知识与财富的链接
在图像模板匹配问题中,基于像素灰度值的相关算法尽管已经十分普遍,并得到广泛的应用,但目前此类算法都还存在有时间复杂度高、对图像亮度与尺寸变化敏感等缺点.为了克服这些缺点,提出一种新的基于图像灰度值的编码表示方法.这种方法将图像分割为一定大小的方块(称为R-块),计算每个R-块图像的总灰度值,并根据它与相邻R-块灰度值的排序关系进行编码.然后通过各个R-块编码值的比较,实现图像与模板的匹配新算法中各个R-块编码的计算十分简单;匹配过程只要对编码值进行相等比较,而且可以采用快速的比较算法新算法对像素灰度的变化与噪声具有鲁棒性,其时间复杂度是O(M2log(N)).实验结果表明,新算法比现有的灰度相关算法的计算时间快了两个数量级.
Correlation algorithms based on pixel gray value are very popular and widely used in image template matching. However, these algorithms have high time complexity and are sensitive to the variation of image luminance and scale. To avoid that, a new algorithm based on coding image grey value is proposed. This algorithm divides the image into certain size blocks called R-block, sums the gray value of each R-block pixel, and codes the R-block according to the sorting result among the neighborhood of R-blocks. Image and template are matched by comparing their R-block coding. The R-block is very rapidly and easily coded and only equality comparison is needed. The new algorithm is robust to the linear transformation of pixel grey value and image noise. Its time complexity is reduced to O(M2log(N)), or namely is improved two order of magnitude in contrast to the current correlation algorithms’.
