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3-D Depth Reconstruction from a Single Still Image 何開暘 2010.6.11.

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Presentation on theme: "3-D Depth Reconstruction from a Single Still Image 何開暘 2010.6.11."— Presentation transcript:

1 3-D Depth Reconstruction from a Single Still Image 何開暘 2010.6.11

2 Visual Cues for Depth Perception Monocular Cues  Texture variations, texture gradients, interposition, occlusion, known object sizes, light and shading, haze, defocus Stereo Cues Motion Parallax and Focus Cues

3 image → feature → depth Chose features that capture 3 types of cues: texture variations, texture gradients, and color Model conditional distribution of depths given monocular image features p(d|x) Estimate parameters by maximizing conditional log likelihood of training data Given an image, find MAP estimate of depths

4 Outline Introduction Feature Vector Probabilistic Model Experiments Reference

5 Feature vectors Two types of features  Absolute depth features―used to estimate absolute depth at a particular patch  Relative features―used to estimate relative depths Capture three types of cues  Texture variation―apply Law ’ s masks to intensity channel  Haze―apply a local averaging filter to color channels  Texture gradient―apply six oriented edge filters to intensity channel

6 Features for Absolute Depth Compute summary statistics of a patch i in the image I(x,y) as follows  Use the output of each of the 17 (9 Law’s masks, 2 color channels and 6 texture gradients) filters Fn, n=1,…,17 as: (dimension 34) To estimate absolute depth at a patch, local image features centered on the patch are insufficient Use more global properties

7 More Global Properties Use image features extracted at multiple spatial scales (three scale) Features used to predict depth of a particular patch are computed from that patch as well as 4 neighboring patches (Repeated at each of the three scales) Add to features of a patch additional summary features of the column it lies in (5*3+4)*34=636 dimensional

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9 Features for Relative Depth To learn the dependencies between two neighboring patches Compute a 10-bin histogram of each of the 17 filter outputs, giving a total of 170 features y is for each patch i at scale s Relative depth features y ijs for two neighboring patches i and j at scale s will be the differences between their histogram, i.e., y ijs =y is -y js

10 Outline Introduction Feature Vector Probabilistic Model Experiments Reference

11 Gaussian Model

12 Laplacian Model

13 Outline Introduction Feature Vector Probabilistic Model Experiments Reference

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15 Result

16 Improving Performance of Stereovision using Monocular Cues

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18 The average errors as a function of the distance from the camera

19 Reference A.Y. Ng A. Saxena, S.H. Chung. 3-d depth reconstruction from a single still image. In International Journal of Computer Vision (IJCV), 2007. Michels, J., Saxena, A., & Ng, A. Y. (2005). High speed obstacle avoidance using monocular vision and reinforcement learning. In 22 nd international conference on machine learning (ICML).


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