發(fā)布時間：2018-05-12 21:11

  本文選題：圖像分割 + CV模型��； 參考：《哈爾濱工程大學(xué)學(xué)報》2017年05期

【摘要】：針對Chan-Vese(CV)模型對含有偽影、光照不均的圖像不能進行有效分割的不足,本文提出了結(jié)合熵與局部信息的動態(tài)偽影偏差場修正CV模型。模型根據(jù)區(qū)域同質(zhì)性特征,利用熵構(gòu)造區(qū)域能量系數(shù),自動調(diào)節(jié)目標與背景區(qū)域在模型中的權(quán)重。采用全局與局部結(jié)合的方式自適應(yīng)控制區(qū)域演化。將偽影指示函數(shù)應(yīng)用到區(qū)域檢測項,無需先驗灰度信息即可消除異常值,精確地使像素歸類。結(jié)合Retinex理論對圖像進行分解,忽略亮度變化并提取不含照度信息的目標結(jié)構(gòu)圖像,避免偏差場對分割的影響。通過與CV模型、LIF模型對比驗證了算法的有效性,結(jié)果表明,本文提出的算法在目標干擾嚴重情況下分割性能最優(yōu),重疊率可達0.9,誤分割率控制在0.06以內(nèi)。與CV模型、LIF模型相比分割精度與速度性能優(yōu)勢明顯。
[Abstract]:Aiming at the deficiency of Chan-Vesegne CV) model which can not effectively segment images with artifacts and uneven illumination, a modified CV model combining entropy and local information is proposed in this paper. According to the characteristics of regional homogeneity, the model uses entropy to construct regional energy coefficient, and automatically adjusts the weight of target and background region in the model. The global and local combination is adopted to control the evolution of the region adaptively. The artifact indication function is applied to the region detection item, and the outliers can be eliminated without prior gray level information, and the pixels can be classified accurately. Based on the Retinex theory, the image is decomposed, the brightness change is ignored and the target structure image without illumination information is extracted to avoid the effect of the deviation field on the segmentation. The validity of the proposed algorithm is verified by comparing it with the CV model / LIF model. The results show that the proposed algorithm has the best segmentation performance in the case of serious target interference, with an overlap rate of 0.9 and an error segmentation rate of less than 0.06. Compared with the CV model and the LIF model, the segmentation accuracy and the speed performance are obvious.
【作者單位】： 南京理工大學(xué)機械工程學(xué)院;
【基金】：國家自然科學(xué)基金項目(61105094) 江蘇省科研創(chuàng)新計劃(CXLX12-0189)
【分類號】：TP391.41

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