SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution

Video Super-Resolution (VSR) aims to recover sequences of high-resolution (HR) frames from low-resolution (LR) frames. Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames. However, in the real world, there is a lot of irrelevant information in adjacent frames of videos with fast scene switching, these VSR methods cannot adaptively distinguish and select useful information. In contrast, with a transformer structure suitable for temporal tasks, we devise a novel adaptive scenario video super-resolution method. Specifically, we use optical flow to label the patches in each video frame, only calculate the attention of patches with the same label. Then select the most relevant label among them to supplement the spatial-temporal information of the target frame. This design can directly make the supplementary information come from the same scene as much as possible. We further propose a cross-scale feature aggregation module to better handle the scale variation problem. Compared with other video super-resolution methods, our method not only achieves significant performance gains on single-scene videos but also has better robustness on cross-scene datasets

Segmentation Method of Magnetic Tile Surface Defects Based on Deep Learning

Magnet tile is an essential part of various industrial motors, and its performance significantly affects the use of the motor. Various defects such as blowholes, break, cracks, fray, uneven, etc., may appear on the surface of the magnet tile. At present, most of these defects rely on manual visual inspection. To solve the problems of slow speed and low accuracy of segmentation of different defects on the magnetic tile surface, in this paper, we propose a segmentation method of the weighted YOLACT model. The proposed model uses the resnet101 network as the backbone, obtains multi-scale features through the weighted feature pyramid network, and performs two parallel subtasks simultaneously: generating a set of prototype masks and predicting the mask coefficients of each target. In the prediction mask coefficient branch, the residual structure and weights are introduced. Then, masks are generated by linearly combining the prototypes and the mask coefficients to complete the final target segmentation. The experimental results show that the proposed method achieves 43.44/53.44 mask and box mAP on the magnetic tile surface defect dataset, and the segmentation speed reaches 24.40 fps, achieving good segmentation results

Geometric Information and Rational Parametrization of Nonsingular Cubic Blending Surfaces

The techniques for parametrizing nonsingular cubic surfaces have shown to be of great interest in recent years. This paper is devoted to the rational parametrization of nonsingular cubic blending surfaces. We claim that these nonsingular cubic blending surfaces can be parametrized using the symbolic computation due to their excellent geometric properties. Especially for the specific forms of these surfaces, we conclude that they must be 3, 4, or 5 surfaces, and a criterion is given for deciding their surface types. Besides, using the algorithm proposed by Berry and Patterson in 2001, we obtain the uniform rational parametric representation of these specific forms. It should be emphasized that our results in this paper are invariant under any nonsingular real projective transform. Two explicit examples are presented at the end of this paper

An Improved Local Descriptor based Object Recognition in Cluttered 3D Point Clouds

Object recognition in three-dimensional point clouds is a new research topic in the field of computer vision. Numerous nuisances, such as noise, a varying density, and occlusion greatly increase the difficulty of 3D object recognition. An improved local feature descriptor is proposed to address these problems in this paper. At each feature point, a local reference frame is established by calculating a scatter matrix based on the geometric center and the weighted point-cloud density of its neighborhood, and an improved normal vector estimation method is used to generate a new signature of histograms of orientations (SHOT) local-feature descriptor. The geometric consistency and iterative closest point method realize 3D model recognition in the point-cloud scenes. The experimental results show that the proposed SHOT feature-extraction algorithm has high robustness and descriptiveness in the object recognition of 3D local descriptors in cluttered point-cloud scenes

A Family of Modified Even Order Bernoulli-Type Multiquadric Quasi-Interpolants with Any Degree Polynomial Reproduction Property

By using the polynomial expansion in the even order Bernoulli polynomials and using the linear combinations of the shifts of the function f(x)(x∈ℝ) to approximate the derivatives of f(x), we propose a family of modified even order Bernoulli-type multiquadric quasi-interpolants which do not require the derivatives of the function approximated at each node and can satisfy any degree polynomial reproduction property. Error estimate indicates that our operators could provide the desired precision by choosing a suitable shape-preserving parameter c and a nonnegative integer m. Numerical comparisons show that this technique provides a higher degree of accuracy. Finally, applying our operators to the fitting of discrete solutions of initial value problems, we find that our method has smaller errors than the Runge-Kutta method of order 4 and Wang et al.’s quasi-interpolation scheme