Selected work

Representative Papers

Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets

Jun 17, 2026

This study investigates the effectiveness of energy-based features for terrain classification in mobile robotics, particularly under modality-constrained scenarios where only a single sensory input is available. Through systematic evaluation on three public datasets, the work demonstrates for the first time that energy features alone possess strong discriminative capability as an independent modality. The experimental framework encompasses diverse deep learning architectures—including CNNs, RNNs, Encoder-only Transformers, and Mamba—augmented with automated hyperparameter tuning and optimized input sequence lengths. Results show that energy-only features achieve classification accuracies of 85–90%, which further improve to 96–99% when fused with inertial data, yielding an average accuracy gain of 1–2% and outperforming existing state-of-the-art methods.

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Vision-Language Based Expert Reporting for Painting Authentication and Defect Detection

Mar 13, 2026

This study addresses the reliance on expert intuition in thermographic analysis of cultural heritage artifacts, which lacks standardized, interpretable, and cross-sample comparable automated methods. The authors propose the first fully automated framework that integrates multimodal active infrared thermography with a vision-language model. By leveraging Principal Component Thermography (PCT), Thermographic Signal Reconstruction (TSR), and Pulse Phase Thermography (PPT), the framework generates a consensus segmentation mask to guide the vision-language model in producing structured diagnostic reports accompanied by uncertainty quantification. Experiments on marquetry samples demonstrate that the method reliably detects anomalies and yields consistent, generalizable, expert-level interpretations, significantly enhancing the systematic applicability of thermography in artifact conservation.

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Recent publications

Latest Papers

Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets

Jun 17, 2026

This study investigates the effectiveness of energy-based features for terrain classification in mobile robotics, particularly under modality-constrained scenarios where only a single sensory input is available. Through systematic evaluation on three public datasets, the work demonstrates for the first time that energy features alone possess strong discriminative capability as an independent modality. The experimental framework encompasses diverse deep learning architectures—including CNNs, RNNs, Encoder-only Transformers, and Mamba—augmented with automated hyperparameter tuning and optimized input sequence lengths. Results show that energy-only features achieve classification accuracies of 85–90%, which further improve to 96–99% when fused with inertial data, yielding an average accuracy gain of 1–2% and outperforming existing state-of-the-art methods.

0 citationsRead paper

Vision-Language Based Expert Reporting for Painting Authentication and Defect Detection

Mar 13, 2026

This study addresses the reliance on expert intuition in thermographic analysis of cultural heritage artifacts, which lacks standardized, interpretable, and cross-sample comparable automated methods. The authors propose the first fully automated framework that integrates multimodal active infrared thermography with a vision-language model. By leveraging Principal Component Thermography (PCT), Thermographic Signal Reconstruction (TSR), and Pulse Phase Thermography (PPT), the framework generates a consensus segmentation mask to guide the vision-language model in producing structured diagnostic reports accompanied by uncertainty quantification. Experiments on marquetry samples demonstrate that the method reliably detects anomalies and yields consistent, generalizable, expert-level interpretations, significantly enhancing the systematic applicability of thermography in artifact conservation.

0 citationsRead paper