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Fig. 2 Processing details of sample proposal strategies.

Strategies of sample proposal: In order to use informative samples for training a scene-specialized classifier, we put forward two sample-proposal strategies.

Average of 3 samples Any individual sample Proposal I Minimum 90%% of specified fiber content.

Moreover, we propose different strategies for the three steps of the Monte Carlo filter: (2) Strategies of sample proposal: In order to use informative samples for training a scene-specialized classifier, we put forward two sample-proposal strategies.

The sample proposal strategies consist in suggesting a set of target samples to be added in the specialized dataset.

Figure 11 reports the results of our specialization algorithm according to the sample proposal strategies while using the OAS strategy as an observation one.

The parameter α s reflects the ratio between the number of sample proposals returned at the current iteration and the number of sample proposals in the previous iteration.

Figure 10 demonstrates that the number of sample proposals stabilizes from iteration 4, which marks the validation of the stopping criterion.

Models of roads and vehicles based on edge information are presented, the Bayesian problem's formulations are constructed, and a Markov chain is designed to sample proposals to detect vehicles.

Fig. 3 Illustration of sample-proposal strategies.

Fig. 11 Comparison of sample-proposal strategies.