November 2018
Intermediate to advanced
272 pages
6h 51m
Japanese
book
エレガントなSciPy ―Pythonによる科学技術計算
by Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 山崎 邦子, 山崎 康宏
Content preview from エレガントなSciPy ―Pythonによる科学技術計算
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140
5 章 疎行列を用いた分割表
5.9
情報変化量の使い方
最後に、
VI
を使って、できる限りの最高な画像の自動セグメンテーションの推定方法を紹介し
ま す 。「
3
章
ndimage
を使った画像領域のネットワーク」に登場した、闊歩する人懐こいトラを
覚えていますか（図
5-4
参照。もし覚えていないなら、危険察知能力を鍛えた方がよいかもしれ
ませんよ！）。
3
章で学んだワザを使って、トラの画像のセグメンテーションをいくつもの可能な
方法で生成し、どれが一番よいか評価してみましょう。
from skimage import io
url = ('http://www.eecs.berkeley.edu/Research/Projects/CS/vision/bsds'
'/BSDS300/html/images/plain/normal/color/108073.jpg')
tiger = io.imread(url)
plt.imshow(tiger);
■
画像のセグメンテーションの結果を評価するには、何らかの正解データが必要です。実は、ヒト
はトラを検知するのがすばらしく上手なので（自然淘汰最高！）、ヒトにトラを見つけてもらいさ
えすればよいのです。運のよいことに、カリフォルニア大学バークレー校の研究者がすでに何十人
ものヒトにこの画像を見せて手作業でセグメンテーションしてもらっています
†
。
まずは、
Berkeley Segmentation Dataset and Benchmark
（
http://bit.ly/2sdHN92 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.