Efficient Unsupervised Image Stitching Using Attention Mechanism with Deep Homography Estimation

Traditional feature-based image stitching techniques often encounter obstacles when dealing with images lackingunique attributes or suffering from quality degradation. The scarcity of annotated datasets in real-life scenesseverely undermines the reliability of supervised learning methods in image stitching. Furthermore, existing deeplearning architectures designed for image stitching are often too bulky to be deployed on mobile and peripheralcomputing devices. To address these challenges, this study proposes a novel unsupervised image stitching methodbased on the YOLOv8 (You Only Look Once version 8) framework that introduces deep homography networksand attentionmechanisms. Themethodology is partitioned into three distinct stages. The initial stage combines theattention mechanism with a pooling pyramid model to enhance the detection and recognition of compact objectsin images, the task of the deep homography networks module is to estimate the global homography of the inputimages consideringmultiple viewpoints. The second stage involves preliminary stitching of the masks generated inthe initial stage and further enhancement through weighted computation to eliminate common stitching artifacts.The final stage is characterized by adaptive reconstruction and careful refinement of the initial stitching results.Comprehensive experiments acrossmultiple datasets are executed tometiculously assess the proposed model. Ourmethod’s Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) improved by 10.6%and 6%. These experimental results confirm the efficacy and utility of the presented model in this paper.

A Gridless Router Based on Hierarchical PB Corner Stitching Structure

A multi layer gridless area router is reported.Based on corner stitching,this router adopts tile expansion to explore path for each net.A heuristic method that penalizes nodes deviating from the destination is devised to accelerate the algorithm.Besides,an enhanced interval tree is used to manage the intermediate data structure.In order to improve the completion rate of routing,a new gridless rip up and rerouting algorithm is proposed.The experimental results indicate that the completion rate is improved after the rip up and reroute process and the speed of this algorithm is satisfactory.

Computer aided stitching approach for dental restoration models

According to the bio-characteristics of the lower and upper cavity surfaces of dental restoration, a stitching approach is proposed based on a virtual zipper working mechanism and a minimization of the surface total curvature energy, which is used to resolve the stitching problems existing during computer-aided design for dental restorations. First, the two boundaries corresponding to the lower and upper surfaces are triangulated based on the zipper working mechanism to generate the initial stitching surface patch, of which the edges are distributed uniformly between the boundaries. Secondly, the initial stitching surface patch is subdivided and deformed to reconstruct an optimized surface patch according to the bio-characteristics of the teeth. The optimized surface patch is minimally distinguishable from the surrounding mesh in smoothness and density, and it can stitch the upper and lower cavity surfaces naturally. The experimental results show that the dental restorations obtained by the proposed method can satisfy both the shape aesthetic and the fitting accuracy, and meet the requirements of clinical oral medicine.

A morphology-stitching method to improve Landsat SLC-off images with stripes

Stripes are artifacts in satellite images caused by various factors such as hardware defects. In some cases, these artifacts are introduced by some mitigating algorithms like Landsat SLC-off （Scan Line Corrector） gap-filling methods of LLHM （Local Linear Histogram Matching） and AWLHM （Adaptive Window Linear Histogram Matching）, which leave stripes as a byproduct. To improve Landsat SLC-off images with stripes,we propose an algorithm involving some hypothetical stripe-crossing stitch lines using the mean pixel value of the stitch lines.

A survey on image and video stitching

Image/video stitching is a technology for solving the field of view(FOV)limitation of images/videos.It stitches multiple overlapping images/videos to generate a wide-FOV image/video,and has been used in various fields such as sports broadcasting,video surveillance,street view,and entertainment.This survey reviews image/video stitching algorithms,with a particular focus on those developed in recent years.Image stitching first calculates the corresponding relationships between multiple overlapping images,deforms and aligns the matched images,and then blends the aligned images to generate a wide-FOV image.A seamless method is always adopted to eliminate such potential flaws as ghosting and blurring caused by parallax or objects moving across the overlapping regions.Video stitching is the further extension of image stitching.It usually stitches selected frames of original videos to generate a stitching template by performing image stitching algorithms,and the subsequent frames can then be stitched according to the template.Video stitching is more complicated with moving objects or violent camera movement,because these factors introduce jitter,shakiness,ghosting,and blurring.Foreground detection technique is usually combined into stitching to eliminate ghosting and blurring,while video stabilization algorithms are adopted to solve the jitter and shakiness.This paper further discusses panoramic stitching as a special-extension of image/video stitching.Panoramic stitching is currently the most widely used application in stitching.This survey reviews the latest image/video stitching methods,and introduces the fundamental principles/advantages/weaknesses of image/video stitching algorithms.Image/video stitching faces long-term challenges such as wide baseline,large parallax,and low-texture problem in the overlapping region.New technologies may present new opportunities to address these issues,such as deep learning-based semantic correspondence,and 3D image stitching.Finally,this survey discusses the challenges of image/video stitching and proposes potential solutions.

Efficient Image Stitching in the Presence of Dynamic Objects and Structure Misalignment

This paper presents a new method for simultaneously eliminating visual artifacts caused by moving objects and structure misalignment in image stitching. Given that the input images are roughly aligned, our approach is implemented in two stages. In the first stage, we discover motions between input images, and then extract their corresponding regions through a multi-seed based region growing algorithm. In the second stage, with prior information provided by the extracted regions, we perform a graph cut optimization in gradient-domain to determine which pixels to use from each image to achieve seamless stitching. Our method is simple to implement and effective. The experimental results illustrate that the proposed approach can produce comparable or superior results in comparison with state-of-the-art methods.

Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

At present,underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system.However,the processed underwater terrain images have inconspicuous and few feature points.In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed,we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block.First,the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information.The accelerated-KAZE(AKAZE)algorithm is used to combine the feature block information to match the reference image and the target image.Then,the random sample consensus(RANSAC)is applied to optimize the matching results.Finally,image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion.The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information,which not only solves the problem of stitching failure of underwater terrain images due to unobvious features,and further reduces the sensitivity to noise,but also effectively reduces the iterative calculation in the feature point matching process of the traditional method,and improves the stitching speed.Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.

DYNAMICS OF THE LOOPER DRIVE MECHANISM ON A BLIND STITCHING MACHINE

The looper drive mechanism is a main moving part in the blind stitching machine, which is aspatial 5 bar RRRSR linkage. In this paper, a dynamic analysis of the looper drive mechanism is made by means of the ma-trix method. Two methods are adopted in the calculation of the shaking force and shaking moment, one isdone by the constraint reaction of the flame-connected kinematic parts; the other is the inertialforces of all moving links.

Research on Stitching Algorithm Based on Tree Branch Image

Branch identification technology is a key technology to achieve automated pruning of fruit tree branches, and one of its technical bottlenecks lies in the stitching of branch images. To this end, we propose a set of branch image stitching technology algorithms. The algorithm is based on the grey-scale prime centroid method to determine the detection feature points, and uses the coordinate transformation matrix H of the corresponding points of the image to carry out the image geometric transformation, and realises the feature matching through sample comparison and classification methods. The experimental results show that the matched point images are more correct and less time-consuming.

Sparse Reconstructive Evidential Clustering for Multi-View Data

Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.

Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention dueto its outstanding performance and nonlinear application. However, most existing methods neglect that viewprivatemeaningless information or noise may interfere with the learning of self-expression, which may lead to thedegeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistencyand Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple viewsand fuses them based on their discrimination, so that it can effectively explore consistent and complementaryinformation for achieving precise clustering. Specifically, the view-specific self-expression is learned by a selfexpressionlayer embedded into the auto-encoder network for each view. To guarantee consistency across views andreduce the effect of view-private information or noise, we align all the view-specific self-expressions by contrastivelearning. The aligned self-expressions are assigned adaptive weights by channel attention mechanism according totheir discrimination. Then they are fused by convolution kernel to obtain consensus self-expression withmaximumcomplementarity ofmultiple views. Extensive experimental results on four benchmark datasets and one large-scaledataset of the CCAC method outperformother state-of-the-artmethods, demonstrating its clustering effectiveness.

Multi-View Structured Light 3D Measurement System

Vision-based measurement technology benefits high-quality manufacturers through improved dimensional precision,enhanced geo-metric tolerance,and increased product yield.The monocular 3D structured light visual sensing method is popular for detecting online parts since it can reach micron-meter depth accuracy.However,the line-of-sight requirement of a single viewpoint vision system often fails when hiding occurs due to the object’s surface structure,such as edges,slopes,and holes.To address this issue,a multi-view 3D structured light vi-sion system is proposed in this paper to achieve high accuracy,i.e.,Z-direction repeatability,and reduce hiding probability during mechani-cal dimension measurement.The main contribution of this paper includes the use of industrial cameras with high resolution and high frame rates to achieve high-precision 3D reconstruction.Moreover,a multi-wavelength(heterodyne)phase expansion method is employed for high-precision phase calculation.By leveraging multiple industrial cameras,the system overcomes field of view occlusions,thereby broadening the 3D reconstruction field of view.Finally,the system achieves a Z-axis repetition accuracy of 0.48µm.

Predicting the Stitch Density of Finished Fabrics Using Weft Blended Grey Knit Fabrics

Stitch density is one of the critical quality parameters of knit fabrics. This parameter is closely related to other physical quality parameters like fabric weight, fabric tightness factor, fiber types, blend ratio, yarn diameter and linear density, and fabric structure. Selecting stitch density (wales per inch, course per inch) is essential to getting the appropriate fabric weight and desired quality. Usually, no rules or assumptions exist to get the desired stitch density in the finished fabric stage. Fifteen types of blended knit fabrics were prepared to conduct the study. The varying percentages of cotton, polyester, and elastane are incorporated in the blends. Regression analysis and regression ANOVA tests were done to predict the stitch density of finished fabrics. A suitable regression equation is established to get the desired results. The study also found that the stitch density value in the finished stage fabric decreases by approximately 15% compared to the stitch density in the grey fabric stage. This study will help the fabric manufacturers get the finished fabric stitch density in advance by utilizing the grey fabric stitch density data set. The author expects this research to benefit the knitting and dyeing industry, new researchers, and advanced researchers.

Low-Rank Multi-View Subspace Clustering Based on Sparse Regularization

Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.

践踏处理对不同植丝深度混合草坪根系生长和运动质量的影响

植丝混合草坪建植技术涉及植丝密度和深度等参数。不同的植丝密度和深度在促进草坪草生长、提升草坪质量的有效性方面存在差异。本试验以纯天然草坪(CK)为对照,设置5种植丝深度处理(6 cm,9 cm,12 cm,15 cm,18 cm),研究践踏对不同植丝深度混合草坪坪观质量、根系生长和运动质量的影响。结果表明:各植丝深度混合草坪在践踏处理后均能减缓反弹率和表面硬度的增大;植丝深度在9 cm及以上时,根系总根长、根表面积都高于对照,6 cm植丝深度混合草坪根系总体积显著低于对照。18 cm植丝草坪根的平均直径提升。本研究表明9 cm以上植丝式混合草坪均可一定程度提高根系形态参数,因而能提升践踏后足球场草坪质量和运动性能,其中,18 cm深度植丝效果最好,不建议采取6 cm植丝深度进行植丝混合草坪建植。

Hourglass-GCN for 3D Human Pose Estimation Using Skeleton Structure and View Correlation

Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.

UMTransNet:结合U-Net和多尺度感知Transformer的图像拼接定位方法

当前基于深度学习的图像拼接定位方法大多只关注深层次特征,且感受野有限,忽略了浅层次特征,影响图像拼接定位的准确性。针对上述问题,文中提出一种结合改进U-Net和多尺度多视角Transformer的图像拼接定位网络UMTransNet。改进U-Net模型的编码器,将编码器中的最大池化层替换成卷积层,防止浅层次特征的流失;将多尺度多视角Transformer嵌入到U-Net的跳跃连接中,Transformer的输出特征与U-Net的上采样特征进行有效融合,实现深层次特征与浅层次特征的平衡,从而提高图像拼接定位的准确性。通过可视化检测结果图显示,所提方法在定位拼接篡改区域方面表现得更加出色。

智能座舱域控制器FPD⁃LinkⅢ检测系统设计

随着汽车电子技术的进步,FPD-LinkⅢ作为高速串行视频传输接口,正广泛应用于智能座舱系统。为对其进行检测,提出一套基于DS90UB948解串芯片的FPD-LinkⅢ视频处理方案。系统采用ZYNQ作为处理核心,对解串芯片输出的并行信号进行图像压缩拼接和输出,输出的拼接视频流用于实时显示。采集的单路图像用于质量检测,通过图像相似度算法进行定量分析。实验结果表明:系统可正确接收、处理三路FPD-LinkⅢ视频流,拼接图像连贯流畅,采集图像清晰;并且SSIM图像相似度算法可准确定位异常通路,满足了FPD-LinkⅢ的质量验证要求,为构建高阶智能驾驶仿真测试平台的图像采集与传输提供了技术支持与保障。

Stitch welding of Ti-6Al-4V titanium alloy by fiber laser

Stitch welding of plate covered skeleton structure of Ti-6Al-4V titanium alloys has a variety of applications in aerospace vehicle manufacture. The laser stitch welding of Ti-6Al-4V titanium alloys was carried out by a 4 kW ROFIN fiber laser. Influences of laser welding parameters on the macroscopic geometry, porosity, microstructure and mechanical properties of the stitch welded seams were investigated by digital microscope, optical microscope, scanning electron microscope and universal tensile testing machine. The results showed that the three-pipe nozzle with gas flow rate larger than 5 L/min could avoid oxidization, presenting better shielding effect in comparison with the single-pipe nozzle. Porosity formation could be suppressed with the gap between plate and skeleton less than 0.1 mm, while the existing porosity can be reduced with remelting. The maximum shear strength of stitch welding joint with minimal porosity was obtained by employing laser power of 1700 W, welding speed of 1.5 m/min and defocusing distance of ＋8 ram.

辐射制冷降温针织面料的制备及性能研究

具有日间被动辐射冷却的纺织品可以减轻高温天气对人体的伤害。文章以腈纶和蚕丝两种原料为基材,以聚乙烯醇(PVA)、无机纳米粒子二氧化硅(SiO_(2))和三氧化二铝(Al_(2)O_(3))的混合分散液为整理剂对纱线进行处理,利用手摇横机制备了两种罗纹组织的针织面料,再对织物进行盐酸刻蚀造孔处理,得到了具有高效辐射制冷效果的降温针织面料。研究表明,织物表面及纤维间被填料粒子覆盖,经盐酸刻蚀后出现均匀微孔;盐酸刻蚀得到的微孔能够明显提高织物对太阳光的反射率;与原织物相比,盐酸刻蚀后的织物在大气窗口的平均红外发射率约为87.9%,太阳反射率为75.0%,在辐照强度为501 mW/cm^(2)时,与对照织物相比可降低温度约12℃,具有较大的应用潜力。