Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Active Recurrence of Lighting Condition for Fine-Grained Change Detection
Authors: Qian Zhang, Wei Feng, Liang Wan, Fei-Peng Tian, Ping Tan
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive quantitative experiments and challenging real-world tasks on fine-grained change monitoring of cultural heritages verify the effectiveness of our approach. We also validate its generality to non-Lambertian scenes. |
| Researcher Affiliation | Academia | Qian Zhang1,2, Wei Feng1,2 , Liang Wan2,3, Fei-Peng Tian1,2, Ping Tan4 1 School of Computer Science and Technology, Tianjin University, China 2Key Research Center for Surface Monitoring and Analysis of Cultural Relics, SACH, China 3 School of Computer Software, Tianjin University, China 4 School of Computing Science, Simon Fraser University, Canada |
| Pseudocode | No | The paper includes a working flow diagram (Figure 2) but no formal pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement or link regarding open-source code for the described methodology. |
| Open Datasets | No | We build 7 scenes (S1 7) to evaluate our ALR method and baselines. S1 3 are near-Lambertian scenes. S4 7 are non Lambertian scenes, including many specular (S4, S7), transparent (S5) and cast shadow (S6) regions respectively. For each scene, we collect 20 multi-illumination images by a Canon 5D Mark III camera. |
| Dataset Splits | No | In practice, we only need K 12 side lighting images for initialization. This process is terminated by an ALR goodness g that measures the recurrence accuracy by the overlap ratio of Cref and Ct, If g is good enough, e.g., g > 0.99, the ALR process stops. |
| Hardware Specification | No | We use a small LED bulb as the near point light source to carry out the experiments. We collect 20 multi-illumination images by a Canon 5D Mark III camera. |
| Software Dependencies | No | To compare with our ALR method, we use PTM [Tom et al., 2001], HSH [Elhabian et al., 2011], LDR [Favaro and Papadhimitri, 2012] as our baselines. Fine-grained changes are detected by FGCD algorithm [Feng et al., 2015] for both ALR and PTM results. |
| Experiment Setup | Yes | In our experiments, we empirically set rλ 0 = θλ 0 = φλ 0 = 3mm in our robotic platform. µ is the speed-up rate of navigation magnitude and is empirically set as 1.2 in our experiments. |