GDAL+QT，直方图的统计，使用QChart显示直方图的折线分布

A new approach to quantify the lung nodule speculation levels in CT (computed tomography) images was proposed. Firstly, two-dimensional image of nodule was generated by using the spiral scan technology. Secondly, the lung nodule was segmented in the two-dimensional image using the dynamic programming. Thirdly, based on the expanded regions from the segmented boundary, the speculation was segmented by use of threshold segmentation. Fourthly, the feature region where speculation may exist was extr

Algorithm Description Recognizing objects from large image databases, histogram based methods have proved simplicity and usefulness in last decade. Initially, this idea was based on color histograms that were launched by swain [1]. This algorithm presents the first part of our proposed technique named as “Histogram processed Face Recognition” [2] For training, grayscale images with 256 gray levels are used. Firstly, frequency of every gray-level is computed and stored in vectors for further processing. Secondly, mean of consecutive nine frequencies from the stored vectors is calculated and are stored in another vectors for later use in testing phase. This mean vector is used for calculating the absolute differences among the mean of trained images and the test image. Finally the minimum difference found identifies the matched class with test image. Recognition accuracy is of 99.75% (only one mis-match i.e. recognition fails on image number 4 of subject 17) [1] M. J. Swain and D. H. Ballard, “Indexing via color histogram”, In Proceedings of third international conference on Computer Vision (ICCV), pages 390–393, Osaka, Japan, 1990. [2] Fazl-e-Basit, Younus Javed and Usman Qayyum, "Face Recognition using processed histogram and phase only correlation ", 3rd IEEE International Conference on Emerging Technology pp. 238-242

根据参考图像各个通道的灰度分布，将一副图像的灰度分布映射过去，使映射后的两幅图像灰度分布非常接近，被称为histogram matching或者histogram specification，常用于网络训练的图像数据扩增