Point Cloud Vibration Compensation Algorithm Based on an Improved Gaussian–Laplacian Filter

Although many mesh generation methods have been developed, difficulties still exist in generating high-quality structured hex-meshes for specific local geometric features such as holes and columns in three-dimensional models. In this paper, a mesh generation approach is proposed to overcome the problem of meshing local features having regular structure with high quality. Firstly, analyze the feature's geometry information and customize its mesh process. Secondly, introduce parametric design into the optimization of mesh customization. Scaled Jacobian and aspect ratio are taken as indexes of the objective functions. Thirdly, an optimized module of the feature is set up by integrating the meshing scheme into program. By establishing a knowledge base of optimized modules corresponding to geometric features, a local feature can be automatically meshed according to its geometry information. Experiments in this paper reveal that the method is applicable and stable for generating high-quality meshes for local geometric features with regular structure.

Automatic object detection and tracking has been widely applied in the video surveillance systems for homeland security and data fusion in the remote sensing and airborne imagery. The typical applications include human motion analysis and the vehicle detection. Here we implement object detection and tracking under shape graphs of interesting objects integrating local contextual information (corner/point features, etc) of the objects. On the top layer, shapes/sketches provide a discrimination measure to describe the global status of the interesting objects. This kind of information is very useful to improve the object tracking performance for occlusion. The shape can be modeled as a graph or hyper graph through its local geometric features. On the bottom layer, local geometric features are used to capture local properties of objects and perform correspondence estimation of high-level shapes. The local features provide a way to conquer inaccurate object segmentation and extraction. The experiments were implemented on human face tracking and vehicle detection and tracking.

Medical image segmentation is a fundamental task in medical image analysis, playing a crucial role in disease diagnosis, treatment planning, and clinical decision-making. Accurate segmentation of anatomical structures, such as blood vessels, organs, and lesions, is essential for reliable medical interpretations. However, existing segmentation models often face challenges in effectively capturing both local and global features within medical images. Traditional convolutional neural networks (CNNs) excel at extracting fine-grained local features but struggle to model long-range dependencies due to their limited receptive fields. Conversely, transformer-based models can capture global contextual relationships but often fail to preserve detailed local structures, leading to suboptimal segmentation performance. The purpose of this study is to develop SEFormer, a novel hybrid network that integrates SENet, ResNet, and Transformer to enhance medical image segmentation. SEFormer aims to effectively capture both local and global features, addressing the limitations of traditional CNNs and transformer-based models.SENet is incorporated to recalibrate feature maps by adaptively emphasizing informative features, thereby compensating for the global representation limitations of CNNs. Meanwhile, the ResNet backbone ensures deep feature extraction while maintaining computational efficiency. Additionally, the Transformer module is introduced to capture long-range dependencies and enhance contextual awareness, complementing the local feature extraction capabilities of CNNs. This hybrid approach allows SEFormer to balance fine-grained spatial details with broader contextual information, leading to more precise segmentation results.Furthermore, to mitigate information loss during feature extraction and ensure a more complete and hierarchical representation of image information, we introduce a feature pyramid structure inspired by multi-scale image pyramid models. By progressively increasing the receptive field across different scales and employing skip connections, SEFormer effectively fuses multi-scale local and global features at each stage of the pyramid. This hierarchical fusion mechanism ensures a richer and more robust feature representation, which is particularly beneficial for segmenting complex medical structures such as blood vessels. We evaluate SEFormer on the CHASE_DB1 dataset, a widely used benchmark for retinal vessel segmentation. Experimental results demonstrate that SEFormer outperforms existing state-of-the-art segmentation methods, achieving a 3.25% improvement in segmentation accuracy. Additionally, we conduct ablation studies to verify the contributions of different components within SEFormer. The results show that incorporating SENet enhances feature recalibration and channel attention, while the Transformer module significantly improves global context awareness. The feature pyramid structure further contributes to performance gains by ensuring a multi-scale representation of vascular structures. Compared to conventional CNN-based methods, our approach achieves better segmentation accuracy with improved robustness to variations in vessel thickness, noise, and image contrast. Furthermore, SEFormer maintains computational efficiency, making it suitable for real-world clinical applications where both accuracy and processing speed are critical. SEFormer provides an efficient and accurate approach for medical image segmentation by leveraging the complementary strengths of CNNs, SENet, and Transformers while integrating a feature pyramid structure to maximize feature representation. Future research directions include extending SEFormer to other medical imaging modalities, such as CT and MRI scans, as well as exploring further optimization techniques to reduce computational costs while maintaining high segmentation performance. Additionally, integrating SEFormer with active learning frameworks could further improve segmentation performance by leveraging expert-annotated data more efficiently.

Optical Character Recognition (OCR) research includes computer vision, artificial intelligence, and pattern recognition. Character recognition has garnered a lot of attention in the last decade due to its broad variety of uses and applications, including multiple-choice test data, business documents (e.g., ID cards, bank notes, passports, etc.), and automatic number plate recognition. This paper introduces an automatic recognition system for printed numerals. The automatic reading system is based on extracting local statistical and geometrical features from the text image. Those features are represented by eight vectors extracted from each digit. Two of these features are local statistical (A, A th), and six are local geometrical (P1, P2, P3, P4, P5, and P6). Thus, the database created consists of 1120 statistical and geometrical features. For the purpose of recognition, the features of the test image are compared with the features of all the images saved in the database depending on the value of the Minimum Distance (MD). All digits (0–9) were identified with 100% accuracy. The average computational time required to recognize a numeral at any font size is 0.06879 seconds.

Coarse pose registration is a crucial step in 6DOF pose estimation. Assuming the objects to be estimated are regular and their 3D models are known, this paper presents local geometric features (LGFs) to estimate the poses of objects in point cloud. Coordinate frames describing the object's pose are defined according to geometric information. Pose estimation are performed by calculating the transformation between the frames in the scene point cloud and the reference model. Two kinds of LGFs are proposed: one is the corner edge feature (CEF) which exploits corner and radiating edges, and the other is the circle normal feature (CNF) which exploits circle center and normal vector of the plane where the circle is located. Included angle sequence of adjacent edges and circle radius, which are invariant to SE (3) transformation, are selected to identify CEF and CNF, respectively. Simulation is performed to verify the feasibility of the proposed features.

As 3D acquisition technology develops and 3D sensors become increasingly affordable, large quantities of 3D point cloud data are emerging. How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved. The point cloud geometric information is hidden in disordered, unstructured points, making point cloud analysis a very challenging problem. To address this problem, we propose a novel network framework, called Tree Graph Network (TGNet), which can sample, group, and aggregate local geometric features. Specifically, we construct a Tree Graph by explicit rules, which consists of curves extending in all directions in point cloud feature space, and then aggregate the features of the graph through a cross-attention mechanism. In this way, we incorporate more point cloud geometric structure information into the representation of local geometric features, which makes our network perform better. Our model performs well on several basic point clouds processing tasks such as classification, segmentation, and normal estimation, demonstrating the effectiveness and superiority of our network. Furthermore, we provide ablation experiments and visualizations to better understand our network.

This paper presents a new approach to estimate accurate and robust 3D semantic correspondence with the hierarchical neural semantic representation. Our work has three key contributions. First, we design the hierarchical neural semantic representation (HNSR), which consists of a global semantic feature to capture high-level structure and multi-resolution local geometric features to preserve fine details, by carefully harnessing 3D priors from pre-trained 3D generative models. Second, we design a progressive global-to-local matching strategy, which establishes coarse semantic correspondence using the global semantic feature, then iteratively refines it with local geometric features, yielding accurate and semantically-consistent mappings. Third, our framework is training-free and broadly compatible with various pre-trained 3D generative backbones, demonstrating strong generalization across diverse shape categories. Our method also supports various applications, such as shape co-segmentation, keypoint matching, and texture transfer, and generalizes well to structurally diverse shapes, with promising results even in cross-category scenarios. Both qualitative and quantitative evaluations show that our method outperforms previous state-of-the-art techniques.

On shortened 3D local binary descriptors

PDF | Automated Surface Inspection systems have become standard tools for the manufacturing of steel products. They are used at various steps of the manufacturing process: starting at the hot-rolling mill to detect and remove defective material from the process, until finishing lines for quality control before shipment to the customer. The benefit of using automated surface inspection is two-fold as it allows monitoring both the quality of the product and the process. This results in value for the systems users through process improvements and increased productivity. SIAS, the Siemens VAI solution for surface inspection, has been developed to ensure that these two goals are met. The SIAS solution brings unequalled performance recognized by the most prestigious steel suppliers with unique functions. The paper presents the range of applications of SIAS and the benefits it brings in each case, as well as the tools that have been developed to pull the most out of the surface inspection system from the perspective of the maintenance, quality and production teams.

SADGFeat: Learning local features with layer spatial attention and domain generalization

Lane detection is a fundamental component of perception systems in autonomous driving. Despite significant progress in this area, existing methods still face challenges in complex scenarios such as abnormal weather, occlusions, and curved roads. These situations typically demand the integration of both the global semantic context and local visual features to predict the lane position and shape. This paper presents ORANet, an enhanced lane detection framework built upon the baseline CLRNet. ORANet incorporates two novel modules: Enhanced Coordinate Attention (EnCA) and Channel-Spatial Shuffle Attention (CSSA). EnCA models long-range lane structures while effectively capturing global semantic information, whereas CSSA strengthens the precise extraction of local features and provides optimized inputs for EnCA. These components operate in hierarchical synergy, collectively establishing a complete enhancement pathway from refined local feature extraction to efficient global feature fusion. The experimental results demonstrate that ORANet achieves greater performance stability than CLRNet in complex roadway scenarios. Notably, under shadow conditions, ORANet achieves an F1 score improvement of nearly 3% over CLRNet. These results highlight the potential of ORANet for reliable lane detection in real-world autonomous driving environments.

Local binary pattern (LBP) is an important image texture feature extraction method. This paper proposes a combination of adaptive threshold and directional local image feature representation, and to realize the image retrieval. This local combination feature is composed of two parts: 1)The standard deviation of the gray value of all pixels in the local neighborhood of the image and the gray value of the center pixel are used as the comparison threshold for encoding. This threshold is adaptively changed following the grayscale changes in the neighborhood, and can indicate the severity of the gray scale change; 2)directional local pattern: by comparing the grayscale changes of local neighborhood pixels in different directions, discrimination and binary encoding are performed to indicate the local texture features directionality. Finally, the two features are combined to form the local features of the image, and the features are used for image retrieval. Experiments on the Corel-1k database show that this method has a better image retrieval effect than existing local feature representation methods.

In this article, we study the rigidity properties of deformation parameters of the natural action of a discontinuous subgroup Γ⊂G, on a homogeneous space G/H, where H stands for a closed subgroup of a Euclidean motion group G:=On(R)⋉Rn. That is, we prove the following local (and global) rigidity theorem: the parameter space admits a rigid (equivalently a locally rigid) point if and only if Γ is finite. Remarkably, it turns out that H is compact whenever Γ is infinite, which makes accessible the study of the corresponding parameter and deformation spaces and their topological and local geometrical features. This shows that the Calabi–Markus phenomenon occurs in this setting. That is, if H is a closed noncompact subgroup of G, then G/H does not admit a compact Clifford–Klein form, unless G/H itself is compact. We also answer a question posed by T. Kobayashi. That is, no homogeneous space G/H admits a noncommutative free group as a discontinuous group.

Both ice and silica crystallize into solid-state structures composed of tetrahedral building units that are joined together to form an infinite four-connected net. Mathematical considerations suggest that there is a vast number of such nets and thus potential crystal structures. It is therefore perhaps surprising to discover that, despite the differences in the nature of interatomic interactions in these materials, a fair number of commonly observed ice and silica phases are based on common nets. Here we use computer simulation to investigate the origin of this symmetry between the structures formed for ice and silica and to attempt to understand why it is not complete. We start from a comparison of the dense phases and then move to the relationship between the different open (zeolitic and clathratic) structures formed for both materials. We show that there is a remarkably strong correlation between the energetics of isomorphic silica and water ice structures and that this correlation arises because of the strong link between the total energy of a material and its local geometric features. Finally, we discuss a number of as yet unsynthesized low-energy structures which include a phase of ice based on quartz, a silica based on the structure of ice VI, and an ice clathrate that is isomorphic to the silicate structure nonasil.

Due to the noise accompanied with fault signals, it is challenging to identify the discriminant information and the local geometric feature from the complex fault data for enhancing fault diagnosis accuracy. To address this challenge, this work proposed an anti-noise algorithm based on locally linear embedding integrated with diffusion distance and maximum correntropy criterion (DMLLE). In DMLLE, diffusion distance was adopted instead of the Euclidean distance for neighborhood construction. Meanwhile, the optimal weights were updated to reveal local geometry information based on the loss function of the maximum correntropy criterion. Subsequently, DMLLE is eventually developed to further restrain noise embedding into raw signals and obtain low dimensional features. Furthermore, weighted extreme gradient boosting is used to map the low dimensional features to the types of faults, which easily implements fault pattern recognition. Finally, two synthetic manifold datasets and fault data acquired from the transmit/receive (T/R) module are used to validate the performance of the proposed diagnosis methodology. Compared with the existing methods, the proposed diagnosis methodology generates a smoother flow structure by preserving the local neighborhood of the dataset with noises and realizes a higher accuracy of 94.41% on the T/R module dataset, which outperforms 3%–9% better than other classification models. Therefore, it can be concluded that the proposed diagnosis methodology can effectively extract intrinsic fault features by weakening the influence aroused by noise and achieve superior accuracy in fault diagnosis by addressing the problem of small sample size.