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分割视图 希望大家多多批评指正 多多交流(segmentation View I hope Members can interact more criticism correction)

数字图象处理VC++实现,里面有很多图像处理方面的算法，有参考意义。(Digital image processing in VC++)

文章讲述了一种基于多边形的优化排样方法，结合不规则件特征确定了零件排放的最佳位置，给出了排料规则库的设计，通过排料空间的边界组织和边界更新，形成了新的排样空间。 (Article describes a polygon-based layout optimization method combined with irregular parts on characters of the part the best place to discharge given row of the design rule base material, the border by nesting space organization and boundary updates, formed The new layout space.)

说明：  使用gpu、cpu并行进行sift算子计算匹配，能够在原来的基础上加速处理，但对显卡要求较高，具体环境配置使用方法可以参照mannual(SiftGPU is an implementation of SIFT [1] for GPU. SiftGPU processes pixels parallely to build Gaussian pyramids and detect DoG Keypoints. Based on GPU list generation[3], SiftGPU then uses a GPU/CPU mixed method to efficiently build compact keypoint lists. Finally keypoints are processed parallely to get their orientations and descriptors. SiftGPU is inspired by Andrea Vedaldi s sift++[2] and Sudipta N Sinha et al s GPU-SIFT[4] . Many parameters of sift++ ( for example, number of octaves, number of DOG levels, edge threshold, etc) are also available in SiftGPU. The shader programs are dynamically generated according to the parameters that user specified. SiftGPU also includes a GPU exhaustive/guided sift matcher SiftMatchGPU. It basically multiplies the descriptor matrix on GPU and find closest feature matches on GPU. Both GLSL and CUDA implementations are provided. )

褶积模型 正演模拟 子波与反射系数合成地震响应(model zhengyanmoni zhejimoxing)

VS 环境下利用opencv 实现图像的傅里叶变换，输入图片，保存输出结果。(Under VS environment use opencv to achieve image Fourier transform, the input image and save the output.)

一种快速图像阈值分割法，不是otsu或迭代法，Wellner 1993快速自适应的图像二值化方法，有具体使用方法，很详细(A fast image thresholding segmentation method, not otsu or iterative methods, Wellner 1993 fast adaptive image binarization method)

Image processing using sobel operator

灰度数字图像增强处理,灰度数字图像增强处理,灰度数字图像增强处理,(Deal with gray-scale digital image enhancement)

CVPR2011文章Efficient_Rotation_Invariant_Object_Detection_using_Boosted_Random_Ferns(paper of CVPR2011 Efficient_Rotation_Invariant_Object_Detection_using_Boosted_Random_Ferns)