Object shape recognition is a challenging problem in the field of pattern recognition and computer vision.
物体形状的识别是模式识别与计算机视觉领域中的具有挑战性的问题。
Shape recognition of object is important research direction in pattern recognition, and is widely used in image analysis, machine vision and target discrimination.
物体的形状识别是模式识别的重要方向，广泛应用于图像分析、机器视觉和目标识别等领域。
Shape is the inherence characteristic of an object in the image, and it is the important character used for the object recognition.
形状是图像中目标的基本内在特性，是用于目标识别的重要特征，因此基于形状的目标识别方法研究具有重要意义。
Shape recognition of object is one of the important directions in pattern recognition, and is widely used in image analysis, machine vision and target discrimination, etc.
物体的形状识别是模式识别的重要方向之一，广泛应用于图像分析、机器视觉和目标识别等领域。
Contours and boundaries that define object shape and indicate outer limits for regions. They are critical for human or computer recognition of objects.
轮廓与边界定义了目标的外表形状，确定了区域之间的分界线，它们是人类与计算机进行目标识别的重要特征。
An important feature of shape description is the contour curves of object. To improve the efficiency of recognition, dominant points are usually used to represent the contour.
描述形状的重要特征是物体的轮廓曲线，为了提高识别效率，通常使用控制点表达轮廓。
The matching computation for 3D shape of superquadric models is one key problem in 3D object recognition with superquadrics.
超二次模型形状匹配计算是应用超二次模型进行三维物体识别的一个关键问题。
Fast searching centroids of 2d arbitrary shape is still a key problem in the fields of pattern recognition and object tracking.
快速搜索任意形状二维目标的质心，一直是模式识别、目标跟踪等领域中的关键问题。
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。
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。
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