發(fā)布時(shí)間：2018-12-17 18:58

【摘要】：隨著新一代信息技術(shù)的發(fā)展及圖像采集設(shè)備的普及,圖像數(shù)據(jù)的數(shù)量與日俱增,其中不乏大量與空間信息有關(guān)的圖像,有待深入利用,以擴(kuò)展空間信息來(lái)源。眾所周知,圖像是對(duì)客觀世界最形象、最真實(shí)的記錄與表達(dá),其中不僅蘊(yùn)含了豐富的光譜、紋理、幾何形狀等表層信息,而且還蘊(yùn)含了物體的尺寸、深度變化等深層次的空間信息。 圖像空間信息提取,在圖像測(cè)量、目標(biāo)識(shí)別、機(jī)器人導(dǎo)航、應(yīng)急救援等領(lǐng)域具有重要的應(yīng)用價(jià)值。另外,在某些僅存在有限圖像資料的場(chǎng)合,如歷史場(chǎng)景、事故現(xiàn)場(chǎng)等,基于單幅圖像的空間信息提取成為唯一的方法。在此背景下,本文以單幅未標(biāo)定圖像為研究對(duì)象,實(shí)現(xiàn)未知距離的幾何量測(cè),恢復(fù)圖像所含場(chǎng)景的深度信息,評(píng)價(jià)幾何量測(cè)和深度計(jì)算的精度。 針對(duì)幾何量測(cè),本文研究了基于交比原理的單幅圖像幾何量測(cè)方法,并對(duì)該量測(cè)結(jié)果精度進(jìn)行評(píng)定；分析了誤差來(lái)源,建立了誤差預(yù)測(cè)與修正模型。主要研究?jī)?nèi)容和成果如下： (1)實(shí)現(xiàn)了基于交比的單幅圖像跨平面距離量測(cè)。研究了基于交比的單幅圖像二維度量信息提取方法,將共線距離量測(cè)算法模型和共面距離量測(cè)算法模型推廣至跨平面距離量測(cè)算法模型,減少了已知條件的數(shù)量。 (2)分析了單幅圖像幾何量測(cè)的誤差來(lái)源,并提出了相應(yīng)誤差修正方法。本文從拍攝相機(jī)鏡頭畸變、滅點(diǎn)計(jì)算及其誤差傳播、像素拾取偏差、已知條件數(shù)量、已知條件分布等方面分析了幾何量測(cè)誤差的來(lái)源和影響機(jī)理。研究發(fā)現(xiàn)：1)量測(cè)誤差與待測(cè)線段到已知矩形中心的距離具有強(qiáng)相關(guān)性；2)當(dāng)已知條件數(shù)量超過(guò)三個(gè)時(shí),采用三組量測(cè)結(jié)果的均值可顯著提高量測(cè)精度；3)量測(cè)誤差的大小與已知條件的分布沒(méi)有顯著聯(lián)系。 (3)建立了幾何量測(cè)誤差預(yù)測(cè)和修正模型,實(shí)現(xiàn)了幾何量測(cè)誤差定量描述。本文依據(jù)量測(cè)誤差與距離(待測(cè)線段到已知矩形中心)的強(qiáng)相關(guān)性,構(gòu)建了單幅圖像幾何量測(cè)誤差預(yù)測(cè)與修正模型,提高了量測(cè)結(jié)果的整體精度,實(shí)現(xiàn)了單像幾何量測(cè)誤差定量分析。 圖像量測(cè)僅僅恢復(fù)了圖像的度量信息,圖像深度也是圖像空間信息不可或缺的組成部分,同時(shí)圖像量測(cè)和深度計(jì)算也是互為條件和互為補(bǔ)充的。因此,本文完善了現(xiàn)有深度計(jì)算方法,實(shí)現(xiàn)了絕對(duì)深度計(jì)算,評(píng)價(jià)了深度計(jì)算結(jié)果的精度,提出了改善方法。主要研究?jī)?nèi)容和成果如下： (1)完善了深度計(jì)算模型,實(shí)現(xiàn)了圖像連續(xù)深度半自動(dòng)化估計(jì)。本文完善了基于針孔成像模型的地面點(diǎn)深度計(jì)算模型,根據(jù)圖像中地物分割結(jié)果和空間位置關(guān)系,分區(qū)域?qū)嵤┝松疃扔?jì)算及估計(jì),實(shí)現(xiàn)了單幅圖像連續(xù)深度半自動(dòng)化估計(jì)。 (2)提出了基于深度差的相機(jī)高度恢復(fù)方法,改善了深度計(jì)算結(jié)果。本文分析了深度計(jì)算模型的誤差影響因素,發(fā)現(xiàn)相機(jī)高度誤差是深度計(jì)算精度的主要影響因素之一,基于該結(jié)論本文提出“基于地面點(diǎn)已知深度差的相機(jī)高度恢復(fù)方法”,可顯著提高深度估計(jì)結(jié)果的可靠性。
[Abstract]:With the development of new-generation information technology and the popularization of image-collecting equipment, the number of image data is increasing, and there are lots of image related to spatial information, which is to be used in-depth to extend the spatial information source. It is well known that the image is the most image and the most true record and expression of the objective world, which not only contains abundant spectrum, texture, geometric shape and other surface information, but also contains the deep space information such as the size and the depth of the object. The image space information extraction has important application price in the fields of image measurement, target recognition, robot navigation, emergency rescue, etc. a value. In addition, in some situations where only limited image data is present, such as a history scene, an accident scene, etc., spatial information extraction based on a single image becomes the only party. In this background, in this paper, a single uncalibrated image is used as the study object, and the geometric measurement of the unknown distance is realized, the depth information of the scene contained in the image is recovered, and the precision of the geometric measurement and the depth calculation is evaluated. In this paper, the method of measuring the geometric quantity of a single-amplitude image based on the cross-specific principle is studied, and the accuracy of the measurement results is evaluated. The error source is analyzed, and the error prediction and repair are established. Positive models. Primary study content and The results are as follows: (1) The single image cross-level based on the intersection ratio is realized. The method of the two-dimensional measurement of single-amplitude image based on the intersection ratio is studied. The model of the linear distance measurement and the model of the total-surface distance measurement are extended to the model of the cross-plane distance measurement and the known method is reduced. (2) The error source of the single-image geometric measurement is analyzed, and the phase is presented. In this paper, the errors of geometric measurement are analyzed from the aspects of camera lens distortion, point-of-point calculation and error propagation, pixel pick-up deviation, known condition number, known condition distribution, etc. the results show that 1) the measured error has a strong correlation with the distance between the line segment to be measured and the center of the known rectangular center; 2) when the number of known conditions exceeds three, the mean value of the three groups of measurement results can be displayed to improve the precision of measuring the measuring precision; 3) the magnitude of the error of measurement and the point of the known condition (3) The model of geometric measurement error prediction and correction is established, and several methods are implemented. In this paper, based on the strong correlation between the measuring error and the distance (the line to be measured to the center of the known rectangle), the error prediction and correction model of single-image geometric measurement is constructed, and the overall precision of the measurement result is improved, and the single image is realized. The quantitative analysis of the measurement error of the image is that the measurement of the image is only the measure information of the image, and the depth of the image is an integral part of the image spatial information, and the image measurement and the depth calculation also Therefore, the present depth calculation method is improved, the absolute depth calculation is realized, and the depth calculation results are evaluated. The accuracy of the method is improved. The main research contents and results are as follows: (1) The depth calculation model is improved and the implementation is realized. The image continuous depth semi-automatic estimation is presented. The ground point depth calculation model based on the pinhole imaging model is improved, and the depth calculation and estimation are carried out on the basis of the object segmentation result and the spatial position relation in the image. image continuous deep semi-automatic estimation. (2) a high-depth-based camera is proposed In this paper, the error influence factor of the depth calculation model is analyzed, and it is found that the height error of the camera is one of the main influencing factors of the depth calculation precision. Based on this conclusion, the 鈥淐amera height recovery method based on ground point known depth difference鈥，

本文編號(hào)：2384660