Active Recurrence of Lighting Condition for Fine-Grained Change Detection
Authors: Qian Zhang, Wei Feng, Liang Wan, Fei-Peng Tian, Ping Tan
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |