cvpr16_deblur_study-master

简单图像颜色特征提取，用于自己使用，基本常规的那种(Simple Image Color Feature Extraction)

说明：  用C++实现的对数变换达到图像增强效果，代码亲测可用(Logarithmic transformation realized by C++ achieves image enhancement effect, and code pro-test is available.)

路标识别的应用实例，可以快捷的检测并识别路标的种类！(Road sign recognition application example, you can quickly test and identify the types of signs!)

数字图像处理，边缘提取，分割，湖面面积计算matlab程序(Image processing, edge detection, segmentation, the lake area)

说明：  基于卡尔曼滤波的camshift算法，实现目标跟踪(CAMSHIFT algorithm based on Kalman filter for target tracking)

下面给出了48项泽尼克多项式，外加一项常数项。需要注意的是，读者并不需要严格按照下文所示的顺序排列这些泽尼克项，实际上在不同的应用和机构会采用不同的排列顺序。 表中的#0项是个常数或者说是平移项（piston term），这一项的系数也代表了平均光程差；而#1和#2项分别是x和y方向的倾斜项（tilt terms），#3代表了聚焦，因此，#1到#3项代表了波前的高斯或者近轴特性；#4和#5项代表了像散和离焦，#6和#7项代表彗差和倾斜，而#8项代表了3级像差和离焦，也就是说#4到#8项为3级相差项；同样地，#9到#15项代表了5级像差，而#16到#24项代表了7级像差，#25到#35项代表了9级像差，#36到#48项代表了11级像差。(Here are 48 Zernike polynomials, plus a constant term. It is important to note that the reader does not need to strictly arrange the Zernike entries in the order shown below, but in fact different orders of arrangement are applied to different applications and institutions. Table #0 is a constant or a shift (piston term), a coefficient of this term also represents the average optical path difference; while #1 and #2 are inclined X and Y direction (tilt terms), #3 represents the focus, therefore, #1 to #3 on behalf of the wavefront. Gauss or paraxial properties; #4 and #5 represent the astigmatism and defocus, #6 and #7 represent coma and tilt, and #8 represents the 3 order aberration and defocus, that is to say #4 to #8 is 3 level difference; similarly, #9 to #15 on behalf of the 5 order aberration #16 and #24, to represent the 7 level #25 to #35 aberration, on behalf of the 9 #36 to #48 aberration, on behalf of the 11 order aberration.)

通过低通滤波器，实现对图像的平滑处理，使得图像的对比度变低，具有膨胀的效果。(Low-pass filter, to achieve smoothing of the image, so that the image contrast is low, having an expansion effect.)

说明：  利用BP神经网络进行图像分割。主要适用于RGB信息丰富的图像。以RGB为BP网络的三个输入，与对应的灰度图对网络进行训练。(The use of BP neural network image segmentation. RGB is mainly applied to information-rich images. BP network to RGB for the three inputs, with grayscale corresponds to the network training.)

将RGB格式图像转换到YUV空间，输入RGB图像返回YUV格式数据(Converting RGB format images to YUV space)

批量图片合并源码