ikmeans

基于分形理论的改进的一种图像分割算法,用matlab语言实现(Based on Fractal Theory An improved image segmentation algorithm)

用图切分（Graph_Cut）分割图像彩色图像、灰度图像及二值图像，效果较好，尤其是金相组织图像的分割更加有效。(Graph_Cut)

完成流程编码和huffman编码，适合做图像编码的熵编码，只要调整接口就好。(Complete the process encoding and huffman coding, image coding suitable for entropy coding, as long as the adjustment of the interface is like.)

MNIST数据集中图像数据文件， 60000个训练集(The MNIST dataset image data files, 60000 training set)

说明：  四种点云配准算法。FPFH,NDT,3DS…(Four point cloud registration algorithms.FPFH,NDT,3DS.)

实现了图像的加水印，去噪，加噪声等功能，小波包分析提取振动信号中的特征频率，是机器学习的例程。( Realize image watermarking, de-noising, plus noise and other functions, Wavelet packet analysis to extract vibration signal characteristic frequency, Machine learning routines.)

图像检测的算法，Faster R-CNN算法，先对整张图像进行卷积计算，然后通过感兴趣区域池化层(RoI Pooling Layer)将选择性搜索算法推荐出来的候选区域和卷积网络计算出的特征映射图进行融合，得到候选区域对应的特征矢量，这种共享卷积计算的操作极大地减少了卷积计算的次数。而且这些特征矢量的维度统一，方便后续的分类工作。通过感兴趣区域池化层处理卷积特征，并将得到的特征送往两个并行计算任务进行训练，分类和定位回归。通过这些方法和改进的框架，Fast R-CNN 用更短的训练和测试时长，取得了比 R-CNN 更好的效果(Faster R-CNN algorithm first convolutes the whole image, then fuses the candidate regions recommended by the selective search algorithm and the feature mapping maps calculated by the convolution network through the RoI Pooling Layer to get the corresponding feature vectors of the candidate regions, which greatly reduces the number of convolution calculations. Moreover, the dimension of these feature vectors is unified, which facilitates the subsequent classification work. The convolution feature is processed by the pooling layer of the region of interest, and the obtained feature is sent to two parallel computing tasks for training, classification and positioning regression. Through these methods and improved framework, Fast R-CNN uses shorter training and testing time and achieves better results than R-CNN.)

说明：  基于FPGA平台的双目视觉处理项目，可以通过双目摄像头实现目标物体测距，测量大小等功能(Binocular vision processing based on FPGA platform)

基于遗传算法的图像增强代码，亲测好用，值得下载(Based on the genetic algorithm image enhancement code, pro-measurement easy to use, it is worth downloading)

几种图像去燥以及图像增强的滤波器示例，中值滤波，陷波滤波器、频域低通滤波，均值滤波等(Several examples of image de dryness and image enhancement filters, median filtering, notch filter, frequency domain low-pass filtering, mean filtering, etc.)