A major goal of the pattern recognition is to find methods that could provide some distortion-invariant recognition, i.e. the detection process has low sensitivity to the deformations of input objects. Typically, three deformations are considered: position, rotation, and scale changes of the input.

In this book, the emphases of the research are two invariances: rotation invariant texture classification and scale invariant pattern recognition.

本书研究的重点是旋转不变纹理分类和尺度不变模式识别。对旋转不变纹理分类的研究，我们提出了两种新方法，即基于Gabor小波的旋转不变纹理分类方法和基于圆形Gabor小波的旋转不变纹理分类方法。同时为了测定旋转纹理的角度，我们又提出了一种基于相位一致性的方向估计算法。对尺度不变模式识别的研究，我们提出了基于经验模态分解和梅林径向调和分解的方法。

本书可作为模式识别、图像处理及相近专业的研究生和专业人员的教材或参考书。

Chapter Ⅰ Introduction

 1.1 Background

 1.2 The Problem of Invariance

 1.3 Scope of the Research and Contribution of this Book

 1.4 The Mathematical Background

 1.5 Organization of This Book

 1.6 Summary

Chapter Ⅱ Review of the Previous Works

 2.1 Review of Rotation Invariant Texture Classification

 2.2 Review of the Correlation Filters about Scale Invariant Pattern Recognition

 2.3 Summary

Chapter Ⅲ The Proposed Invariant Methods

 3.1 Proposed Rotation Invariant Texture Classification Methods[170,171,173,174]

 3.2 Proposed Orientation Estimation Method[172]

 3.3 Proposed Scale Invariant Pattern Recognition Method[168,169]

 3.4 Summary

Chapter Ⅳ Experimental Results and Analysis

 4.1 Experiments for Rotation Invariant Texture Classification

 4.2 Experiments for Orientation Estimation

 4.3 Experiments for Scale Invariant Pattern Recognition

 4.4 Summary

Chapter Ⅴ Conclusion

 5.1 Rotation-invariant texture classification Using Gabor wavelet

 5.2 Rotation-Invariant Texture Classification Using Circular Gabor Wavelets

 5.3 Orientation estimat on from phase congruency

 5.4 Scale Invariant Pattern Recofnition

Reference