3-D mean-separation-type short-time DFT with its application to moving-image denoising

Komatsu, Takashi; Tyon, Ken; Saito, Takahiro

doi:10.1109/icip.2017.8296825

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kaur et al [46] propose a Fractional Fourier Transform based Riesz fractional derivative approach for edge detection and apply it to enhance images. Komatsu et al [73] construct the 3-D mean-separation-type short-time DFT and apply it to denoise moving images. It is also worth noting that FTs have been widely applied in handling time-series forecasting problems.…”

Section: Applications Of Fourier Transformsmentioning

confidence: 99%

View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums

Wong

Xia

Hong

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more heterogeneous trajectories with different representation forms, such as 2D or 3D coordinates, 2D or 3D bounding boxes, and even high-dimensional human skeletons, need to be analyzed and forecasted. Among these heterogeneous trajectories, interactions between different elements within a frame of trajectory, which we call the "Dimension-Wise Interactions", would be more complex and challenging. However, most previous approaches focus mainly on a specific form of trajectories, which means these methods could not be used to forecast heterogeneous trajectories, not to mention the dimension-wise interaction. Besides, previous methods mostly treat trajectory prediction as a normal time sequence generation task, indicating that these methods may require more work to directly analyze agents' behaviors and social interactions at different temporal scales. In this paper, we bring a new "view" for trajectory prediction to model and forecast trajectories hierarchically according to different frequency portions from the spectral domain to learn to forecast trajectories by considering their frequency responses. Moreover, we try to expand the current trajectory prediction task by introducing the dimension M from "another view", thus extending its application scenarios to heterogeneous trajectories vertically. Finally, we adopt the bilinear structure to fuse two factors, including the frequency response and the dimension-wise interaction, to forecast heterogeneous trajectories via spectrums hierarchically in a generic way. Experiments show that the proposed model outperforms most state-of-the-art methods on ETH-UCY benchmark, Stanford Drone Dataset and nuScenes with heterogeneous trajectories, including 2D coordinates, 2D and 3D bounding boxes. ! * Equal contribution. Codes are available at https://github.com/cocoon2wong/E-Vertical.

show abstract

Section: Applications Of Fourier Transformsmentioning

confidence: 99%