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都会在假设测试图像具有与训练图像相同的方向和尺度的情况下提出分类问题。因此，即使测试图像的尺度方向与训练图像的尺度方向略有不同，它们的性能也很差。该项目研究了一种基于从谱矩获得的旋转和尺度（RS）不变量的纹理分类新技术。许多研究人员已经研究了从矩导出的旋转不变量。然而，使用它们进行模式识别的成功结果仅限于具有有限大小对象的二值图像。将不变矩应用于灰度图像是很困难的，因为图像在图像边界上具有非零值。我们使用其谱密度函数（SDF）的矩来代替图像的灰度矩来获得旋转不变量，因为 SDF 具有有利的特性。当图像旋转和缩放时，其SDF旋转相同的ang1e并进行相反的缩放；此外，大多数纹理图像的光谱功率集中在原点周围，并向高频快速衰减。此外，我们还计划获得稳定的旋转力矩；我们将用于矩计算的积分区域定义为一个圆，并通过设置圆内图像总功率的恒定比率来调整其半径以适应尺度变化。由于谱矩在尺度方面具有简单的属性，因此我们可以通过旋转不变量的比率来定义RS不变量。我们在使用Brodatz专辑中具有非常高分类率的16个纹理的实验中证实了该方法的有效性。我们将该技术应用于检测（1）纹理图像的旋转角度和缩放因子，（2）衣服的缺陷和（3）乳房X光照片中的微钙化，并获得了良好的结果。