Fundamentals_Radar

大连理工大学卢湖川教授团队显著性检测方法4(Visual Tracking via Probability Continuous Outlier Model)

读入语音信号并对其进行dct变换进行压缩变换的matlab程序(Reading the speech signal compression transform Matlab procedures and dct transform)

对于叶片面积的计算，进行图像预处理，灰度转换、直方图、去噪声等，最终计算出叶片面积的像素和。(For the calculation of leaf area, image pre-processing, gray-scale conversion, histogram, to noise, etc., and ultimately calculate the leaf area and the pixels.)

极化SAR图像处理中的目标特征矩阵分解程序(Polarization SAR image processing target characteristic matrix decomposition process)

基于霍夫变换的直线检测，效果非常好，图片自己找，可能需要对图片格式做操作。(Line Detection Based on Hough Transform)

压缩感知去噪代码，一种用于去噪的CS的有效代码，简单适用(Compressed sensing denoising code, one for denoising CS valid code, a simple application )

图像分割的阈值法，最小误差法，全局阈值，提取目标。(Image segmentation threshold method, minimum error method.)

这是一个用于血管分割的程序，程序中附带多幅测试图片(This is a program for vascular segmentation, with multiple test images attached to the program)

SIFT，即尺度不变特征变换（Scale-invariant feature transform，SIFT），是用于图像处理领域的一种描述。这种描述具有尺度不变性，可在图像中检测出关键点，是一种局部特征描述子。[1] 该方法于1999年由David Lowe[2] 首先发表于计算机视觉国际会议（International Conference on Computer Vision，ICCV），2004年再次经David Lowe整理完善后发表于International journal of computer vision（IJCV）。截止2014年8月，该论文单篇被引次数达25000余次。(SIFT, namely Scale Invariant Feature Transform (Scale-invariant feature transform, SIFT), it is a description for image processing. This description has scale invariance, can detect critical point in the image, it is a local feature descriptor. [1] The method in 1999 by David Lowe [2] First published in the International Conference on Computer Vision (International Conference on Computer Vision, ICCV), 2004 was again perfect finishing after David Lowe published in the International journal of computer vision (IJCV) . As of August 2014, the paper cited the number of times a single article of more than 25,000 times.)

运用matlab在遥感图象上运用神经网络方法进行图像分类(Using matlab to classify remote sensing images using neural network method)