October 2014
Intermediate to advanced
288 pages
6h 27m
Japanese
Content preview from 実践 機械学習システム
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192
10
章 コンピュータビジョン：パターン認識
とえば、照明変動を正規化するなど）。
本章ではクラス分類に関する問題を扱います。画像に対してサポートベクターマシンなどの分類器
によって学習が行えるようにしたいと思います。したがって、機械学習手法を適用する前に、画像か
ら特徴量を抽出する必要があります。
本章の最後では局所特徴量（local feature）について学びます。これは比較的最近の手法で（この分
野で先駆者となったSIFT（Scale-invariant feature transform）が始めて登場したのは1999年でした）、
多くの問題で目覚ましい結果が報告されています。
10.2
画像の読み込みと表示
画像を操作するために、私たちはmahotasと呼ばれるパッケージを用いることにします。mahotas
はオープンソース（MITライセンスのため、どのようなプロジェクトでも利用できます）であり、Luis
Pedro Coelho（本書の著者の一人）によって開発が行われています。幸いにも、mahotasはNumPyに
基いて開発さ
れています。そのため、これまで学んできたNumPyに関する知識を画像処理でも用いる
ことができます。画像処理を行うパッケージは他にもscikit-image（Skimage）やSciPyのndimageモ
ジュール、OpenCVのPythonバインディングなどがあります。これらは全てNumPyを一緒に使うこ
とができるので、他のパッケージと一緒に機能を融合させて
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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.