Download e-book for kindle: Advanced Concepts for Intelligent Vision Systems: 14th by Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon,

By Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Pavel Zemčík (eds.)

ISBN-10: 3642331394

ISBN-13: 9783642331398

ISBN-10: 3642331408

ISBN-13: 9783642331404

This booklet constitutes the completely refereed lawsuits of the 14th overseas convention on complex options for clever imaginative and prescient structures, ACIVS 2012, held in Brno, Czech Republic, in September 2012. The forty six revised complete papers have been conscientiously chosen from eighty one submissions and care for snapshot research and computing device imaginative and prescient with a spotlight on detection, acceptance, monitoring and identification.

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Extra resources for Advanced Concepts for Intelligent Vision Systems: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012. Proceedings

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The purpose of our active calibration is to correct the left and right angle of the eye axes to reduce the depth error. The basics of this method is transforming each image plane to be orthogonal to each eye axis. , the intrinsic parameters A and extrinsic parameters: Re , Te , Rc and Tc are known. The intrinsic parameters can be calibrated by using various algorithms in which Zhang’s method [16] is widely used. Also, the rotation matrices Rxt (ϕ) and Ryt (θ) are known. In real applications, these two parameters can be read from motor encoders.

The coordinatec of C is given by: ˆ c = −h−1 h (19) H = [h |−hc ] (20) therefore, we can write H as: then, we have: ⎡ ⎤ ⎡ ⎤ xw u ⎢ yw ⎥ ⎥ λ ⎣ v ⎦ = [h| − hc] ⎢ ⎣ zw ⎦ 1 1 (21) Consequently, the transformation from homogeneous world coordinates to homogeneous image coordinates converts to a transformation from homogeneous image coordinates to world coordinates: Pw = c + λh−1 P˜im (22) The main point behind active calibration is the generation of new left and right projection matrices according to time t.

E. a 3D occupancy grid for active view planning), are updated with the new sensor measurements. During the third step, a set of candidate viewpoints is computed using the viewpoint generator. see Sec. 5. Finally, the view with the highest information gain is selected after simulating each candidate viewpoint, see Sec. 6. 0 ms. 40 S. Foix et al. Algorithm 1. Autonomous active view planning M ← ACTIVE V IEW P LANNING(x0 , Σ s , O, S) I NPUTS : x0 : Initial sensor pose in global coordinates. Σ s : Sensor measurement covariance matrix.

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Advanced Concepts for Intelligent Vision Systems: 14th International Conference, ACIVS 2012, Brno, Czech Republic, September 4-7, 2012. Proceedings by Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Pavel Zemčík (eds.)


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