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Convolutional Neural Network (CNN) is commonly used for the Electroencephalogram (EEG) based motor-imagery (MI) decoding. However, its performance is generally limited due to the small size sample problem. An alternative way to address such issue is to segment EEG trials into small slices for data augmentation, but this approach usually inevitably loses the valuable long-range dependencies of temporal information in EEG signals. To this end, we propose a novel self-supervised learning (SSL) based channel attention MLP-Mixer network (S-CAMLP-Net) for MI decoding with EEG. Specifically, a new EEG slice prediction task is designed as the pretext task to capture the long-range information of EEG trials in the time domain. In the downstream task, a newly proposed MLP-Mixer is applied to the classification task for signals rather than for images. Moreover, in order to effectively learn the discriminative spatial representations in EEG slices, an attention mechanism is integrated into MLP-Mixer to adaptively estimate the importance of each EEG channel without any prior information. Thus, the proposed S-CAMLP-Net can effectively learn more long-range temporal information and global spatial features of EEG signals. Extensive experiments are conducted on the public MI-2 dataset and the BCI Competition IV Dataset 2A. The experimental results indicate that our proposed S-CAMLP-Net achieves superior classification performance over all the compared algorithms.

Metric plays a key role in the description of similarity between samples. An appropriate metric for data can well represent their distribution and further promote the performance of learning tasks. In this paper, to better describe the heterogeneous distributions of data, we propose a semi-supervised local-to-global metric learning framework from the geometric insight. Our contributions can be summarized as: Firstly, to enlarge the application scope of local metric learning, we introduce the unsupervised information as the regularization term into our smoothly glued nonlinear metric model. Secondly, we propose two different nonlinear semi-supervised metric learning models with two different loss terms, and find that the smooth loss performs better than the hinge loss by comparison results. Thirdly, we have established not only two metric learning models, but also a nonlinear metric learning framework based on local metrics, which includes supervised and semi-supervised as well as linear and nonlinear metric learning. Moreover, we present an intrinsic steepest descent algorithm on the positive definite manifold for implementation of our semi-supervised nonlinear metric learning models with smooth triplet constrain loss. Finally, we compare our approaches with several state-of-the-art methods on a variety of datasets. The results validate that the robustness and accuracy of classification are both improved under our metrics.

Partial registration for point clouds plays an important role in various fields such as 3D mapping reconstruction, remote sensing, unmanned driving, and cultural heritage protection. Unfortunately, partial registration is challenging due to difficulties such as the low overlap ratio of two point clouds and the perturbation in the orderless and sparse 3D point clouds. Thus, a variety of the 3D shape context descriptors are introduced for finding the optimal matching. However, extracting geometric features and descriptors are time consuming and easily degenerated by noise. To overcome these problems, we introduce a parallel coarse-to-fine partial registration method. Our contributions can be summarized as: Firstly, a robust coarse trimmed method is proposed to estimate the coarse overlap area and the initial transformation via fast bilateral denoising and parallel point feature histogram (PPFH) descriptor aligning. Secondly, an accelerated fine registration procedure is conducted by a parallel trimmed iterative closest point (PTrICP) method. Moreover, most parts of our coarse-to-fine workflow are accelerated under the Graphics Processing Unit (GPU) parallel execution mode for efficiency. Thirdly, we extend our method from the rigid registration to the isotropic scaling registration, which improves its applicability. Experiments have demonstrated that our method is feasible and robust in various situations, including the low overlap ratio, outlier, noise and scaling.

In this paper, we introduce a modified Generalized Iterative Closest Point (GICP) algorithm by presenting a coarse-to-fine strategy. Our contributions can be summarized as: Firstly, we use adaptively a plane-to-plane probabilistic matching model by gradually reducing the neighborhood range for given two point sets. It is an inner coarse-to-fine iteration process. Secondly, we use an outer coarse-to-fine strategy to bridge the point-to-point and plane-to-plane registration for refining the matching. Thirdly, we use the trimmed method to gradually eliminate the effects of incorrect correspondences, which improves the robustness of the methods especially for the low overlap cases. Moreover, we also extend our method to the scale registration case. Finally, we conduct extensive experiments to demonstrate that our method is more reliable and robust in various situations, including missing points, noise and different scale factors. Experimental results show that our approach outperforms several state-of-the-art registration methods.

The accuracy of classification and retrieval significantly depends on the metric used to compute the similarity between samples. For preserving the geometric structure, the symmetric positive definite (SPD) manifold is introduced into the metric learning problem. However, the SPD constraint is too strict to describe the real data distribution. In this paper, we extend the intrinsic metric learning problem to semi-definite case, by which the data distribution is better described for various classification tasks. First, we formulate the metric learning as a minimization problem to the SPD manifold on subspace, which not only considers to balance the information between inner classes and inter classes by an adaptive tradeoff parameter but also improves the robustness by the low-rank subspaces presentation. Thus, it benefits to design a structure-preserving algorithm on subspace by using the geodesic structure of the SPD subspace. To solve this model, we develop an iterative strategy to update the intrinsic metric and the subspace structure, respectively. Finally, we compare our proposed method with ten state-of-the-art methods on four data sets. The numerical results validate that our method can significantly improve the description of the data distribution, and hence, the performance of the image classification task.

In this paper, we are concerned with the registration of two 3D data sets with large-scale stretches and noises. First, by incorporating a scale factor into the standard iterative closest point (ICP) algorithm, we formulate the registration into a constraint optimization problem over a 7D nonlinear space. Then, we apply the singular value decomposition (SVD) approach to iteratively solving such optimization problem. Finally, we establish a new ICP algorithm, named Scale-ICP algorithm, for registration of the data sets with isotropic stretches. In order to achieve global convergence for the proposed algorithm, we propose a way to select the initial registrations. To demonstrate the performance and efficiency of the proposed algorithm, we give several comparative experiments between Scale-ICP algorithm and the standard ICP algorithm. Note to Practitioners--In this paper, we proposed the Scale-ICP algorithm, which is used to deal with the registration between two data sets with scale stretches. In practice, there are a large number of such problems. For example, the registration between the range data sets that have different scanning resolutions determined by the distances from the sensor to the object surfaces. The data sets can be not only image data but also the other measured data, and hence it also can be extended to machining industry besides the computer vision field. [ABSTRACT FROM AUTHOR]