Driving Rhythm Method for Driving Comfort Analysis on Rural Highways

Yu, Bo; Chen, Yu-Ren

doi:10.7307/ptt.v29i4.2217

Cited by 6 publications

(1 citation statement)

References 23 publications

(25 reference statements)

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“…In recent years, spline curves have been used in computer graphics applications to generate curves of various shapes. For example, automatic driving lane lines (Ma et al 2019;Yu and Chen 2017), text detection (Liu et al 2020;Tang et al 2022;Nguyen et al 2021), Fault detection (Park et al 2011;Guo and Wang 2005), etc. Among them, CatmullRom spline function is a classic interpolating spline, which is suitable for parameterization of tampered regions due to its fitting effect and inference cost (Chandra 2020;.…”

Section: Catmullrom Splines Detectionmentioning

confidence: 99%

CatmullRom Splines-Based Regression for Image Forgery Localization

Zhang,

Xu,

et al. 2024

AAAI

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IFL (Image Forgery Location) helps secure digital media forensics. However, many methods suffer from false detections (i.e., FPs) and inaccurate boundaries. In this paper, we proposed the CatmullRom Splines-based Regression Network (CSR-Net), which first rethinks the IFL task from the perspective of regression to deal with this problem. Specifically speaking, we propose an adaptive CutmullRom splines fitting scheme for coarse localization of the tampered regions. Then, for false positive cases, we first develop a novel re-scoring mechanism, which aims to filter out samples that cannot have responses on both the classification branch and the instance branch. Later on, to further restrict the boundaries, we design a learnable texture extraction module, which refines and enhances the contour representation by decoupling the horizontal and vertical forgery features to extract a more robust contour representation, thus suppressing FPs. Compared to segmentation-based methods, our method is simple but effective due to the unnecessity of post-processing. Extensive experiments show the superiority of CSR-Net to existing state-of-the-art methods, not only on standard natural image datasets but also on social media datasets.

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