CHAOS_PSO_TEF

基于PCNN的特征提取,PCNN用于特征提取时，具体平移、旋转、尺度、扭曲等不变性，这正是许多年来基于内容的图像检索系统追求的目标，同时PCNN用于特征提取时，有很好的抗噪性。而且PCNN直接来自于哺乳动物视觉皮层神经的研究，具有提取图像形状，纹理，边缘的属性。用PCNN能很好地对图像进行签名，将二维的图像的特征提取成一维矢量签名。(Feature extraction of specified object is an important preprocessing stage in machine vision systems. In this paper, we present a novel hybrid feature extraction method using PCNN (Pulse Coupled Neural Network) and shape information. First, we use PCNN firing map train to formulate object’s time signature, then we use roundness of each firing map to formulate object’s shape information vector, the final feature matrix we got is combined time signature and roundness. We take correlations as our judge criteria in our experiments. It has been proved that the algorithm is not sensitivity with the rotation, scaling and translation of the object and is a useful method for target recognition applications.)

非局部平均，用来去除灰度图里面的噪声，效果明显(Probability Non-Local Means)

使用matlab实现的,基础单变量以及多变量梯度下降算法,和信号分类以及信号归一化部分算法，是机器学习领域最基本的代码，希望大家多多交流(matlab implementation of basic univariate and multivariate gradient descent and signal classification algorithms, machine learning basic code, hope that more exchanges)

pdf文件转化为图片.pdf 文件转化为图片VC代码实现。(from pdf to image)

说明：  Calculate the Laplacian of an image, can be accelerated by the GPU.

使用极限学习机ELM算法进行遥感图像分类的源代码，很好的例子(Use extreme learning machine ELM remote sensing image classification algorithm source code, a good example)

对BMP文件进行图像处理，包括：边缘检测、频域变换、几何变换、图像变换校正、灰度变换、图像编码、平滑和匹配(BMP files for image processing, comprising: an edge detection, a frequency domain transformation, geometric transformation, image transformation correction gradation conversion, image coding, and matching the smooth)

主要介绍关于图像匹配的相应matlab程序设计(Focuses on the appropriate image matching matlab program )

对低质量图像分割和计数，以大米照片为例。(Of low-quality image segmentation and counting to rice as an example photo.)

有关图像的目标识别："给出一种基于特征分类辨识的合成孔径雷达图像目标检测方法#用恒虚警和扩展分形方法对3&E图像进行目 标检测后用面积和峰值能量比算子辨识目标和背景杂波!去除一部分虚警!用小波域主成分分析对每个检测窗口内的图 像提取特征向量!用支持向量机对提取得到的特征向量进行分类!辨识目标和背景杂波!完成目标检测#使用&K?3数 据对该方法进行验证和分析!实验结果表明!经过特征分类辨识后!在检测率不变的情况下!虚警数目显著降低# (Related to the image target recognition)