14 research outputs found
DRKF: Distilled Rotated Kernel Fusion for Efficient Rotation Invariant Descriptors in Local Feature Matching
The performance of local feature descriptors degrades in the presence of
large rotation variations. To address this issue, we present an efficient
approach to learning rotation invariant descriptors. Specifically, we propose
Rotated Kernel Fusion (RKF) which imposes rotations on the convolution kernel
to improve the inherent nature of CNN. Since RKF can be processed by the
subsequent re-parameterization, no extra computational costs will be introduced
in the inference stage. Moreover, we present Multi-oriented Feature Aggregation
(MOFA) which aggregates features extracted from multiple rotated versions of
the input image and can provide auxiliary knowledge for the training of RKF by
leveraging the distillation strategy. We refer to the distilled RKF model as
DRKF. Besides the evaluation on a rotation-augmented version of the public
dataset HPatches, we also contribute a new dataset named DiverseBEV which is
collected during the drone's flight and consists of bird's eye view images with
large viewpoint changes and camera rotations. Extensive experiments show that
our method can outperform other state-of-the-art techniques when exposed to
large rotation variations.Comment: 8 pages, 7 figure
Statistical Prediction of the South China Sea Surface Height Anomaly
Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997-2007
Statistical Prediction of the South China Sea Surface Height Anomaly
Based on the simple ocean data assimilation (SODA) data, this study analyzes and forecasts the monthly sea surface height anomaly (SSHA) averaged over South China Sea (SCS). The approach to perform the analysis is a time series decomposition method, which decomposes monthly SSHAs in SCS to the following three parts: interannual, seasonal, and residual terms. Analysis results demonstrate that the SODA SSHA time series are significantly correlated to the AVISO SSHA time series in SCS. To investigate the predictability of SCS SSHA, an exponential smoothing approach and an autoregressive integrated moving average approach are first used to fit the interannual and residual terms of SCS SSHA while keeping the seasonal part invariant. Then, an array of forecast experiments with the start time spanning from June 1977 to June 2007 is performed based on the prediction model which integrates the above two models and the time-independent seasonal term. Results indicate that the valid forecast time of SCS SSHA of the statistical model is about 7 months, and the predictability of SCS SSHA in Spring and Autumn is stronger than that in Summer and Winter. In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997–2007