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Researcher profile

Kuo-Liang Chung

Kuo-Liang Chung contributes to research discovery and scholarly infrastructure.

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Computer Visioneess.IV

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Published work

3 published item(s)

preprint2026arXiv

Point Cloud Registration via Probabilistic Self-Update Local Correspondence and Line Vector Sets

Point cloud registration (PCR) is a fundamental task for integrating 3D observations in remote sensing applications. This paper proposes a fast and effective PCR algorithm utilizing probabilistic self-updating local correspondence and line vector sets. Our dual RANSAC interaction model comprises a global RANSAC evaluating the global correspondence set and a local RANSAC operating on dynamically updated local sets. Initially, these local sets are constructed using angle histogram statistics and line vector length preservation techniques. To improve accuracy, a probabilistic self-updating strategy refines the local sets after each interaction round. To reduce runtime, we introduce a global early termination condition that optimally balances accuracy and efficiency. Finally, a weighted singular value decomposition estimates the registration solution. Evaluations on public datasets demonstrate our algorithm achieves superior time efficiency and at least a 10% root mean square error improvement over state-of-the-art methods. The C++ source code is publicly available at https://github.com/ivpml84079/Probabilistic-Self-Update-Line-Vector-Set-Based-Point-Cloud-Registration.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Reduced Codebook and Re-Interpolation Approach for Enhancing Quality in Chroma Subsampling

Prior to encoding RGB full-color images or Bayer color filter array (CFA) images, chroma subsampling is a necessary and crucial step at the server side. In this paper, we first propose a flow diagram approach to analyze the coordinate-inconsistency (CI) problem and the upsampling process-inconsistency (UPI) problem existing in the traditional and state-of-the-art chroma subsampling methods under the current coding environment. In addition, we explain why the two problems degrade the quality of the reconstructed images. Next, we propose a reduced codebook and re-interpolation (RCRI) approach to solve the two problems for enhancing the quality of the reconstructed images. Based on the testing RGB full-color images and Bayer CFA images, the comprehensive experimental results demonstrated at least 1.4 dB and 2.4 dB quality improvement effects, respectively, of our RCRI approach against the CI and UPI problems for the traditional and state-of-the-art chroma subsampling methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Novel and Effective CNN-Based Binarization for Historically Degraded As-built Drawing Maps

Binarizing historically degraded as-built drawing (HDAD) maps is a new challenging job, especially in terms of removing the three artifacts, namely noise, the yellowing areas, and the folded lines, while preserving the foreground components well. In this paper, we first propose a semi-automatic labeling method to create the HDAD-pair dataset of which each HDAD-pair consists of one HDAD map and its binarized HDAD map. Based on the created training HDAD-pair dataset, we propose a convolutional neural network-based (CNN-based) binarization method to produce high-quality binarized HDAD maps. Based on the testing HDAD maps, the thorough experimental data demonstrated that in terms of the accuracy, PSNR (peak-signal-to-noise-ratio), and the perceptual effect of the binarized HDAD maps, our method substantially outperforms the nine existing binarization methods. In addition, with similar accuracy, the experimental results demonstrated the significant execution-time reduction merit of our method relative to the retrained version of the state-of-the-art CNN-based binarization methods.

0 citations0 reviewsNo code linkedOpen access