Deep Reinforcement Learning based Patch Selection for Illuminant Estimation

Previous deep learning based approaches to illuminant estimation either resized the raw image to lower resolution or randomly cropped image patches for the deep learning model. However, such practices would inevitably lead to information loss or the selection of noisy patches that would affect estimation accuracy. In this paper, we regard patch selection in neural network based illuminant estimation as a controlling problem of selecting image patches that could help remove noisy patches and improve estimation accuracy. To achieve this, we construct a selection network (SeNet) to learn a patch selection policy. Based on data statistics and the learning progression state of the deep illuminant estimation network (DeNet), the SeNet decides which training patches should be input to the DeNet, which in turn gives feedback to the SeNet for it to update its selection policy. To achieve such interactive and intelligent learning, we utilize a reinforcement learning approach termed policy gradient to optimize the SeNet. We show that the proposed learning strategy can enhance the illuminant estimation accuracy, speed up the convergence and improve the stability of the training process of DeNet. We evaluate our method on two public datasets and demonstrate our method outperforms state-of-the-art approaches

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world