De-fencing is to eliminate the captured fence on an image or a video, providing a clear view of the scene. It has been applied for many purposes including assisting photographers and improving the performance of computer vision algorithms such as object detection and recognition. However, the state-ofthe-art de-fencing methods have limited performance caused by the difficulty of fence segmentation and also suffer from the motion of the camera or objects. To overcome these problems, we propose a novel method consisting of segmentation using convolutional neural networks and a fast/robust recovery algorithm. The segmentation algorithm using convolutional neural network achieves significant improvement in the accuracy of fence segmentation. The recovery algorithm using optical flow produces plausible de-fenced images and videos. The proposed method is experimented on both our diverse and complex dataset and publicly available datasets. The experimental results demonstrate that the proposed method achieves the state-of-the-art performance for both segmentation and content recovery.
Abstract-A low-power fully integrated bioamplifier is presented that can amplify signals in the range from mHz to kHz while rejecting large DC offsets generated at the electrode-tissue interface. The novel aspect of this amplifier is that its analog output is represented by a series of pulses which provide a low-power, noise-resistant means for coding and transmission. The original analog signal can be reconstructed from the resulting pulse train with 13 bit precision at a remote site where power consumption is not so crucial. The fabricated analog amplifier exhibits a gain of 39.5dB from 0.3 Hz to 5.4k Hz. The power consumption of the whole system is less than 300 µW/channel from a 5-V supply. The fully integrated system was designed in the AMI 0.6µm CMOS process and it consumes 0.088 mm 2 /channel of chip area.
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