By Deokwoo Lee, Hamid Krim (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders, Pavel Zemčík (eds.)
This booklet constitutes the completely refereed lawsuits of the 14th overseas convention on complex techniques for clever imaginative and prescient platforms, ACIVS 2012, held in Brno, Czech Republic, in September 2012. The forty six revised complete papers have been rigorously chosen from eighty one submissions and take care of picture research and computing device imaginative and prescient with a spotlight on detection, reputation, monitoring and identification.
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The latter choice of baseline is not constant, resulting in a more complicated computation model for the reason that the lengths between rotation centers and optical centers are less easy to estimate. 2 Depth Calculation We use a simpliﬁed model for the depth calculation as shown in Fig. 2 x PZ Zc M el M M Z T Tl r br bl er baseline Fig. 2. e. eye, camera and object are aligned without rotation and translation, we can easily derive the following equations: tan(θl ) = Z bl (11) tan(θr ) = Z br (12) b = bl + br (13) b 1/tan(θl ) + 1/tan(θr ) (14) From (11), (12) and (13), we have: Z = where b = er − el is the baseline, θl and θr are the left angle of the left eye axis and the right angle of the right eye axis, respectively.
Moreover, depth calibration is applied to improve 3D ToF camera measurements following the methodology of . All together helps to get more accurate point clouds and to correctly register them in order to make the model grow. 2 Results The Zeus, Mozart and Camel objects are appropriate for the evaluation because they have been used previously in other works. Moreover, the obtained results can be compared with two previous approaches used at DLR in the past (Fig. 6). Although a straightforward comparison between them is not an easy task because each approach used different sensors, some interesting conclusions can be extracted.
5 with Fig. 4, the validity of our model as well as the depth calculation algorithm is shown. 2 Experiment 2 Experiment 2 investigates the inﬂuence of the object positioning accuracy on the depth error. First, we introduce the deﬁnition of positioning accuracy. Given the image center, we track the projected image point of an object inside a window of the size ε around the image center. We call ε the positioning accuracy. The smaller ε, the more accurate the positioning. As shown in Fig. 6, the depth error drops signiﬁcantly when the positioning accuracy increases 10 times, whereas after this threshold, the improvement of 34 X.