Efficient Neuromorphic Vision Pipelines for Egocentric Perception on Low Power Systems

卷积神经网络在广泛应用于计算机视觉问题时取得了巨大的成功。然而，它们在效率和能力上仍然明显优于人类视觉系统。这自然导致了将人类视觉系统中的机制纳入cnn以提高其性能和效率的愿望。格拉斯哥大学目前的研究正在探索一种受生物学启发的软件视网膜的效用和对数极图像变换的扩展，以减少cnn的记忆需求和训练时间，并提高其规模和旋转不变性。这项工作旨在研究一种新的CNN架构的发展，该架构专门用于处理软件视网膜的输出，以最大限度地提高其计算效率以及在经典计算机视觉问题中的性能。同时，这项研究将为生物学上可信的视觉系统提供一个更完整的计算模型。因此，本研究的目标是设计一种新颖的卷积神经网络架构，以最佳方式处理软件视网膜的多分辨率输出，并改进两者之间的接口，如下所示

Convolutional Neural Networks have seen great success when applied to a wide range of computer vision problems. However, they are still significantly outperformed by the human visual system in both efficiency and ability. This naturally leads to a desire to incorporate mechanisms found in the human visual system into CNNs to improve their performance and efficiency. Current research at the University of Glasgow is exploring the utility of a biologically inspired software retina and an extension of the log-polar image transform to reduce the memory requirement and training time of CNNs as well as improving their scale and rotation invariance. This work intends to investigate the development of a novel CNN architecture that is specifically designed to process the output of the software retina in order to maximize its computational efficiency as well as its performance in classic computer vision problems. Simultaneously this research will offer a more complete computational model of a biologically plausible vision system. Accordingly, the objective of this research is to design a novel Convolutional Neural Network architecture that optimally processes the multiresolution output of a Software Retina and also improve the interface between the two, as listed below