提出了振幅位相合成圆谐滤波器用于旋转不变模式识别。
The rotation invariant amplitude phase composite circular harmonic filter (CHF) is proposed.
建立仿射不变的形状相似性度量是模式识别、计算机视觉和图象理解领域中的基本问题之一。
Establishing affine invariant similarity measure of shapes is one of the basic problems in pattern recognition, computer vision and image understanding domains.
提取用于模式识别的不变矩特征量在光学上始终是一个有待进一步研究的问题。
Extraction of invariant moments used for pattern recognition is a subject in optics to be further studied.
平移不变性在图像处理和模式识别等应用中具有十分重要的意义。
Translation invariant lies at the heart of many image processing and pattern recognition.
不变矩的几何变换不变性特点，使其在模式识别、计算机视觉和图像重构中被广泛使用。
For geometric invariance of the invariant moment, it has been used in pattern recognition, computer vision and image reconstruction.
图像不变量特征的提取与构造是模式识别和计算机视觉领域中的关键技术之一。
Extracting or construction of the invariant features is one of the key technologies in the field of pattern recognition and computer vision.
在不变性模式识别系统设计中，本文采用正交傅立叶-梅林矩提取图像特征的方法，实现了零件形状的可靠识别。
During the system design, this paper USES the way of orthogonal Fourier-Mellin moments to extract the image feature, and this conducts the reliable recognition of mechanical workpiece.
拐点特征是模式识别中经常用到的一类不变量。
Invariant corner features are often used in pattern recognition.
寻找相对于平移、尺度、旋转、扭曲不变的仿射不变量是现今多尺度分析在模式识别中应用的关键性问题。
It's a key problem to search for affine invariant with respect to translation, scaling, rotation and skewing in multi-resolution analysis.
现存的基于不变特征的二维模式识别方法在目标被模糊了的情况下都无法精确识别。
The existing approaches to invariant two dimensional pattern recognition are useless when the pattern is blurred.
针对该问题，引入模式识别中的不变矩概念，提出基于不变矩的空间小目标检测方法。
Focus on this problem, a detection method of space small targets is presented based on moment invariant which is a concept of pattern recognition.
不变矩是模式识别中的一种重要方法，它具有平移不变性、比例不变性和旋转不变性等优点。
Invariant moments are important measure in the pattern recognition. Invariant moments are independent of position, scale and orientation.
不变矩是模式识别中的一种重要方法，它具有平移不变性、比例不变性和旋转不变性等优点。
Invariant moments are important measure in the pattern recognition. Invariant moments are independent of position, scale and orientation.
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