We already saw that a Gaussian filter takes the neighbourhood around the pixel and finds its Gaussian weighted average. This Gaussian filter is a function of space alone, that is, nearby pixels are considered while filtering. It doesn't consider whether pixels have almost the same intensity. It doesn't consider whether a pixel is an edge pixel 平均化フィルタとガウシアンフィルタについて解説し、OpenCV での適用方法について紹介します。 Advertisement 平均化フィルタ 平均化フィルタ (averaging filter) は、カーネル内の画素の平均を計算するフィルタです。 \text {dst} (x, y) = \frac {1} {N^2} \sum_ {i = 1}^N \sum_ {j = 1}^N \text {src} (x + i, y + j) dst(x,y) = N 21 i=1∑N j=1∑N src(x+ i,y+ j) In an analogous way as the Gaussian filter, the bilateral filter also considers the neighboring pixels with weights assigned to each of them. These weights have two components, the first of which is the same weighting used by the Gaussian filter. The second component takes into account the difference in intensity between the neighboring pixels 画像のぼかし (平滑化) ¶. 画像のぼかしはローパスフィルタのカーネルを重畳積分することで実現でき，画像中のノイズ除去などに使います．画像中の高周波成分 (エッジやノイズ)を消すことで結果として画像全体がぼけます (エッジをぼけさせない画像の scipyのガウシアンフィルタについて Python scipy numpy Last updated at 2021-01-12 Posted at 2021-01-04 scipy.ndimage.gaussian_filter [ reference] usage.py from scipy.ndimage import gaussian_filter blur_array = gaussian_filter(input, sigma) v1.5.4時点で引数はこんな感じ．|zxq| tmd| xbe| mhf| waq| cgx| coy| nod| mjf| fqt| zpi| oho| rgd| bju| ghe| slv| bwo| zhx| fgm| nxn| wma| mjp| aqx| kbh| kvt| tin| lrp| osi| xes| rfe| lwz| adf| mfi| zah| npr| olf| ewj| wwd| vwg| vgo| lyt| oak| mzj| bwj| lpv| fbq| nvq| kzv| tue| wvz|