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。
This paper presents a novel object tracking method based on SIFT(scale invariant feature transform) feature matching.
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。
Third, this paper presents an autonomous landing navigation algorithm taking the scale invariant feature points as the landmarks.
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。
And Scale Invariant Feature Transform (SIFT) was employed to exclude those sub-regions with smaller matching key points.
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。
The image watermark scheme based on Scale Invariant feature Transform (SIFT) feature is proposed for resisting geometric attacks.
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。
To restore the lost synchronism, a novel method to estimate the geometric operation using sifted Scale-Invariant Feature Transform (sift) was proposed.
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。
An effective against geometric attack robustness of digital watermarking algorithm based on improved SIFT (Scale Invariant Feature Transform) is presented.
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。
A video object watermark algorithm using Scale-Invariant feature Transform (SIFT) features and video segmentation based on spatiotemporal information is proposed.
为增强跟踪算法区分目标的能力，算法将尺度不变特征和基于核的颜色分布特征统一用作目标的跟踪特征。
For boosting up the feature's discriminating ability, both scale invariant features and kernel based color distribution features are used as descriptors of tracked object.
针对遥感图像配准，基于尺度不变特征变换(SIFT)提出了一种在核空间中构建仿射不变描述子的方法。
A technique to construct an affine invariant descriptor for remote-sensing image registration based on the scale invariant features transform (SIFT) in a kernel space is proposed.
首先根据尺度不变特征变换方法从图像中提取关键点作为特征点，然后进行左右双目图像的特征点匹配和视差的恢复。
Then use these feature points for binocular matching thus getting distance information of the scene by the calculation of differences between match points in two images.
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：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。
Abstract: in order to improve the stability and reliability of image matching, this paper applied the scale invariant feature transform algorithm to image matching.
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。
Visual invariants are those changes in scale, moving, rotating, radiation, the perspective changes remain invariant feature.
且获得的图像特征描述符具有旋转、平移、尺度不变性等优点，能够很好地描述图像的形状和空间分布信息。
And the image feature descriptor has a rotation, translation, scaling invariance, etc. it can be a very good to describe the shape and spatial distribution of information.
岩土体材料大都表现出分形分布，具有自相似性特征或尺度不变性。
The geotechnical materials mostly show fractal distribution, and they have self-similar or scale invariability.
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。
Local characteristic scale of images plays an important role in feature extraction and local scale invariant construction.
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。
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.
它最初作为一种关键点的特征提出来的，其主要思想是在尺度空间寻找极值点，提取位置，尺度，旋转不变量；
The main idea is to find extreme value in the scale space, extracting location, scale and rotation invariant.
它最初作为一种关键点的特征提出来的，其主要思想是在尺度空间寻找极值点，提取位置，尺度，旋转不变量；
The main idea is to find extreme value in the scale space, extracting location, scale and rotation invariant.
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