Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that, in contrast to fingerprints, are robust under external environment effects such as dirt and paper cuts. Moreover, they form a complex rich shape that is unique, even in identical twins, and allows a high degree of freedom. However, most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality. Since the start of the COVID-19 pandemic, most hand-based biometric systems have become undesirable due to their possible impact on the spread of the pandemic. Consequently, new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness. One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle. This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years. For the other research contribution, a novel geometrical feature extraction method has been developed based on the Curvelet Transform. This method is useful for extracting robust rotation invariance features from vein images. The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.     

Research on image segmentation of Miao costume patterns based on the RSKP-UNet model北大核心CSCD

The Miao nationality is the sixth largest ethnic group in China which has a history of thousands of years. It has created a unique material culture and spiritual culture in its development process and the Miao costume is a highly condensed collection of the material and spiritual culture of the Miao nationality. As one of the unique symbols of Miao culture the Miao costume has profound cultural heritage and cultural connotations. The patterns of the Miao costume are particularly eye-catching as they not only symbolize the wisdom of the Miao people in thousands of years of production and life but also symbolize the pursuit of the good spirit of the Miao people. However under the impact of modern pop culture and foreign culture these cultural symbols are gradually disappearing. In order to protect and inherit them the Miao costume pattern segmentation has become the most important work. However the Miao costume pattern segmentation is quite difficult. At present there are few studies on the extraction classification identification and preservation of the features of Miao costume pattern segmentation. With the excellent segmentation performance of the U-Net model and the advantages of easy deployment the paper improves the basic structure of the U-Net model and proposes a Miao costume pattern segmentation algorithm based on the RSKP-UNet Residual Selective-Kernel Parallel U-Net model. The algorithm adds Residual modules in the encoder part of the U-Net model to improve the feature extraction capability of the model and embeds the SKNet modules and ParNet modules in the decoder part to enhance the feature expression capability of the model. The paper uses evaluation metrics to measure the segmentation performance of the model and compares it with four segmentation models based on deep learning. The paper not only combines the current research focus-deep learning and attention mechanism but also introduces the Lovász-hinge loss function to effectively solve the problem of class imbalance in the Miao costume patterns. The RSKP-UNet model is better than other models in various segmentation indicators. Compared with the benchmark model U-Net the Dice coefficient IoU precision recall and accuracy are improved by 6. 98% 11. 07% 2. 89% 6. 75% and 3. 92% . The segmentation algorithm proposed in this paper realizes the extraction of the element content of the Miao costume patterns through image segmentation of Miao costume patterns which can be used to build the Miao costume pattern database in this way thus helping designers relevant researchers and organizations to provide research foundation and completing the protection and inheritance of the Miao costume culture. The paper also provides some reference for the segmentation research of other minority costume patterns. © 2022 Authors. All rights reserved.     

A tutorial on the segmentation of metallographic images: Taxonomy,new MetalDAM dataset,deep learning-based ensemble model,experimental analysis and challenges

Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps.     

含裂纹型缺陷压力容器用钢失效评定曲线研究

在力学性能测试的基础上．应用CEGB R6-Revison 4方法中的选择1和选择3曲线，对16MnR，20R和Q235B三种吉裂纹型缺陷压力容器用钢的失效评定曲线进行研究。结果表明．三种材料的失效评定曲线基本上均在Lr〈0.4后因试样结构不同而发生分散。而且均以是以Lr=0.7为分界线，在两侧分别出现不同的最为保守和最不保守曲线。给出了符合三种材料特性的失效评定曲线拟合方程，以便为工程应用提供依据。     

影响线切割加工工件表面质量因素及改善措施

对在线切割加工中影响工件表面质量的机床结构、运丝机构、切割参数、工艺参数、电参数、工件材料、工作液等进行分析,并就相应的影响程度提出了相应措施和改善办法。     

利用阈值分割技术提取煤泥浮选泡沫图像的物理特征

探讨了煤泥浮选泡沫图像特征的提取方法，首先将RGB图像转化为灰度级图像，再采用直方图均衡化和局域中值滤波处理，以改善图像的对比度，利用阈值分割技术和二值化处理方法提取了气泡的个数，对亮点的面积，形状，周长进行了计算，求出了气泡的粒度分布，通过对51幅煤泥浮选泡沫图像的研究。发现该方法适用于亮点明显，气泡粒度大的图像，可以用来指导煤泥浮选的操作。     

Land cover mapping with patch-derived landscape indices

Automated-classification procedures of satellite imagery are mainly based on surface reflectance and generally ignore shape and size of landforms. On the other hand, quantitative landscape ecology has been focused on the patch concept as a landscape unit due to its relevance in the theory and practice of the conservation of species in human-modelled landscapes. The present paper explores how landscape metrics can introduce the component of spatial pattern of landscape elements to enhance land cover classification reliability. In particular, a method is proposed to extract patch-derived indices and to introduce them in a supervised classification of Landsat Thematic Mapper (TM) images as neo-channels. To extract patch-derived indices, an image segmentation method based on edge detection was used to define patches without an a priori knowledge of land cover classes. We calculated four patch indices: area, perimeter, shape index and fractal dimension. These indices were introduced in the classification to test the improvement of classification reliability. Well-known additional information (texture and topographic features) was also tested for comparison purposes. The method was tested for mapping land cover types in a mountainous region in the French Pyrenees, the Massif of Arize. The results of classification reliability allowed us to conclude that patch indices and topographic features significantly improved the discrimination of land cover classes. The combination of these additional information types by means of data fusion is useful for land cover classification purposes.     

石拱桥的有限弹簧法分析及其安全性评价

依据计算几何学的分割理论,开发了对石拱桥进行任意形状单元划分的计算机前处理系统,提出了用有限弹簧法模拟石拱桥力学性能的计算方法。其特点是能够把石拱桥的拱圈看成个别石材块体构成的不连续体,考虑拱圈随着偏心压缩而引起的石材块体间开口变形的同时,也考虑拱背填料和拱圈的相互作用及其它的荷载分散效果。数值模拟了多跨石拱桥在分布活荷载及其水平地震荷载作用下的力学性能,并进行了安全性评价。     

AutoCAD中圆弧折线化方法与实现

AutoCAD作为一种重要的地形图制图软件,图中存在大量的圆弧图元。由于圆弧只有端点或圆心坐标,弧线本身缺乏精确的坐标信息,因此导致一些数据检查时出错。Auto CAD软件本身不提供圆弧转折线的功能。本文尝试采用等角切分方法,利用VBA二次开发技术,在保持图形原有特征及精度要求的前提下,将圆弧、圆及多段线中的圆弧分别转换为纯折线的多段线,从而去除圆弧部分。满足基础地理信息数据库建库等特殊的数据需求。     

求解超高压筒形容器爆破压力的神经网络方法

将BP和RBF神经网络的理论和算法应用于预测超高压容器爆破压力的研究中。选用MATLAB神经网络工具箱建立预测爆破压力的神经网络模型,研究模型中影响爆破压力的主要参数,内外径比值和材料的强度极限,屈服极限,屈服强度与强度极限的比值;选用Faupel、Crossland和Bones等文献中的爆破实验数据对神经网络模型进行训练,用训练好的神经网络模型对爆破压力进行预测。预测结果表明,用BP和RBF神经网络方法建立的模型能够对超高压筒形容器的爆破压力进行较为准确的预测。