CAJ | 학술논문

提出了一种新的木材表面缺陷的描述和检测方法，首先将木材表面图像划分成互不重叠的矩形块，即将木材图像矩阵进行分块；然后对每一块图像进行多方向多尺度Gabor变换，统计各个矩形块图像在不同尺度和方向上Gabor系数的均值和方差，将这些均值和方差组成一个描述矩形块的特征向量；为实现木材表面缺陷类别的检测，最后将块特征向量归一化后输入LS-SVM分类器，利用特征向量的相似度来进行缺陷的定位和识别。结果表明，该方法避免了传统检测方法需要进行图像分割的复杂性和局限性，它通过一个或多个矩形块的组合来定位缺陷，检测准确率超过91％。
제출료일충신적목재표면결함적묘술화검측방법，수선장목재표면도상화분성호불중첩적구형괴，즉장목재도상구진진행분괴；연후대매일괴도상진행다방향다척도Gabor변환，통계각개구형괴도상재불동척도화방향상Gabor계수적균치화방차，장저사균치화방차조성일개묘술구형괴적특정향량；위실현목재표면결함유별적검측，최후장괴특정향량귀일화후수입LS-SVM분류기，이용특정향량적상사도래진행결함적정위화식별。결과표명，해방법피면료전통검측방법수요진행도상분할적복잡성화국한성，타통과일개혹다개구형괴적조합래정위결함，검측준학솔초과91％。
We proposed a new method for wood surface defects description and detection based on Gabor features.Firstly, the wood surface image is divided into non-overlapping rectangular blocks.Then, every block of the image is decomposed by convolving with multi-scale and multi-orientation Gabor filters.Through statistical techniques , including mean and variance of Gabor coefficients inside each block, the block feature vector can be obtained to describe every block.Finally, the ex-tracted feature vectors are normalized and inputted into the LS-SVM classifier to locate and detect the defects .Our method can avoid the complexity and limitations of image segmentation and the detection accuracy is more than 91%.