Abstract. In order to improve the measurement accuracy of binocular vision system, a structural model is build. On the basis of it, relational features between target point and structural parameter of system are analyzed, and the effect of each parameter to the measurement accuracy is simulated. According to simulation results, the general principles and methods of structure configuration for binocular vision system is offered.
Target classification based on the transferable belief model (TBM) is believed to be more robust than the Bayesian method. However, existing TBM classifier may forget over time the estimated prior information of the class. This paper proposes a recursive TBM classifier, which could combine the current basic belief assignment (BBA) of the class with the historic class information. Besides, feature mapping from the feature space to the class space, instead of the conventional converse mapping, is utilized to improve the performance of the recursive classifier. Simulation results reveal that the proposed TBM classifier eliminated the deficiency of existing TBM method and has more robust performance than the Bayesian classifier.
In order to improve the detection effect of the weak small infrared targets, we present an improved morphological filtering algorithm based on the classical mathematical morphology filtering. In the algorithm, the combination of morphological operations, multi -scale and multi -structure elements are used to filter the image. The experiments are simulated on the MATLAB platform, and the results evaluate the effectiveness of the improved algorithm.
KEYWORDSmorphological filtering, multi-scale and multi-structure elements, infrared image preprocessing.
In recent years, the tracking methods for near-air infrared objects have been developed rapidly, but there is no dataset for the evaluation of these methods. In view of this, a series of near-air infrared sequences was collected by the aircraft, then the dataset for near-air infrared object tracking was accomplished. The dataset is aimed at various challenges of the short-term single-object tracking, and it followed the VOT-TIR2015.
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