郭二軍
摘 ?要: 傳統(tǒng)的圖像匹配融合方法在匹配多重紋理圖像時(shí),很容易出現(xiàn)誤差匹配,融合后的圖像清晰度不高,輪廓不鮮明,針對(duì)上述問(wèn)題,在云平臺(tái)網(wǎng)絡(luò)上研究了一種新的多重紋理圖像匹配融合方法。首先,計(jì)算多重紋理圖像的匹配代價(jià),分析圖像像素的相似度和特異性,構(gòu)建動(dòng)態(tài)規(guī)劃路徑,在不同網(wǎng)絡(luò)結(jié)構(gòu)下匹配多重紋理圖像;然后,建立樹(shù)狀圖對(duì)圖像進(jìn)行融合;最后,利用視察矯正方法將匹配融合得到的誤差點(diǎn)消除。為驗(yàn)證該方法的工作效果,與傳統(tǒng)匹配融合方法進(jìn)行實(shí)驗(yàn)對(duì)比,結(jié)果表明,給出的方法能夠清晰地得到像素點(diǎn)云,使融合后的圖像輪廓鮮明,畫(huà)質(zhì)清晰,適用于圖像重構(gòu)。
關(guān)鍵詞: 云平臺(tái); 網(wǎng)絡(luò)圖像; 多重紋理圖像; 圖像匹配; 圖像融合; 融合方法
中圖分類號(hào): TN911.73?34; TP391.41 ? ? ? ? ? ? ? ? 文獻(xiàn)標(biāo)識(shí)碼: A ? ? ? ? ? ? ? ?文章編號(hào): 1004?373X(2019)19?0059?05
Abstract: The traditional image matching and fusion methods are prone to error matching when matching of multi?texture images is conducted. The fused image is not clear and its contour is not clear. To solve the above problems, a new multi?texture image matching and fusion method is studied on cloud platform network. Firstly, the matching cost of multi?texture image is calculated, the similarity and specificity of image pixels are analyzed, the dynamic programming path is constructed, and the multi?texture image is matched under the condition of different network structures. Then, the tree image is established for image fusion. Finally, the error points obtained by matching fusion are eliminated with inspection correction method. In order to verify the effectiveness of this method, some experiments are carried out for comparison with traditional matching and fusion methods. The results show that the proposed method can obtain the pixel point?cloud clearly, which makes the fused image contour distinct and quality clear, and is suitable for image reconstruction.
Keywords: cloud platform; network image; multi?texture image; image matching; image fusion; fusion method
圖像紋理是能夠表述圖像表面和結(jié)構(gòu)的基本屬性,通過(guò)圖像的平均亮度、最大亮度、最小亮度、圖像尺寸、圖形形狀來(lái)描述[1]。紋理元素隨機(jī)建立空間關(guān)系,經(jīng)過(guò)一段時(shí)間,圖像紋理之間的基元呈現(xiàn)相關(guān)性關(guān)系。多重紋理圖像的匹配融合在創(chuàng)建逼真的三維模型中發(fā)揮著重要的作用,在廣告、動(dòng)畫(huà)、視頻等領(lǐng)域有著廣闊的發(fā)展空間。目前研究的網(wǎng)絡(luò)多重紋理圖像匹配融合技術(shù)多是利用人機(jī)交互界面,雖然取得的圖像精度很高,但是操作過(guò)程復(fù)雜,自動(dòng)化效果差,在規(guī)劃時(shí)僅能使用一條掃描線,很容易出現(xiàn)匹配錯(cuò)誤,尤其是對(duì)于一些紋理不夠充分或者是局部區(qū)域有重復(fù)特征的圖像,目前方法的缺點(diǎn)更加明顯[2]。
相較普通圖像而言,多重紋理圖像結(jié)構(gòu)復(fù)雜,匹配融合更加困難。本文在云平臺(tái)網(wǎng)絡(luò)中分別對(duì)多重紋理圖像的匹配方法和融合方法進(jìn)行研究,內(nèi)部設(shè)立了立體視覺(jué)系統(tǒng),利用攝像機(jī)鎖定目標(biāo),在三維網(wǎng)絡(luò)和二維網(wǎng)絡(luò)中完成匹配和融合工作,并將多重紋理圖像的信息進(jìn)行恢復(fù)。在平面視覺(jué)和立體視覺(jué)領(lǐng)域,圖像匹配和融合是最關(guān)鍵的兩個(gè)步驟[3]。利用匹配得到的視差圖測(cè)量物體景深,在不同的約束條件下,有著不同的匹配和融合方法,分別是針對(duì)小區(qū)域進(jìn)行匹配和融合以及針對(duì)全局進(jìn)行匹配和融合[4]。多重紋理圖像的小區(qū)域匹配融合工作要比全局匹配融合工作簡(jiǎn)單,產(chǎn)生的誤差也小。本文引入圖像重構(gòu)算法,研究像素點(diǎn)與像素點(diǎn)之間的相似性,分析圖像自身的特異性,使匹配的圖像梯度不斷加大,利用視差圖矯正誤差匹配點(diǎn)和誤差融合點(diǎn)。
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