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CV 輪講 Putting Objects in Perspective

CV 輪講 Putting Objects in Perspective. 藤吉研究室　土屋成光 2008 年 7 月 1 日. Back ground. 一般物体認識 / 画像シーン認識 低解像度 見えの違い 奥行きによるサイズの違い 　　⇒局所的な認識法が通用しない 人間は物体間の関係を利用 三次元構造のモデル化 局所的な認識手法を高精度に. Putting Objects in Perspective. Derek Hoiem ， Alexei A. Efros ， Martial Hebert

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CV 輪講 Putting Objects in Perspective

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  1. CV輪講Putting Objects in Perspective 藤吉研究室　土屋成光 2008年7月1日

  2. Background • 一般物体認識/画像シーン認識 • 低解像度 • 見えの違い • 奥行きによるサイズの違い 　　⇒局所的な認識法が通用しない • 人間は物体間の関係を利用 • 三次元構造のモデル化 • 局所的な認識手法を高精度に

  3. Putting Objects in Perspective Derek Hoiem，Alexei A. Efros，Martial Hebert Carnegie Mellon UniversityRobotics Institute CVPR2006

  4. Understanding an Image

  5. Today: Local and Independent

  6. 検出結果

  7. Local Object Detection False Detections True Detection Missed Missed True Detections Local Detector: [Dalal-Triggs 2005]

  8. Object Support

  9. Object Surface? Support? Surface Estimation Image Support Vertical Sky V-Center V-Right V-Porous V-Solid V-Left [Hoiem, Efros, Hebert ICCV 2005] Software available online

  10. Object Size in the Image Image World

  11. Object Size ↔ Camera Viewpoint Loose Viewpoint Prior Input Image

  12. Object Size ↔ Camera Viewpoint Loose Viewpoint Prior Input Image

  13. Object Size ↔ CameraViewpoint Viewpoint Object Position/Sizes

  14. Object Size ↔ Camera Viewpoint Viewpoint Object Position/Sizes

  15. Object Size ↔ Camera Viewpoint Viewpoint Object Position/Sizes

  16. Object Size ↔ Camera Viewpoint Viewpoint Object Position/Sizes

  17. Efficient from surfaceand viewpoint Image P(surfaces) P(viewpoint) P(object) P(object | surfaces) P(object | viewpoint)

  18. Efficient from surfaceand viewpoint Image P(surfaces) P(viewpoint) P(object | surfaces, viewpoint) P(object)

  19. Scene Parts Are All Interconnected Objects 3D Surfaces Camera Viewpoint

  20. Input to Algorithm Object Detection Surface Estimates Viewpoint Prior Local Car Detector Local Ped Detector Local Detector: [Dalal-Triggs 2005] Surfaces: [Hoiem-Efros-Hebert 2005]

  21. Approximate Model Objects 3D Surfaces Viewpoint

  22. Inference over Tree Viewpoint θ Local Object Evidence Local Object Evidence Objects ... o1 on Local Surface Evidence Local Surface Evidence Local Surfaces … s1 sn

  23. Viewpoint estimation Viewpoint Prior Viewpoint Final Likelihood Likelihood Horizon Height Horizon Height

  24. ObjectIdentitie • Localdetector

  25. Surface Geometry Probability map

  26. Object detection Car: TP / FP Ped: TP / FP Initial (Local) Final (Global) Car Detection 4 TP / 1 FP 4 TP / 2 FP Ped Detection 4 TP / 0 FP 3 TP / 2 FP Local Detector: [Dalal-Triggs 2005]

  27. Experiments on LabelMe Dataset • Testing with LabelMe dataset: 422 images • 923 Cars at least 14 pixels tall • 720 Peds at least 36 pixels tall

  28. Each piece of evidence improves performance Pedestrian Detection Car Detection Local Detector from [Murphy-Torralba-Freeman 2003]

  29. Can be used with any detector that outputs confidences Car Detection Pedestrian Detection Local Detector: [Dalal-Triggs 2005] (SVM-based)

  30. Accurate Horizon Estimation [Murphy-Torralba-Freeman 2003] [Dalal- Triggs 2005] Horizon Prior Median Error: 8.5% 4.5% 3.0% 90% Bound:

  31. Qualitative Results Car: TP / FP Ped: TP / FP Initial: 2 TP / 3 FP Final: 7 TP / 4 FP Local Detector from [Murphy-Torralba-Freeman 2003]

  32. Qualitative Results Car: TP / FP Ped: TP / FP Initial: 1 TP / 14 FP Final: 3 TP / 5 FP Local Detector from [Murphy-Torralba-Freeman 2003]

  33. Qualitative Results Car: TP / FP Ped: TP / FP Initial: 1 TP / 23 FP Final: 0 TP / 10 FP Local Detector from [Murphy-Torralba-Freeman 2003]

  34. Qualitative Results Car: TP / FP Ped: TP / FP Initial: 0 TP / 6 FP Final: 4 TP / 3 FP Local Detector from [Murphy-Torralba-Freeman 2003]

  35. Geometric Context • Estimate surface ground: green, sky: blue, vertical: red, o:porous, x: solid

  36. Perspective Geometric Cues Color Texture Location

  37. Robust Spatial Support • oversegmentation RGB Pixels Superpixels [Felzenszwalb and Huttenlocher 2004]

  38. Multiple Segmentations • 単一のセグメントではセグメントエラーの可能性 • 複数のセグメント数でセグメンテーション Multiple Segmentations Superpixels …

  39. Labeling Segments … … 各セグメント結果を統合

  40. Learn from training images • 前準備 • multiple segmentationの算出 • 各セグメントのラベルの算出 – ground, vertical, sky, or “mixed” • boosted decision trees による密度計算 • 8 nodes per tree • Logistic regression version of Adaboost [Collins and Schapire and Singer 2002] Homogeneity Likelihood Label Likelihood

  41. Image Labeling Labeled Segmentations … Learned from training images Labeled Pixels

  42. meters meters Summary & Future Work Ped Ped Car • Reasoning in 3D: • Object to object • Scene label • Object segmentation

  43. Conclusion • Image understanding is a 3D problem • Must be solved jointly • This paper is a small step • Much remains to be done

  44. CV輪講Recovering Occlusion Boundaries from a Single Image,Closing the Loop in Scene Interpretation 藤吉研究室　土屋成光 2008年8月26日

  45. Background • 一般物体認識/画像シーン認識 • 低解像度 • 見えの違い • 奥行きによるサイズの違い 　　⇒局所的な認識法が通用しない • 人間は物体間の関係を利用 • 三次元構造のモデル化 • 局所的な認識手法を高精度に

  46. Recovering Occlusion Boundaries from a Single Image Derek Hoiem，Andrew N. Stein, Alexei A. Efros，Martial Hebert Carnegie Mellon UniversityRobotics Institute ICCV’07

  47. 単画像からのオクルージョン理解 • オクルージョン，境界理解 • 物体を探索する際に必須 • Edge, region, depthによって推定

  48. 手法の流れ • 千領域にセグメンテーション Watershed with Pb soft boundaries • Region, Boundary, 3D Cuesの算出 depth : horizon + junction to ground • Boundaryの算出 Conditional random field (CRF) • Boundaryを用いて更にセグメンテーション

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