Guobao Xiao

Superpixel-Based Two-View Deterministic Fitting for Multiple-Structure Data

This paper proposes a two-view deterministic geometric model fitting method, termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure data. SDF starts from superpixel segmentation, which effectively captures prior information of feature appearances. The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods. SDF also includes two original elements, i.e., a deterministic sampling algorithm and a novel model selection algorithm. The two algorithms are tightly coupled to boost the performance of SDF in both speed and accuracy. Specifically, the proposed sampling algorithm leverages the grouping cues of superpixels to generate reliable and consistent hypotheses. The proposed model selection algorithm further makes use of desirable properties of the generated hypotheses, to improve the conventional fit-and-remove framework for more efficient and effective performance. The key characteristic of SDF is that it can efficiently and deterministically estimate the parameters of model instances in multi-structure data. Experimental results demonstrate that the proposed SDF shows superiority over several state-of-the-art fitting methods for real images with single-structure and multiple-structure data.

Efficient guided hypothesis generation for multi-structure epipolar geometry estimation

A sampling method EGHG is proposed for multi-structure geometry estimation.EGHG combines the benefits of a global and a local sampling strategy.The global sampling strategy is designed to rapidly obtain promising solutions.The local sampling strategy is designed to efficiently achieve accurate solutions.Experimental results show the effectiveness of EGHG on public real image pairs. We propose an Efficient Guided Hypothesis Generation (EGHG) method for multi-structure epipolar geometry estimation. Based on the Markov Chain Monte Carlo process, EGHG combines two guided sampling strategies: a global sampling strategy and a local sampling strategy. The global sampling strategy, guided by using both spatial sampling probabilities and keypoint matching scores, rapidly obtains promising solutions. The spatial sampling probabilities are computed by using a normalized exponential loss function. The local sampling strategy, guided by using both Joint Feature Distributions (JFDs) and keypoint matching scores, efficiently achieves accurate solutions. In the local sampling strategy, EGHG updates a set of current best hypothesis candidates on the fly, and then computes JFDs between the input data and these candidates. Experimental results on public real image pairs show that EGHG significantly outperforms several state-of-the-art sampling methods on multi-structure data.

Rapid hypothesis generation by combining residual sorting with local constraints

Efficient hypothesis generation plays an important role in robust model fitting. In this study, based on the combination of residual sorting and local constraints, we propose an efficient guided hypothesis generation method, called Rapid Hypothesis Generation (RHG). By exploiting the local constraints to guide the hypothesis generation process, RHG raises the probability of generating promising hypotheses and reduces the computational cost during hypotheses generation. Experimental results on homography and fundamental matrix estimation show that RHG can effectively guide hypothesis generation process and rapidly generate promising hypotheses for heavily contaminated multi-structure data.

Combining preference analysis with local constraints for rapid hypothesis generation

Hypothesis generation is crucial to many robust model fitting methods. In this paper, we propose an effective hypothesis generation method by adopting conditional sampling with local constraints. We choose data to generate hypotheses according to sampling weights, which are computed according to ordered residual indices. To sample a minimal subset, we randomly choose a seed datum, compute sampling weights of all data with regard to the seed datum, search the neighborhood set of the seed datum by using the sampling weights, and then sample the remaining data of the minimal subset from the neighborhood set. It has two advantages to consider the neighboring information in guided sampling: It raises the probability of generating all-inlier minimal subsets and it reduces the computational loads in hypotheses generation. The proposed method shows good performance in fundamental matrix estimation using real image pairs.

Robust procedural model fitting with a new geometric similarity estimator

Abstract Procedural model fitting (PMF) is a generalization of classical model fitting and has numerous applications for computer vision and computer graphics. The task of PMF is to search a geometric model set for the model that is most similar to a set of data points. We propose a strict and robust similarity estimator for PMF to handle imperfect data. The proposed estimator is based on the error from model to data, while most other estimators are based on the error from data to model. We then use the proposed estimator to guide the cuckoo search algorithm to search for the most similar model. To accelerate the search process, we also propose a coarse-to-fine model dividing strategy to early reject dissimilar models. In this paper, the proposed PMF method is applied to fit building models on laser scanning data. It is also applied to fit character models on eighteen variants of imperfect MNIST data to achieve few-shot pattern recognition. In the 5-shot recognition, our method outperforms the state-of-the-art method on thirteen variants of the imperfect data. In particular, for one of the data corrupted by grid lines, our method obtains a high accuracy of 65%, whereas the state-of-the-art method only obtains an accuracy of 30%.

Conceptual space based model fitting for multi-structure data

Abstract In this paper, we propose a novel fitting method, called the Conceptual Space based Model Fitting (CSMF), to fit and segment multi-structure data contaminated with a large number of outliers. CSMF includes two main parts: an outlier removal algorithm and a model selection algorithm. Specifically, we firstly construct a novel conceptual space to measure data points by only considering the good model hypotheses. Then we analyze the conceptual space to effectively remove the gross outliers. Based on the results of outlier removal, we propose to search center points (representing the estimated model instances) in the conceptual space for model selection. CSMF is able to efficiently and effectively remove gross outliers in data, and simultaneously estimate the number and the parameters of model instances without using prior information. Experimental results on both synthetic data and real images demonstrate the advantages of the proposed method over several state-of-the-art fitting methods.

Superpixel-Guided Two-View Deterministic Geometric Model Fitting

Geometric model fitting is a fundamental research topic in computer vision and it aims to fit and segment multiple-structure data. In this paper, we propose a novel superpixel-guided two-view geometric model fitting method (called SDF), which can obtain reliable and consistent results for real images. Specifically, SDF includes three main parts: a deterministic sampling algorithm, a model hypothesis updating strategy and a novel model selection algorithm. The proposed deterministic sampling algorithm generates a set of initial model hypotheses according to the prior information of superpixels. Then the proposed updating strategy further improves the quality of model hypotheses. After that, by analyzing the properties of the updated model hypotheses, the proposed model selection algorithm extends the conventional “fit-and-remove” framework to estimate model instances in multiple-structure data. The three parts are tightly coupled to boost the performance of SDF in both speed and accuracy, and SDF has the deterministic nature. Experimental results show that the proposed SDF has significant advantages over several state-of-the-art fitting methods when it is applied to real images with single-structure and multiple-structure data.

A Hierarchical Voting Scheme for Robust Geometric Model Fitting

In this paper, we propose an efficient and robust model fitting method, called Hierarchical Voting scheme based Fitting (HVF), to deal with multiple-structure data. HVF starts from a hierarchical voting scheme, which simultaneously analyses the consensus information of data points and the preference information of model hypotheses. Based on the proposed hierarchical voting scheme, HVF effectively removes “bad” model hypotheses and outliers to improve the efficiency and accuracy of fitting results. Then, HVF introduces a continuous relaxation based clustering algorithm to fit and segment multiple-structure data. The proposed HVF can effectively estimate model instances from the model hypotheses generated by random sampling, which usually includes a large proportion of “bad” model hypotheses. Experimental results show that the proposed HVF method has significant superiority over several state-of-the-art fitting methods on both synthetic data and real images.

Induced subgraph searching for geometric model fitting

In this paper, we propose a novel model fitting method based on graphs to fit and segment multiple-structure data. In the graph constructed on data, each model instance is represented as an induced subgraph. Following the idea of pursuing the maximum consensus, the multiple geometric model fitting problem is formulated as searching for a set of induced subgraphs including the maximum union set of vertices. After the generation and refinement of the induced subgraphs that represent the model hypotheses, the searching process is conducted on the “qualified” subgraphs. Multiple model instances can be simultaneously estimated by solving a converted problem. Then, we introduce the energy evaluation function to determine the number of model instances in data. The proposed method is able to effectively estimate the number and the parameters of model instances in data severely corrupted by outliers and noises. Experimental results on synthetic data and real images validate the favorable performance of the proposed method compared with several state-of-the-art fitting methods.

Evolution-based outlier removal for geometric model fitting

In this paper, we propose a novel method, called Evolution-based Outlier Removal (EOR) method, to remove outliers for robust geometric model fitting. We first select some data points and guide them to evolve towards the inliers. And then, we statistically analyze the evolutional results and distinguish inliers from outliers. Our main contribution in this paper is that, we develop a fitness function to improve the “quality” of selected point sets, which is then used to remove outliers. Experiments on real images illustrate the superiority of the proposed method over several state-of-the-art outlier removal methods.