CAJ | 학술논문

提出了一种从二维视图识别三维目标的多网络融合方法，基于单个网络分类的置信度概念，有效地结合多个网络的输出结果作出最终分类判决。应用三个多层前向网络(隐层神经元数、初始权值等取不同值)，设计了基于分类确信度的多网络融合结构。对四类车辆目标进行的识别实验表明，所提出的多网络融合方法明显优于单个网络的识别性能。
제출료일충종이유시도식별삼유목표적다망락융합방법，기우단개망락분류적치신도개념，유효지결합다개망락적수출결과작출최종분류판결。응용삼개다층전향망락(은층신경원수、초시권치등취불동치)，설계료기우분류학신도적다망락융합결구。대사류차량목표진행적식별실험표명，소제출적다망락융합방법명현우우단개망락적식별성능。
A multiple networks fusion approach is proposed for 3D object recognition from 2D views. As the probability of correct classification is correlated with certainty of a network, a fusion method based on certainty is developed which combines the outputs from all the neural networks to improve classification performance. A multiple networks fusion structure is constructed by combining three multi-layer forward propagation network that differ from the others in internal parameters such as the number of hidden layer nodes, initial random weights et al.. The performance is compared to that of individual MLP using four different vehicles involving clean and noisy images. It is shown that multiple networks fusion has major advantages over single multi-payer forward propagation network.