Soft multilabel learning and deep feature fusion for unsupervised person re-identification

Abstract

When the microLED is in the forward working direction, it is difficult to precisely adjust its voltage to obtain different brightness. Moreover, when the microLED/OLED is turned on, they will be in a closed state for a long time, causing the image display brightness to be deteriorated by the human eye. In order to solve these problems, this paper proposes a dual-frame decentralized fusion scanning strategy to achieve different brightness by adjusting the microLED/OLED on-time. Firstly, the method de-weights the data bits and inserts their on-times into the closed time. Then the data bit weights are double-frame fused after decentralization. Finally, the scanning order of the data bits is redefined. According to the proposed scanning strategy, we designed a scanning controller to drive digital on-silicon microdisplay. The results show that the dual-frame decentralized fusion scan proposed in this paper can accurately adjust the luminance of microLED/OLED and improve the brightness of the image observed by human eyes. Compared with other scanning strategies, the scanning strategy improves the scanning efficiency to 93.75%, the field frequency is increased to 2040 Hz, the scanning clock frequency is 102.36 MHz, and the scanning data bandwidth is reduced. The feasibility of the scan controller is proved by testing at last.