Classification of Rotated and Scaled Textured Images

旋转和缩放纹理图像的分类

• 批准号: 08680398
• 负责人: YOSHIDA Yasuo
• 金额: $ 1.54万
• 依托单位: Kyoto Institute of Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1996
• 资助国家: 日本
• 起止时间: 1996 至 1997
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08680398/
• 关键词: spectral moments; rotation invariants; scale invariants; texture classification; defect detection; rotation angle; scaling factor; 拡大不変量; 布地欠陥検出; テクスチャ面傾斜角; 微小石灰質検出; 回転・拡大画像識別

Most of texture classification approaches pose classification problems under the assumption that a test image possesses the same orientation and scale as training images. According1y, they perform poorly even though the orientation of the scale of the test image differs a little from those of the training ones.This project studies a new technique for texture classification based on rotation and scale (RS) invariants obtained from spectral moments. Rotation invariants derived from moments have been studied by many researchers. However, successful results for pattern recognition using them are limited only to binary images with a finite-sized object. Application of the invariant moments to gray-scale images is difficult because the images have non-zero values on the image boundaries.Instead of the gray-scale moments of the image, we use the moments of its spectral density function (SDF) to obtain rotation invariants because the SDF has advantageous properties. When an image is rotated and scaled, its SDF rotates by the same ang1e and is inversely scaled ; moreover, the spectral powers of most textured images are concentrated around the origin and decay rapidly toward high frequencies. In addition, we scheme to obtain stable rotation moments ; we define as a circle the integral region for moment calculation and adjust its radius to sca1e change by setting a constant rate of the image total power in the circle. Since spectral moments have a simple property with respect to scale, we can define RS invariants by a ratio of rotation invariants.We confirmed the validity of the proposed method in experiments using 16 textures from Brodatz album with very high classification rates. We applied this technique to detection of (1) the rotation ang1e and the scaling factor of textured images, (2) the defect of c1othes and (3) the micro calcifications in mammograms and obtained prosperous results.

大多数纹理分类方法在测试图像具有与训练图像相同的方向和规模的假设下构成分类问题。根据11y，即使测试图像的规模的方向与训练图的规模的方向有很大不同。该项目研究了一种基于旋转和规模（RS）不变性的新技术，该技术是从光谱矩获得的。许多研究人员都研究了瞬间的旋转不变性。但是，使用它们的模式识别的成功结果仅限于具有有限大小对象的二进制图像。由于图像在图像边界上具有非零值，因此将不变力矩应用于灰度图像非常困难。该图像的灰度矩矩形，我们使用其光谱密度函数（SDF）的矩获得旋转不变性，因为SDF具有优势性。当图像旋转并缩放时，其SDF会旋转相同的Ang1e，并呈负缩放；此外，大多数纹理图像的光谱力集中在原点周围，并迅速衰减高频。此外，我们计划获得稳定的旋转力矩；我们将整体区域定义为矩计算的整体区域，并通过设置圆中图像总功率的恒定速率来调整其半径为SCA1E变化。由于光谱矩在比例方面具有简单的属性，因此我们可以按旋转不变的比例定义RS不变性。我们使用Brodatz专辑中的16种纹理证实了该方法在实验中的有效性，分类速率很高。我们将此技术应用于（1）旋转ANG1E和纹理图像的缩放因子，（2）C1othes的缺陷以及（3）乳房X线照片中的微钙化并获得了繁荣的结果。