Optimization-based method removing highlights with flash/no-flash image.

This paper proposes an optimization-based method to remove highlights from specular surfaces without any special device other than a flash, or any limited capturing environment required by existing methods. Accordingly, we formulate an optimization problem to estimate a highlight-free image from the difference image between a flash/no-flash image pair. This optimization will automatically find and recover erroneous pixels (saturated pixels and remaining highlights) in the difference image.

Action-Driven Visual Object Tracking With Deep Reinforcement Learning.

In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and background. The pretraining is done by utilizing deep reinforcement learning (RL) as well as supervised learning.