基于主動輪廓模型的光譜圖像分割算法研究
發(fā)布時間：2018-12-24 12:36
【摘要】：在圖像處理與計算機視覺領(lǐng)域,圖像分割是最基礎(chǔ)最重要的問題之一。近年來,國內(nèi)外學(xué)者對基于主動輪廓模型的圖像分割算法研究比較積極和深入,但現(xiàn)有的主動輪廓分割算法還缺乏普適性,分割精度也有待提高,因此,還需進一步探索研究針對光譜圖像進行處理的分割算法�；诖�,本文針對幾何主動輪廓模型中水平集方程的改進開展相應(yīng)研究,并將其成功應(yīng)用于二維單波段紅外圖像及三維多光譜圖像的分割。本文主要提出并研究了兩種新型的光譜圖像主動輪廓分割模型:(1)自適應(yīng)的基于多特征的紅外圖像分割模型。該模型引入自適應(yīng)權(quán)重系數(shù)整合全局符號壓力函數(shù)和基于多種局部特征信息的符號壓力函數(shù),以實現(xiàn)水平集方程的改進;局部項利用高斯核函數(shù)嵌入多種統(tǒng)計特征信息和紋理信息,能夠更加全面地表征圖像各類區(qū)域。對不同場景下的紅外圖像進行實驗,并與傳統(tǒng)的主動輪廓模型以及邊緣檢測算法進行對比,結(jié)果表明:該模型受背景噪聲影響小,且能夠?qū)崿F(xiàn)對灰度不均勻、邊界模糊和對比度低的紅外圖像的有效分割。(2)基于空間-光譜信息的多光譜圖像分割模型。其核心在于構(gòu)造一個新型基于空間—光譜信息的符號壓力函數(shù)。一方面,通過計算比較輪廓內(nèi)外主成分大小,作為判斷輪廓演化方向的準(zhǔn)則;另一方面,利用權(quán)重系數(shù)將距離測度和光譜形狀測度綜合,作為向相應(yīng)方向演化時的演化力大小。對AOTF多光譜成像系統(tǒng)采集的多光譜圖像進行實驗,結(jié)果表明:相比于對單一譜段進行處理的傳統(tǒng)主動輪廓模型,該模型充分利用豐富的光譜信息,分割精度更高;相比于經(jīng)典多光譜圖像非監(jiān)督分類算法,該模型不受細(xì)節(jié)信息干擾,能得到更突出的多光譜圖像目標(biāo)輪廓。
[Abstract]:In the field of image processing and computer vision, image segmentation is one of the most basic and important problems. In recent years, scholars at home and abroad have been active and in-depth research on image segmentation algorithm based on active contour model, but the existing active contour segmentation algorithm is still lack of universality, and the segmentation accuracy needs to be improved. It is also necessary to further explore the segmentation algorithm for spectral image processing. Based on this, the improvement of the level set equation in the geometric active contour model is studied in this paper, and it is successfully applied to the segmentation of two dimensional single band infrared image and three dimensional multispectral image. In this paper, two novel active contour segmentation models for spectral images are proposed and studied: (1) an adaptive infrared image segmentation model based on multiple features is proposed. In order to improve the level set equation, the adaptive weight coefficient is introduced to integrate the global symbolic pressure function and the symbolic pressure function based on a variety of local characteristic information. Using Gao Si kernel function to embed a variety of statistical feature information and texture information, local terms can more comprehensively represent various regions of the image. The infrared images in different scenes are tested and compared with the traditional active contour model and edge detection algorithm. The results show that the model is less affected by background noise and can achieve uneven gray scale. Efficient segmentation of infrared images with blurry boundary and low contrast. (2) Multi-spectral image segmentation model based on space-spectral information. Its core is to construct a new symbolic pressure function based on spatial-spectral information. On the one hand, the magnitude of the principal components inside and outside the contour is calculated and compared as the criterion to judge the evolution direction of the contour; on the other hand, the distance measure and the spectral shape measure are synthesized by using the weight coefficient as the evolutionary force when the contour evolves in the corresponding direction. The experimental results of multispectral images collected by AOTF multispectral imaging system show that compared with the traditional active contour model which processes a single spectral segment, the model makes full use of abundant spectral information and has higher segmentation accuracy. Compared with the classical unsupervised multispectral image classification algorithm, the model can get more prominent multi-spectral image target contour without the interference of detail information.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
本文編號：2390636
[Abstract]:In the field of image processing and computer vision, image segmentation is one of the most basic and important problems. In recent years, scholars at home and abroad have been active and in-depth research on image segmentation algorithm based on active contour model, but the existing active contour segmentation algorithm is still lack of universality, and the segmentation accuracy needs to be improved. It is also necessary to further explore the segmentation algorithm for spectral image processing. Based on this, the improvement of the level set equation in the geometric active contour model is studied in this paper, and it is successfully applied to the segmentation of two dimensional single band infrared image and three dimensional multispectral image. In this paper, two novel active contour segmentation models for spectral images are proposed and studied: (1) an adaptive infrared image segmentation model based on multiple features is proposed. In order to improve the level set equation, the adaptive weight coefficient is introduced to integrate the global symbolic pressure function and the symbolic pressure function based on a variety of local characteristic information. Using Gao Si kernel function to embed a variety of statistical feature information and texture information, local terms can more comprehensively represent various regions of the image. The infrared images in different scenes are tested and compared with the traditional active contour model and edge detection algorithm. The results show that the model is less affected by background noise and can achieve uneven gray scale. Efficient segmentation of infrared images with blurry boundary and low contrast. (2) Multi-spectral image segmentation model based on space-spectral information. Its core is to construct a new symbolic pressure function based on spatial-spectral information. On the one hand, the magnitude of the principal components inside and outside the contour is calculated and compared as the criterion to judge the evolution direction of the contour; on the other hand, the distance measure and the spectral shape measure are synthesized by using the weight coefficient as the evolutionary force when the contour evolves in the corresponding direction. The experimental results of multispectral images collected by AOTF multispectral imaging system show that compared with the traditional active contour model which processes a single spectral segment, the model makes full use of abundant spectral information and has higher segmentation accuracy. Compared with the classical unsupervised multispectral image classification algorithm, the model can get more prominent multi-spectral image target contour without the interference of detail information.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41
【參考文獻】
相關(guān)期刊論文 前10條
1 邱德艷;;遙感圖像分類方法比較研究[J];考試周刊;2014年18期
2 龐冬冬;史健芳;;基于改進主動輪廓模型的圖像分割算法[J];電視技術(shù);2013年01期
3 王文淵;王芳梅;;改進的最大熵算法在圖像分割中的應(yīng)用[J];計算機仿真;2011年08期
4 朱齊丹;荊麗秋;畢榮生;吳葉斌;;最小誤差閾值分割法的改進算法[J];光電工程;2010年07期
5 聞兵工;馮伍法;劉偉;馬一薇;;基于光譜曲線整體相似性測度的匹配分類[J];測繪科學(xué)技術(shù)學(xué)報;2009年02期
6 劉洲峰;徐慶偉;李春雷;;基于小波變換的圖像分割研究[J];計算機應(yīng)用與軟件;2009年04期
7 梅雪;夏良正;李久賢;;一種基于變分水平集的紅外圖像分割算法[J];電子與信息學(xué)報;2008年07期
8 張文杰;張鈞萍;張曄;;基于區(qū)域活動輪廓模型的高光譜圖像分割方法[J];遙感技術(shù)與應(yīng)用;2008年03期
9 張博;蘇永利;;一種快速的符號距離函數(shù)的生成方法[J];計算機應(yīng)用與軟件;2008年06期
10 楊威;李俊山;史德琴;胡雙演;;基于改進變分水平集的紅外圖像分割方法[J];計算機工程;2008年04期
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