián)強(qiáng)度發(fā)現(xiàn)LO和p F腦區(qū)所表征的關(guān)聯(lián)強(qiáng)度顯著強(qiáng)于PPA腦區(qū)。進(jìn)一步分析發(fā)現(xiàn),LO和pF表征的物體與場(chǎng)景關(guān)聯(lián)強(qiáng)度對(duì)于室內(nèi)、室外場(chǎng)景存在差異,PPA區(qū)域沒(méi)發(fā)現(xiàn)該差異。上述發(fā)現(xiàn)在RSC(扣帶回皮層)腦區(qū)域均沒(méi)有觀察到。基于上述結(jié)果推測(cè),LO、pF和PPA腦區(qū)參與了場(chǎng)景內(nèi)、外物體與場(chǎng)景相關(guān)聯(lián)的信息加工,共同完成物體與場(chǎng)景關(guān)聯(lián)關(guān)系處理的過(guò)程。其中LO和pF主要的表征語(yǔ)義關(guān)聯(lián),PPA主要表征場(chǎng)景空間特征和語(yǔ)義關(guān)聯(lián),且均對(duì)場(chǎng)景的開(kāi)闊程度敏感,RSC不表征場(chǎng)景與物體的關(guān)聯(lián)關(guān)系。
[Abstract]:Using computer vision technology to understand images is still a very complicated task, and it is a very difficult challenge to use computer to realize the recognition of scenes, but the human brain can easily understand pictures and recognize scenes. Therefore, it is helpful to promote the development and progress of computer vision field by drawing lessons from human brain visual information processing mechanism and constructing algorithms and models for computer to simulate human vision and brain working mechanism. The purpose of this paper is to provide theoretical and methodological basis for the development of computer vision by studying the correlation mechanism between external objects and scenes in the cognitive process of human visual scene. In this paper, four kinds of scene pictures (including two kinds of indoor scenes and two kinds of outdoor scenes) are used as stimulus materials. Two images of significant objects in each type of scene are used as stimulative materials, which are not present in the scene but are related to the scene semantics. Functional Magnetic Resonance Imaging (fMRI) was used to record brain activity during viewing these stimuli. Based on the correlation analysis and regression analysis of multiple voxel patterns in different regions of interest, the activation patterns of external objects and scenes in the scene were analyzed and analyzed by regression analysis. In the two subregions of LOC (bilateral occipital complex region) (bilateral occipital region, posterior region of fusiform gyrus) and PPA (region of parahippocampal gyrus), there was a significant positive correlation between object and scene activation mode. And the correlation intensity between objects and scenes is significantly lower than that between objects and scenes. Compared with the correlation intensity between brain regions, it was found that the correlation intensity of Lo and PF brain regions was significantly stronger than that of PPA brain regions. Further analysis found that the correlation strength between objects and scenes represented by Lo and PF was different in indoor and outdoor scenes but not found in PPA area. None of these findings were observed in the RSC (cingulate cortex) brain region. Based on the above results, it is inferred that the brain regions of LOPF and PPA are involved in the processing of the information associated with the scene and the external objects are involved in the processing of the correlation relationship between the objects and the scene, and the process of processing the relationship between the objects and the scene is completed together. Among them, Lo and PF mainly represent semantic association and PPA mainly represent spatial feature and semantic association of scene, and both are sensitive to the open degree of scene. RSC does not represent the relation between scene and object.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP391.41

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