基于图像数据转换和深度学习的相位OTDR事件检测

FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation

Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos. Especially, the prediction based approach is one of the most studied methods to detect anomalies by predicting frames that include abnormal events in the test set after learning with the normal frames of the training set. However, a lot of prediction networks are computationally expensive owing to the use of pre-trained optical flow networks, or fail to detect abnormal situations because of their strong generative ability to predict even the anomalies. To…

视觉感受野广义高斯导数模型在自然图像变换下的协方差特性

Covariance properties under natural image transformations for the generalized Gaussian derivative model for visual receptive fields

本研究针对视觉感受野的广义高斯导数模型，推导其在多种自然几何图像变换下的协方差特性，揭示其支持视觉系统实现几何不变性的机制，并探讨对生物视觉的启示。

根据视觉感受野的广义高斯导数模型，几何图像变换下时空感受野联合协方差性质的统一理论

Unified theory for joint covariance properties under geometric image transformations for spatio-temporal receptive fields according to the generalized Gaussian derivative model for visual receptive fields

本文针对视觉感受野模型，证明了一组在空间缩放、仿射、伽利略和时间缩放变换组合下的时空感受野联合协方差性质，并将尺度归一化导数扩展为仿射归一化导数，为不同视角和速度下的时空感受野响应匹配提供了理论基础。

Motion Equivariance OF Event-based Camera Data with the Temporal Normalization Transform

In this work, we focus on using convolution neural networks (CNN) to perform object recognition on the event data. In object recognition, it is important for a neural network to be robust to the variations of the data during testing. For traditional cameras, translations are well handled because CNNs are naturally equivariant to translations. However, because event cameras record the change of light intensity of an image, the geometric shape of event volumes will not only depend on the objects but also on their relative motions with respect to the camera. The deformation of the events caused b…

Event-guided Multi-patch Network with Self-supervision for Non-uniform Motion Deblurring

Contemporary deep learning multi-scale deblurring models suffer from many issues: 1) They perform poorly on non-uniformly blurred images/videos; 2) Simply increasing the model depth with finer-scale levels cannot improve deblurring; 3) Individual RGB frames contain a limited motion information for deblurring; 4) Previous models have a limited robustness to spatial transformations and noise. Below, we extend the DMPHN model by several mechanisms to address the above issues: I) We present a novel self-supervised event-guided deep hierarchical Multi-patch Network (MPN) to deal with blurry images …