SICE東北支部研究集会資料(2013年)

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1 280 ( ) SURF A Study of SURF Algorithm using Edge Image and Color Information Yoshihiro Sasaki, Syunichi Konno, Yoshitaka Tsunekawa * *Iwate University : SURF (Speeded Up Robust Features) (image recognition) (edge image) (color information) (interest point detection) : Tel.&Fax.: (019) tsune@iwate-u.ac.jp 1. Lowe Scale-Invariant Feature Transform (SIFT) 1) SIFT SIFT SIFT SIFT Speeded Up Robust Features(SURF) Herbert Bay 2) SURF SIFT SIFT SURF SURF 1

2 2. SURF SURF SURF (a)log filter(lyy) (b)d filter(dyy) Fig. 1 LoG filter D filter ( y ) 1) 2) 3) 4) 2.1 DoG DoG DoG DoG (1) G(x, y, σ) L(x, y, σ) I(x, y) H(x, y, σ) [ ] Lxx (x, y, σ) I(x, y) L = xy (x, y, σ) I(x, y) L xy (x, y, σ) I(x, y) L yy (x, y, σ) I(x, y) L(x, y, σ) = G (x, y, σ) (2) G(x, y, σ) = 1 ( exp x2 + y 2 ) 2πσ 2 2σ 2 (3) (2) Fig.1(a) 2 LoG(Laplacian Of (1) Fig. 2 Gaussian) Fig.1 y LoG Fig.1(b) D (4) (4) 0.9 LoG D det(h approx ) = D xx I(x, y) D yy I(x, y) (0.9 D xy I(x, y)) 2 (4) (4) Fig.2 DoG σ D (4) DoG DoG 3 DoG 2

3 Fig. 3 Fig Fig.2 DoG 3 DoG 26 σ DoG DoG DoG n n-2 Fig.3 Fig Fig.3 DoG DoG DoG (4) Fig.2 DoG DoG DoG 1 λ 1 2 λ 2 (λ 1 > λ 2 ) λ 1 >> λ 2 λ 1 << λ 2 Fig

4 Fig. 5 Haar Fig. 7 Fig (dx, dy) dx, dx, dy, dy 4 4 4=64 σ 6 (dx,dy) Haar Haar Fig.5 (dx,dy) Fig.6 15 (5) Mn Mn = dx 2 + dy 2 (5) σ ( ) Fig.7 ( ) 4. SURF SURF SURF 4

5 SURF 4.1 (a) (b) Fig. 8 Fig.8(b) DoG D xx Fig.9 D xx Fig.1(b) D yy 90 Fig.9 0 D xx Fig.10 Fig.2 DoG 3 2 Fig.10 ( ) (4) D xx DoG Fig. 9 D xx Fig. 10 Fig.8(a) 39 Fig.8(b)

6 Fig. 11 Fig. 13 (Fig.8(a)) Fig.13 Fig. 12 RGB R G B 30 B R G 30 Fig.11 Fig.12 Fig.12 Fig SURF SURF Fig.14 Fig.15 SURF Fig.7 6

7 Table 1 性能比較 SURF 認識率 [%] 処理時間 [s] SURF +エッジ画像 提案型 SURF まとめ 本報告では SURF の高性能化を図るために Fig. 14 色による特徴点絞り込み後の特徴点 エッジ画像と色情報を用いた SURF を提案した そして 道路標識認識を例に性能評価を行った エッジ画像を使用することで 検出される特徴 点の数を増加させ 色情報を用いることで不要 な特徴点を削除する これにより処理の増加 特徴点数の増加に伴う処理時間の増加を抑えつ つ 認識率を高めることができた 今回は道路標識認識を例に処理を行ったが 道 路標識以外を認識する場合 色による判断が有 Fig. 15 提案型 SURF によるマッチング結果 効であるとは限らない しかし 検出したい物 体以外の部分に現れた特徴点を削除する手法は 有効であり 特徴点を物体上に多く残すことで 従来の SURF と提案型 SURF の性能の比較 認識率の向上が可能であると考えられる 今後 を示す 評価項目は画像の認識率と処理時間で の課題としては 物体上に多くの特徴点を残す ある 標識の認識基準は 認識したい標識との 手法の検討やハードウェア化による処理の高速 マッチングが 3 点以上とれている場合である 化が挙げられる 評価は Intel Core i GHz CPU Math Works 社 Matlab を用いて行った 用意した入 力画像は 26 枚であり 画像サイズは 参考文献 である これらの画像と標識のテンプレートと の間でマッチングを行った 処理時間は平均値 である 評価結果を Table1 に示す 提案型 SURF は従来の SURF に比べ 認識率 を大きく向上することができた さらに エッ ジ画像に加え 色情報も用いることにより 処 1) David G.Lowe Object Recognition from Local Scale-Invariant Features, Proc. of the International Conference on Computer Vision, Corfu Sept ) Herbert Bay, Tinne Tuytelaars, Luc Van Gool SURF: Speeded Up Robust Features, computer vision-eccv Lecture Notes in Computer Science, ) 今野峻一, 恒川佳隆, SURF 特徴点検出を用い た道路標識検出アルゴリズムの検討, 平成 24 年度第 3 回情報処理学会東北支部研究会 理時間の増加が抑えられた 7

2.2 6).,.,.,. Yang, 7).,,.,,. 2.3 SIFT SIFT (Scale-Invariant Feature Transform) 8).,. SIFT,,. SIFT, Mean-Shift 9)., SIFT,., SIFT,. 3.,.,,,,,.,,,., 1,

2.2 6).,.,.,. Yang, 7).,,.,,. 2.3 SIFT SIFT (Scale-Invariant Feature Transform) 8).,. SIFT,,. SIFT, Mean-Shift 9)., SIFT,., SIFT,. 3.,.,,,,,.,,,., 1, 1 1 2,,.,.,,, SIFT.,,. Pitching Motion Analysis Using Image Processing Shinya Kasahara, 1 Issei Fujishiro 1 and Yoshio Ohno 2 At present, analysis of pitching motion from baseball videos is timeconsuming

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