A 2d object recognition algorithm based on geometry invariant and BP Network.
提出了一种基于几何不变性和BP网络的二维目标识别算法。
The algorithm can adapt to object recognition, invariant not only under rotation? Scaling? Translation, but under affine and projective transformation.
算法不仅能适应目标物体在旋转、缩放、平移变换下的不变性识别。而且能适应仿射及射影变换下的不变性识别。
In this paper a method of the three-dimensional object recognition with rotation-invariant based on moire fringe is proposed.
本文提出一种基于莫尔条纹的三维物体旋转不变识别方法。
Moreover, by extracting shape invariant moment characteristics of object region, this paper also presents a BP neural network based object recognition method.
对分割后的目标，提取不变矩特征，然后利用人工神经网络实现了运动目标的快速识别。
The recognition of object figure is by invariant moments.
目标图像的形状识别采用基于图形的不变矩理论。
SIFT has proved to be the most robust local invariant feature descriptor in object recognition and matching.
目前，SIFT已经被证明鲁棒性最好的局部不变特征描述符。
Aim a novel method for three-dimensional (3-d) object rotation-invariant recognition is proposed.
目的提出一种对三维物体进行旋转不变识别的新方法。
Aim a novel method for three-dimensional (3-d) object rotation-invariant recognition is proposed.
目的提出一种对三维物体进行旋转不变识别的新方法。
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