camera calibration

基本解釋

The extraction of the internal corners of a planar checkerboard pattern image is a key stage in camera calibration.
基于棋盤格模板圖像內(nèi)部角點(diǎn)的識(shí)別是攝像機(jī)定標(biāo)過(guò)程中的關(guān)鍵環(huán)節(jié)。
The image-based approach may reduce computational delay, eliminate the necessity for image interpretation and eliminate errors due to sensor modeling and camera calibration.
該方法可以減少計(jì)算延時(shí)，并且對(duì)攝像機(jī)和機(jī)械臂的校準(zhǔn)誤差和目標(biāo)模型誤差具有較強(qiáng)的魯棒性。
This paper introduces a method for camera calibration based on rhombus .
給出了一種基于菱形作為平面模板的攝像機(jī)標(biāo)定方法。

相機(jī)標(biāo)定
The general principle of camera calibration is studied.
研究了常用的相機(jī)標(biāo)定原理。
相機(jī)定標(biāo)
So this paper concentrates on the closed form solution for camera calibration.
因此,本文研究了相機(jī)定標(biāo)的閉形解。
標(biāo)定技術(shù)
鏡頭標(biāo)定
Traditional multi-camera calibration approaches mainly deal with the static cameras,and which always take advantage of the known camera locations or use calibration tools.
眾所周知,傳統(tǒng)的多目鏡頭標(biāo)定主要針對(duì)靜止鏡頭,而且一般是通過(guò)事先估計(jì)攝像機(jī)的架設(shè)位置或者利用標(biāo)定物來(lái)計(jì)算攝像機(jī)之間的關(guān)系,但此類標(biāo)定方法無(wú)法直接解決上述問題。
攝影機(jī)校準(zhǔn)

標(biāo)定技術(shù)
The intrinsic parameters of camera are found from the feature-point -set of views by means of any one of camera self-calibration techniques. And the traditional camera calibration technique is off-line, however the camera self-calibration technique is on-line.
攝像機(jī)自標(biāo)定技術(shù)通過(guò)圖像本身的特征求解攝像機(jī)內(nèi)參數(shù)，傳統(tǒng)的攝像機(jī)標(biāo)定技術(shù)是離線的，而攝像機(jī)自標(biāo)定技術(shù)是在線的，因此攝像機(jī)自標(biāo)定技術(shù)具有相當(dāng)大的理論價(jià)值和實(shí)用價(jià)值。
相機(jī)標(biāo)定
First of all, parallel binocular stereo vision measurement technology is used in this paper. VRML are used to simulate two cameras whose optical axis are parallel, it can simplify the camera calibration and image matching.
首先,本文采用平行雙目立體視覺測(cè)量技術(shù),運(yùn)用VRML模擬兩臺(tái)光軸平行的相機(jī),簡(jiǎn)化了相機(jī)標(biāo)定與圖像匹配。
攝像機(jī)標(biāo)定