An Invariant Approach for Image Recognition

E. Zuniga-Segura, G. Sánchez-Díaz
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Abstract

Shape-of-object representation has always been an important topic in image processing and pattern recognition. This work deals with representation of shape of objects, and approaches to recognize objects. Several invariant techniques are widely used to represent an object because they preserve information and allow considerable data reduction. In this paper, a new approach based on a code representation and testor theory is presented. The proposed method is invariant under translation, scaling and rotation. Also, the paper discusses the capabilities of the method in recognizing objects. In addition, results using simple figures classes are show
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一种图像识别的不变性方法
物体形状表示一直是图像处理和模式识别中的一个重要课题。这项工作涉及物体形状的表示,以及识别物体的方法。几种不变量技术被广泛用于表示对象,因为它们保留信息并允许大量的数据缩减。本文提出了一种基于代码表示和测试人员理论的新方法。该方法在平移、缩放和旋转条件下是不变性的。此外,本文还讨论了该方法在物体识别方面的能力。此外,还显示了使用简单图形类的结果
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