Institution profile

University of Western Australia

Academic institutionaustralasia · au
Official website
Research library273linked papers
Opportunities0open roles
Selected work

Representative Papers

Multiview Point Cloud Registration Based on Minimum Potential Energy for Free-Form Blade Measurement

Feb 11, 2025IEEE Transactions on Instrumentation and Measurement

In industrial metrology, global registration of multi-view point clouds from freeform turbine blades suffers from low accuracy due to severe noise and substantial data incompleteness. To address this, this paper proposes a novel Minimum Potential Energy (MPE)-based registration method. It innovatively introduces a physical potential energy model into point cloud registration, formulating a weighted MPE optimization objective. A dual-flag mechanism is designed to dynamically assess registration status, while a coarse-to-fine strategy enhances robustness and convergence. Furthermore, a force-guided operator and an improved TrICP algorithm are introduced. Experiments on four real-world blade datasets demonstrate that the proposed method achieves higher registration accuracy and superior noise resilience compared to state-of-the-art global registration approaches, significantly improving the reliability and practicality of industrial-grade freeform surface reconstruction.

16 citationsRead paper

Deepfake Detection with Spatio-Temporal Consistency and Attention

Nov 30, 2022International Conference on Digital Image Computing: Techniques and Applications

Existing Deepfake detection methods often overlook local spatiotemporal inconsistencies and subtle forgery patterns. To address this, we propose an end-to-end neural network that jointly models frame-level spatial attention and sequence-level distance attention for fine-grained forgery localization and classification. Our approach innovatively fuses texture-enhanced shallow and deep features and introduces a distance attention mechanism to explicitly capture cross-frame temporal dependencies, thereby modeling spatiotemporal-coupled forgery signatures. Built upon a ResNet backbone, the architecture integrates spatial attention, distance attention, multi-level feature fusion, and texture enhancement modules. Extensive experiments demonstrate that our method achieves state-of-the-art performance on FaceForensics++ and Celeb-DF benchmarks, outperforming prior approaches in detection accuracy while maintaining superior efficiency in memory footprint and computational cost.

4 citationsRead paper

ANUBIS: Skeleton Action Recognition Dataset, Review, and Benchmark

May 04, 2022arXiv.org

Existing 3D skeleton-based action recognition research suffers from fragmented representation taxonomies and evaluation protocols misaligned with real-world scenarios; moreover, mainstream datasets lack critical dimensions—including rear-view perspectives, multi-person interactions, fine-grained or violent actions, and pandemic-era behaviors. To address these gaps, we propose a four-dimensional taxonomy (dataset design, spatial modeling, temporal modeling, and signal enhancement) and introduce ANUBIS: the first large-scale, multi-view 3D skeleton dataset explicitly designed for realistic challenges. ANUBIS features rear-view captures, 101 action classes (including 21 pandemic-related behaviors), and standardized recordings from 128 participants using Azure Kinect’s multi-sensor fusion. We further establish a unified benchmark framework, enabling reproducible evaluation of 12 state-of-the-art models. Our analysis identifies temporal modeling capacity and signal robustness as the primary bottlenecks limiting current performance.

4 citationsRead paper

What Makes a Good TODO Comment?

May 13, 2024ACM Transactions on Software Engineering and Methodology

TODO comments in open-source projects suffer from pervasive low quality (46.7% are vague, information-deficient, or lack practical utility) and chronic lack of resolution, demanding systematic governance. This study first proposes a multidimensional high-quality TODO criterion, empirically derived from lifecycle analysis and management practice comparison of 2,863 TODOs across GitHub’s Top 100 Java repositories. We then develop the first CodeBERT-based fine-tuned model for TODO quality assessment, achieving an F1-score of 0.89 on binary classification. Finally, we deliver actionable writing guidelines and governance recommendations. Our core contributions are threefold: (1) theoretically, the first comprehensive TODO quality assessment framework; (2) methodologically, the first deep learning–driven automated quality identification system; and (3) practically, community-adoptable, evidence-based pathways for improving TODO quality in open-source development.

3 citationsRead paper

Modeling Human Skeleton Joint Dynamics for Fall Detection

Nov 01, 2021International Conference on Digital Image Computing: Techniques and Applications

To address high privacy risks in real-time elderly fall detection, the neglect of inter-frame joint dependencies in existing skeleton-based methods, model parameter redundancy, and evaluation bias under small-data regimes, this paper proposes a lightweight spatiotemporal graph convolutional network (ST-GCN). The method jointly models the temporal dynamics and spatial topological dependencies of skeletal joints, constructing a motion-dynamics-aware dynamic graph structure; model compression is achieved via channel pruning and hierarchical sparsity. Evaluated on three benchmark datasets—NTU RGB+D, NW-UCLA, and SYSU 3D—the approach achieves state-of-the-art performance: it reduces model parameters by 42%, improves average accuracy by over 3.2%, and eliminates raw video capture entirely, enabling image-free privacy-preserving fall recognition.

2 citationsRead paper
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