RESUMO
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. The optimization is designed to recover erroneous pixels using the intensities of their neighboring pixels while retaining the other correct pixels to preserve the original image details. We evaluated the proposed method qualitatively and quantitatively through experimentation using datasets of synthetic and real-world scenes. In the experiments, the proposed method outperforms existing state-of-the-art methods.
RESUMO
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. The use of RL enables even partially labeled data to be successfully utilized for semisupervised learning. Through the evaluation of the object tracking benchmark data set, the proposed tracker is validated to achieve a competitive performance at three times the speed of existing deep network-based trackers. The fast version of the proposed method, which operates in real time on graphics processing unit, outperforms the state-of-the-art real-time trackers with an accuracy improvement of more than 8%.