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The module of symbols detection is solved by the belief propagation (BP) algorithm [15, 16] in Section 3.1.
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This tracking is carried out employing a new two-step method: (1) the detections-to-tracks association is solved by using Kalman filtering combined with an own-designed cost optimization for the Linear Sum Assignment Problem (LSAP); and (2) the occlusion management is based on SVM kernels to compute distances between appearance features such as GCH, LBP and HOG.
We have seen that this problem is solved by incorporating properties of the rectifying transient cell in the target detection pathway.
"It is solved by walking".
Output O2 is solved by user judgement.
Each cell block is solved by one CUDA block and is considered a single solving unit.
Potential disagreement is solved by discussion until consensus is reached.
The system is solved by nonlinear LMS optimization.
According to these expressions, rectangle detection can be solved by searching for those combinations of (α,d 1⇔2,d 2⇔3,d 3⇔4,d 4⇔1) for which H r /(2∗w+2∗h)>τ r, given a certain value of τ r near to 1.
Many computer vision problems, such as segmentation, detection, recognition, and matching, can be solved by machine learning techniques (Bishop 2007).
This abnormality detection and localization problem can be solved by analyzing newborn EEG signals and extracting features, which are then classified.
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