CAJ | 학술논문

　　在机器人视觉系统中运用SIFT描述子对现实世界中的目标进行识别，这一研究已经取得了很大的进步。运用SIFT生成的图像特征向量的性能十分稳定，对旋转、缩放、平移是保持不变性的，对一定程度目标遮挡、光照变化、视点变化、杂物场景和噪声等也能保持很好的不变性。RANSAC算法早就已经是计算机视觉领域常用的一个进行矫正的标准方法，在标准的RANSAC算法基础上加入了假设评价，改进为R-RANSAC（The Randomized RANSAC）算法。对这两个方面进行论述，运用SIFT（尺度不变特征变换）算法对双目机器人的两幅视觉图像进行匹配，采用带SPRT的R-RANSAC改进算法对匹配过程进行优化，尽可能在短的时间里完成匹配矫正，进而加速整个配准的时间。
　　재궤기인시각계통중운용SIFT묘술자대현실세계중적목표진행식별，저일연구이경취득료흔대적진보。운용SIFT생성적도상특정향량적성능십분은정，대선전、축방、평이시보지불변성적，대일정정도목표차당、광조변화、시점변화、잡물장경화조성등야능보지흔호적불변성。RANSAC산법조취이경시계산궤시각영역상용적일개진행교정적표준방법，재표준적RANSAC산법기출상가입료가설평개，개진위R-RANSAC（The Randomized RANSAC）산법。대저량개방면진행논술，운용SIFT（척도불변특정변환）산법대쌍목궤기인적량폭시각도상진행필배，채용대SPRT적R-RANSAC개진산법대필배과정진행우화，진가능재단적시간리완성필배교정，진이가속정개배준적시간。
There have been great advances in object recognition and image registration, through the match of invariant local image feature. With the feature of invariance to affine, 3D projection, scaling and rotation, illumination changes, image translation, the Scale Invariant Features Transform(SIFT)is commonly used in object recognition. The RANdom Sample Consensus(RANSAC) is widely used as robust estimator, as a standard in the field of computer vision. In the process of R-RANSAC, the Randomized (hypothesis evaluation)RANSAC, a modification is made to RANSAC by checking data points sequentially, while the standard RANSAC check all the data points in the model verification step. Own to this improvement, hypotheses with low support can be rejected before all points are considered. In addition, the Sequential Probability Ratios Test(SPRT) is used to minimize R-RANSAC runtime.