RGB-D特征檢測(cè)與描述方法及其應(yīng)用研究
發(fā)布時(shí)間：2019-03-11 15:15
【摘要】：局部特征提取通常作為計(jì)算機(jī)視覺(jué)和圖像處理等任務(wù)的第一步,例如:寬基線(xiàn)匹配,圖像拼接以及圖像分類(lèi)等問(wèn)題,因此局部特征性能的優(yōu)劣直接影響整個(gè)系統(tǒng)最終性能的好壞。隨著RGB-D(depth)傳感器的快速發(fā)展,消費(fèi)級(jí)別的RGB-D攝像機(jī)逐漸得到普及。相較于傳統(tǒng)的RGB攝像機(jī),RGB-D攝像機(jī)可以直接獲取場(chǎng)景的深度信息,對(duì)于提高局部特征的性能有著天然的優(yōu)勢(shì),因此設(shè)計(jì)一種高性能的RGB-D局部特征有著非常大的應(yīng)用價(jià)值。局部特征提取主要涉及特征檢測(cè)、特征描述和特征匹配,孤立研究其中某一內(nèi)容對(duì)于局部特征性能的提升是有限的,因此如何合理地利用RGB-D信息構(gòu)建局部特征是一項(xiàng)非常有技巧性的課題。本文主要針對(duì)現(xiàn)有的RGB-D描述算子LOIND(Local Ordinal Intensity and Nor-mal Descriptor)[1]進(jìn)行優(yōu)化,同時(shí)提出與其耦合度更高的RGB-D 檢測(cè)算子,明顯提升了RGB-D局部特征的性能。為了客觀(guān)全面地評(píng)測(cè)RGB-D局部特征的性能,我們?cè)O(shè)計(jì)采集了標(biāo)準(zhǔn)的RGB-D局部特征評(píng)測(cè)數(shù)據(jù)集。本文主要內(nèi)容和成果如下:1.將RGB-D攝像機(jī)固定在高精度的機(jī)械臂上,完成手眼標(biāo)定后進(jìn)行數(shù)據(jù)采集。設(shè)計(jì)采集了3大類(lèi),15小類(lèi)的RGB-D數(shù)據(jù)集,包含尺度變換、旋轉(zhuǎn)變換、視角變換以及光照變換,為局部特征研究者提供了極大的便利性。2.將自相關(guān)函數(shù)的思想分別作用于灰度圖和點(diǎn)積圖,從而設(shè)計(jì)了一種融合紋理信息和深度信息的RGB-D特征檢測(cè)算子,解決了Harris檢測(cè)算子[2]在光照變化劇烈時(shí),檢測(cè)失敗的問(wèn)題。同時(shí)該檢測(cè)算子融合深度信息的方式與LOIND[1]比較一致,因此檢測(cè)算子和描述算子的耦合度較高,明顯地提高了 RGB-D局部特征的性能。3.基于點(diǎn)云重投影完成尺度估計(jì)和主方向估計(jì),增強(qiáng)了RGB-D局部特征對(duì)于尺度變換和旋轉(zhuǎn)變換的魯棒性。同時(shí)從算法細(xì)節(jié)和代碼兩方面對(duì)LOIND[1]描述算子進(jìn)行優(yōu)化,進(jìn)而提升RGB-D局部特征算子的時(shí)間效率和魯棒性。
[Abstract]:The local feature extraction is usually the first step in the task of computer vision and image processing, such as wide base line matching, image stitching and image classification. With the rapid development of the RGB-D (depth) sensor, the consumption-level RGB-D camera is becoming more and more popular. Compared with the traditional RGB camera, the RGB-D camera can directly acquire the depth information of the scene, and has a natural advantage for improving the performance of the local feature, so that a high-performance RGB-D local characteristic is designed with very large application value. The local feature extraction mainly involves the feature detection, the feature description and the feature matching, and the isolation of some of the content is limited to the enhancement of the local feature performance, so how to use the RGB-D information reasonably to build the local feature is a very technical problem. In this paper, an RGB-D detection operator with higher degree of coupling is proposed, and the performance of the local features of the RGB-D is obviously improved. In order to evaluate the performance of the RGB-D local features in an objective and comprehensive manner, we design a standard RGB-D local feature evaluation data set. The main contents and achievements of this paper are as follows:1. The RGB-D camera is fixed on a high-precision mechanical arm, and after the hand-eye calibration is finished, the data acquisition is carried out. The design and acquisition of the RGB-D data sets of three categories and 15 small classes, including scale transformation, rotation transformation, visual angle transformation and illumination transformation, provide great convenience for the local feature researchers. The concept of self-correlation function is applied to the gray scale graph and the dot product graph respectively, so that an RGB-D characteristic detection operator for fusing the texture information and the depth information is designed, and the problem that the Harris detection operator[2] has failed to detect when the light change is violent is solved. At the same time, the fusion depth information of the detection operator is consistent with the LOIND[1], so the coupling degree of the detection operator and the description operator is high, and the performance of the local characteristic of the RGB-D is obviously improved. The robustness of the local features of the RGB-D to the scale transformation and the rotation transformation is enhanced based on the scale estimation and the main direction estimation of the point cloud re-projection. At the same time, the operator is optimized from the two aspects of the algorithm detail and the code, and the time efficiency and the robustness of the RGB-D local characteristic operator are improved.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41
本文編號(hào)：2438391
[Abstract]:The local feature extraction is usually the first step in the task of computer vision and image processing, such as wide base line matching, image stitching and image classification. With the rapid development of the RGB-D (depth) sensor, the consumption-level RGB-D camera is becoming more and more popular. Compared with the traditional RGB camera, the RGB-D camera can directly acquire the depth information of the scene, and has a natural advantage for improving the performance of the local feature, so that a high-performance RGB-D local characteristic is designed with very large application value. The local feature extraction mainly involves the feature detection, the feature description and the feature matching, and the isolation of some of the content is limited to the enhancement of the local feature performance, so how to use the RGB-D information reasonably to build the local feature is a very technical problem. In this paper, an RGB-D detection operator with higher degree of coupling is proposed, and the performance of the local features of the RGB-D is obviously improved. In order to evaluate the performance of the RGB-D local features in an objective and comprehensive manner, we design a standard RGB-D local feature evaluation data set. The main contents and achievements of this paper are as follows:1. The RGB-D camera is fixed on a high-precision mechanical arm, and after the hand-eye calibration is finished, the data acquisition is carried out. The design and acquisition of the RGB-D data sets of three categories and 15 small classes, including scale transformation, rotation transformation, visual angle transformation and illumination transformation, provide great convenience for the local feature researchers. The concept of self-correlation function is applied to the gray scale graph and the dot product graph respectively, so that an RGB-D characteristic detection operator for fusing the texture information and the depth information is designed, and the problem that the Harris detection operator[2] has failed to detect when the light change is violent is solved. At the same time, the fusion depth information of the detection operator is consistent with the LOIND[1], so the coupling degree of the detection operator and the description operator is high, and the performance of the local characteristic of the RGB-D is obviously improved. The robustness of the local features of the RGB-D to the scale transformation and the rotation transformation is enhanced based on the scale estimation and the main direction estimation of the point cloud re-projection. At the same time, the operator is optimized from the two aspects of the algorithm detail and the code, and the time efficiency and the robustness of the RGB-D local characteristic operator are improved.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 ;Hand-eye calibration with a new linear decomposition algorithm[J];Journal of Zhejiang University(Science A:An International Applied Physics & Engineering Journal);2008年10期
，本文編號(hào)：2438391
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