計(jì)算機(jī)理論論文基于特征點(diǎn)的圖像匹配技術(shù)研究

1、基于特征點(diǎn)的圖像匹配技術(shù)研究 基于特征點(diǎn)的圖像匹配技術(shù)研究 the research on image matching based on feature point【中文摘要】 圖像配準(zhǔn)是對(duì)取自不同時(shí)間、不同傳感器或者不同視角的同一場(chǎng)景的兩幅圖像或者多幅圖像進(jìn)行匹配的過(guò)程,隨著計(jì)算機(jī)視覺(jué)技術(shù)的發(fā)展,圖像匹配技術(shù)被廣泛地應(yīng)用在遙感圖像、醫(yī)學(xué)圖像、三維重構(gòu)、機(jī)器人視覺(jué)等諸多領(lǐng)域中。圖像配準(zhǔn)的方法大致分為兩類(lèi):基于像素的圖像配準(zhǔn)方法和基于特征的圖像配準(zhǔn)方法。而基于特征的圖像配準(zhǔn)方法是目前圖像配準(zhǔn)最常用的方法,其最大的優(yōu)點(diǎn)在于能夠?qū)?duì)整個(gè)圖像進(jìn)行的各種分析轉(zhuǎn)化為對(duì)圖像特征(特征點(diǎn)、特征曲線等)的分析

2、,從而大大減小了圖像處理過(guò)程的運(yùn)算量。經(jīng)過(guò)多年的研究,基于特征的圖像配準(zhǔn)技術(shù)已經(jīng)取得了一定的研究成果,其主要步驟包括:圖像采集、特征提取、特征匹配、圖像變換。本文內(nèi)容以圖像配準(zhǔn)的四個(gè)步驟為主線,首先概括介紹圖像配準(zhǔn)技術(shù)的研究現(xiàn)狀、特點(diǎn)及應(yīng)用領(lǐng)域,從而展示了圖像配準(zhǔn)技術(shù)廣闊的應(yīng)用前景。接著根據(jù)不同的硬件采集設(shè)備介紹了圖像采集的兩種方式以及圖像配準(zhǔn)常用的數(shù)學(xué)模型。論文分析和總結(jié)了現(xiàn)有圖像匹配的主要方法、性能以及存在的問(wèn)題,主要研究了兩個(gè)方面的內(nèi)容:(1)本文通過(guò)研究邊緣特征和點(diǎn)特征,著重介紹了sift(scale invariant feature transform)點(diǎn)的形成過(guò)程以及涉及的邊緣提

3、取技術(shù),為了改善信噪比低的圖像的邊緣檢測(cè)效果,提出了一種基于多尺度多結(jié)構(gòu)元素的數(shù)學(xué)形態(tài)學(xué)與canny算子相結(jié)合的邊緣檢測(cè)算法,得到的邊緣比較清晰,圖像的細(xì)節(jié)表現(xiàn)力和抗噪能力得到加強(qiáng),為后續(xù)的特征提取、目標(biāo)識(shí)別打下了良好的基礎(chǔ);(2)在經(jīng)典的sift特征點(diǎn)的基礎(chǔ)上,針對(duì)不同的視角拍攝、不同的光照和明暗變換、尤其包含考驗(yàn)匹配算法的相似結(jié)構(gòu)多的情形的圖片,提出了一種基于網(wǎng)絡(luò)流的特征優(yōu)化匹配算法-最小費(fèi)用k流算法(mkp)。該算法綜合利用了sift特征點(diǎn)的方向信息和梯度信息,采用本文改進(jìn)的匹配度量函數(shù)衡量特征點(diǎn)之間的相似性來(lái)作為流網(wǎng)絡(luò)上的費(fèi)用,借助于最小費(fèi)用最大流的算法求出特征點(diǎn)之間的最優(yōu)匹配點(diǎn)對(duì),根

4、據(jù)弧上的匹配度量比值和雙向檢查約束的方法來(lái)剔除偽匹配對(duì),得到兩幅圖像的優(yōu)化匹配點(diǎn)對(duì),匹配準(zhǔn)確率比一些經(jīng)典算法提高了14%,使得變換系數(shù)更加精確,圖像配準(zhǔn)效果增強(qiáng)。實(shí)驗(yàn)證明,mkp算法準(zhǔn)確率高,魯棒性強(qiáng),具有較高的使用價(jià)值?！居⑽恼?image registration, is a process to match two or more images of the same scene taken at different times, from different viewpoint, or by different sensors, with the development of th

5、e computer vision, which is widely required in remote sensing, in medicine, in 3d reconstruction,and in computer vision (target location, automatic quality control), and so on. the methods of image registration can be classified into two categories: the intensity-based matching approach and the feat

6、ure-based matching approach. however, as one of the most common methods, the largest advantage of the feature-based approach is its ability of translating the analysis of the whole image into its features that contains the feature point, the feature curve, etc. and as a result, it speeds up the imag

7、e process. after many years of research, the feature-based image registration technology has made some achievements, the majority of the registration methods consist of four steps: image acquisition, feature detection, feature matching and image resampling and transformation.this paper is arranged a

8、ccording to the above four steps. firstly, recent research, characteristics and application areas of image registration are discribed, thereby demonstrating its wide application prospect. then image acquisition in two ways by adopting different hardware equipment is discussed, as well as common imag

9、e transform model. finally, there are two aspects which are we focused on: (1) the process of creating sift (scale invariant feature transform) descriptors and edge detection technology involved are emphatically given by studying edge and point features, then, in order to improve edge detection resu

10、lt on the low snr (signal to noise ratio) image, a mathematical morphology of multi-structure and multi-scale element and canny algorithm is introduced. so the performance of the details and anti-noise ability are strengthened, the convenience for the following steps such as feature extract and obje

11、ct recognition is provided; (2) based on the sift feature extract, an algorithm using network flow to obtain optimal feature matching-min cost k flow problem is put forward, which utilizes the direction and gradient information of sift and the improved matching cost function for measuring the simila

12、rity on the network flow, with minimum cost maximum flow algorithm to derive the global optimal matching, under the arc ratio of the matching measure and the bi-directional check constraints to remove pseudo matching. eventually, optimal matching will be achieved. as a result, about 14 percent impro

13、vement can be obtained. experments on the test image sets demonstrate high accuracy, robustness, and more application. 【中文關(guān)鍵詞】 特征點(diǎn)檢測(cè); 圖像配準(zhǔn); sift; mkp算法; 邊緣檢測(cè) 【英文關(guān)鍵詞】 feature detect; image registration; sift; mkp algorithm; edge detection 【論文目錄】摘要 4-5 abstract 5-6 1 引言 9-12 1.1 論文的研究意義 9 1.2 國(guó)內(nèi)外研究現(xiàn)狀

14、9-10 1.3 論文的主要工作 10-11 1.4 論文的組織結(jié)構(gòu) 11-12 2 圖像配準(zhǔn)的基礎(chǔ)知識(shí) 12-16 2.1 圖像配準(zhǔn)的定義 12 2.2 圖像獲取 12-14 2.2.1 照相機(jī)拍攝 13 2.2.2 采集卡獲取圖像 13-14 2.3 圖像配準(zhǔn)的數(shù)學(xué)模型 14-15 2.4 本章小結(jié) 15-16 3 圖像特征提取 16-31 3.1 邊緣特征檢測(cè)算法 16-22 3.1.1 roberts 邊緣檢測(cè)算子 16-17 3.1.2 sobel 邊緣檢測(cè)算子 17 3.1.3 prewitt 邊緣檢測(cè)算子 17-18 3.1.4 canny 邊緣檢測(cè)算子 18-20 3.1.5 基于多尺度多結(jié)構(gòu)元素的數(shù)學(xué)形態(tài)學(xué)與canny 算子相結(jié)合的邊緣檢測(cè)算法 20-21 3.1.6 改進(jìn)的邊緣檢測(cè)算法驗(yàn)證 21-22 3.2 點(diǎn)特征檢測(cè)算法 22-29 3.2.1 sift 特征點(diǎn) 23-29 3.3 本章小結(jié) 29-31 4 基于sift 特征點(diǎn)的匹配算法 31-39 4.1 特征匹配算法 31-36