Nonrigid Points Alignment with Soft-weighted Selection
Authors: Xuelong Li, Jian Yang, Qi Wang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the proposed method can achieve a better result in both registration accuracy and correct matches compared to state-of-the-art approaches. In this section, we assess our approach by comparing with several methods on different datasets, i.e., the synthesized 2D shapes dataset [Zheng and Doermann, 2006], the benchmark IMM Face Databaset1 and the Oxford dataset2. |
| Researcher Affiliation | Academia | 1School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xian 710072, Shaanxi, P. R. China 2Unmanned System Research Institute (USRI), Northwestern Polytechnical University, Xian 710072, Shaanxi, P. R. China |
| Pseudocode | Yes | Algorithm 1 Self-selected point matching |
| Open Source Code | No | The paper does not provide a direct link to a code repository or explicitly state that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | IMM Face Databaset1 and the Oxford dataset2. ... 1Available at: http://www.imm.dtu.dk/ aam/datasets/datasets.html 2Available at: http://www.robots.ox.ac.uk/vgg/data/dataaff.html |
| Dataset Splits | No | The paper mentions using different datasets for evaluation but does not provide specific details on how these datasets were split into training, validation, and test sets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | All methods are implemented in Matlab, and tested on the same environment. |
| Experiment Setup | Yes | Parameter Setting In the experiments, a deterministic annealing technique strategy is applied on the scale parameter σ2 to improve the algorithm convergence. More specially, give a large initial value of σ2, and reduce them with a fixed annealing rate ν by σ = νσ. We empirically set σ = 2, and ν = 0.93 throughout the whole experiments. The parameter β identifies the range width of the interaction between points. Since the points have been normalized to 0 mean and unit variance, β would be similar to different samples, and we set β = 0.2 in the experiments. The parameter of regularization term includes λ which works for trade-off the smoothness, and λ is affected by the degree of data degradation, which is set by λ = 0.1. Note that parameter η reflects the effect of feature descriptor on the model. So, it can be set as 1 for the same kind of data. Besides, we initialize Cs as zero vector. Whereas the matching error is large at the beginning of iteration, the tolerance of the model to the error would be large for selecting more points. So, we dynamically update ζ, ς that decide the tolerance of the model to the error. As analyzed in Fig.1, we give a large initial value of ζ, ς and reduce them with a fixed rate µ. Since the optimization would be terminated, we choose a lower bound of ζ and set ζfinal = 1.0, and also set a lower bound ςfinal = 1.2 to control the weight of confused points. ς is fixed when ς ςfinal. Low-rank kernel matrix approximation is applied to reduce the computational cost. Parameter n0 is the number of the selected eigenvalues, and as a trade-off, it controls the balance between runtime and matching accuracy. The proposed method is best for n0 [15, 20], where eigenvector P and eigenvalue are calculated by the fast Gauss transform (FGT) and its experimental analysis is shown in Fig.3. From it, we set n0 = 15 in the experiments. |