25. 最近邻插值 (Nearest-neighbor Interpolation) 21. 直方图归一化 (Histogram Normalization) 直方图会存在偏差.比如说,数据集中在0处(左侧)的图像会整体偏暗,数据集中在255处(右侧)的图像会偏亮.如果直方图有所偏向,那么其动态范围(dynamic range)就会较低.为什么使得人能够更好清除地
ranges[i]is an array of two numbers: lower and upper boundaries for the i-th histogram dimension. The whole range [lower,upper] is then split intodims[i]equal parts to determine thei-th input tuple value ranges for every histogram bin. And ifuniform=0, then thei-th element of therang...
cum_sum=np.cumsum(image_hist)norm=(cum_sum-cum_sum.min())*255# normalizationofthe pixel values n_=cum_sum.max()-cum_sum.min()uniform_norm=norm/n_ uniform_norm=uniform_norm.astype('int')# flat histogram image_eq=uniform_norm[image_flattened]# reshaping the flattened matrix to its ori...
然后计算每个cell的直方图,即每个数字出现的频率,然后对该直方图进行归一化处理(参加文章图像直方图归一化 Histogram Normalization) 最后将得到的每个cell的统计直方图进行串联连接成一个特征向量,也就是整幅图的LBP纹理特征向量 2 流程具象化举例 假设场景:一个图像分为10个大小为6*6的区域Block/Cell。 每个区域的每个...
plt.title("2D Histogram") plt.show() 三:直方图反向映射 calcHist方法参数可见:OpenCV---图像直方图 def back_projection_demo(): sample= cv.imread("./s2.png") target= cv.imread("./b.png") roi_hsv=cv.cvtColor(sample,cv.COLOR_BGR2HSV) ...
# cummulative sumcum_sum=np.cumsum(image_hist)norm=(cum_sum - cum_sum.min()) * 255# normalization of the pixel valuesn_=cum_sum.max() - cum_sum.min()uniform_norm=norm / n_uniform_norm=uniform_norm.astype('int') # flat histogramimage_eq=...
计算机视觉应用是有趣和有用的,但是底层算法是计算密集型的。 随着云计算的到来,我们可以使用更多的处理能力。 OpenCV 库使我们能够实时高效地运行计算机视觉算法。 它已经有很多年的历史了,并且已经成为该领域的标准库。 OpenCV 的主要优势之一是它高度优化,几乎可以在所有平台上使用。 这本书将涵盖我们将使用的各种...
The function cv::calcHist calculates the histogram of one or more arrays. The elements of a tuple used to increment a histogram bin are taken from the corresponding input arrays at the same location. The sample below shows how to compute a 2D Hue-Saturation histogram for a color ...
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the original code uses a bit more tricky histogram normalization (so called L2-hysteresis normalization, borrowed from D. Lowe paper on SIFT) The whole code is quite difficult to read because of using multiple abstraction layers and many advanced C++ concepts. I suggest the following: the train...