This paper presents a novel object tracking method based on SIFT(scale invariant feature transform) feature matching.
提出一种基于尺度不变特征变换（SIFT）特征匹配的目标跟踪方法。
And Scale Invariant Feature Transform (SIFT) was employed to exclude those sub-regions with smaller matching key points.
然后利用尺度不变特征变换(SIFT)剔除那些匹配的关键点数目少的子区域。
The image watermark scheme based on Scale Invariant feature Transform (SIFT) feature is proposed for resisting geometric attacks.
提出一种利用尺度不变特征变换(SIFT)特征点实现对抗几何攻击的水印方案。
Third, this paper presents an autonomous landing navigation algorithm taking the scale invariant feature points as the landmarks.
再次，给出了利用尺度不变特征点作为导航陆标进行探测器自主着陆导航的算法。
The approach provides effective features for identification using the order scales extracted by scale invariant feature transform.
利用尺度不变量变换，提取逆合成孔径雷达二维像的尺度信息，并按照从大到小的顺序重新排列，称为顺序尺度。
The image watermarking scheme based on scale invariant feature transform(SIFT) is proposed for resisting rotation scale translation(RST) attacks.
提出一种利用SIFT关键点实现对RST攻击校正的半盲水印方案。
An effective against geometric attack robustness of digital watermarking algorithm based on improved SIFT (Scale Invariant Feature Transform) is presented.
通过改进的SIFT(尺度不变特征变换)算法提出了一种可有效抵抗几何攻击的鲁棒数字水印算法。
Abstract: in order to improve the stability and reliability of image matching, this paper applied the scale invariant feature transform algorithm to image matching.
摘要：针对图像特征提取与匹配的适应性和准确性的问题，本文提出将尺度不变特征变换算法应用到图像特征点提取与匹配。
The variety of ear angle and occlusion are the difficulties of ear recognition. The Scale Invariant Feature Transform(SIFT) is invariant to image scaling, translation and rotation.
人耳的角度变化和遮挡是人耳识别中的难点问题，SIFT局部描述算子具有对图像尺度缩放、平移、旋转等的不变性，因此提出利用SIFT特征的人耳识别算法。
Visual invariants are those changes in scale, moving, rotating, radiation, the perspective changes remain invariant feature.
视觉不变量是指那些对于尺度变化、物体移动、旋转、放射、透视变化仍保持不变的特征。
Local characteristic scale of images plays an important role in feature extraction and local scale invariant construction.
图像的局部特征尺度在进行特征提取和构造尺度不变量时非常重要。
A video object watermark algorithm using Scale-Invariant feature Transform (SIFT) features and video segmentation based on spatiotemporal information is proposed.
提出一种结合尺度不变特征变换(SIFT)特征点校正和时空域视频对象分割的视频对象水印算法。
Because the proposed angular feature is invariant to rotation, translation and scale transformation, the registration result will converge to global minimization rather than local one.
由于本文提出的角特征不随旋转、平移和尺度的变化而变化，所以配准最终收敛到全局最小值，而不是局部最小值。
To restore the lost synchronism, a novel method to estimate the geometric operation using sifted Scale-Invariant Feature Transform (sift) was proposed.
为了恢复已失去的同步信息，提出一种基于筛选尺度不变特征估计几何攻击参数的数字水印算法。
Abstract : The Scale-invariant Feature Transform(SIFT) algorithm was improved in this paper, which could match two pictures and could also compute the object rotation angles in the pictures.
摘要 ：改进了传统的尺度不变特征变换（SIFT）算法，使其在进行图像匹配的同时，可以求取出物体的旋转角度。
Abstract : The Scale-invariant Feature Transform(SIFT) algorithm was improved in this paper, which could match two pictures and could also compute the object rotation angles in the pictures.
摘要 ：改进了传统的尺度不变特征变换（SIFT）算法，使其在进行图像匹配的同时，可以求取出物体的旋转角度。
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