Order-of-addition experiments: A review and some new thoughts

Abstract

The study of order-of-addition (OofA) experiments is prevalent in many scientific and industrial areas. The statistical design of experiments (DOE) will considerably improve the efficiency of OofA experiments. Designing and modeling the OofA experiments have increasingly received a great deal of attention. In this article, we review the latest work on the design and model of OofA experiments, and introduce some new thoughts. We believe that this article will motivate fruitful applications in real OofA experiments as well as future developments in the methodology.     

Hybrid Segmentation Scheme for Skin Features Extraction Using Dermoscopy Images

Objective and quantitative assessment of skin conditions is essential for cosmeceutical studies and research on skin aging and skin regeneration. Various handcraft-based image processing methods have been proposed to evaluate skin conditions objectively, but they have unavoidable disadvantages when used to analyze skin features accurately. This study proposes a hybrid segmentation scheme consisting of Deeplab v3+ with an Inception-ResNet-v2 backbone, LightGBM, and morphological processing (MP) to overcome the shortcomings of handcraft-based approaches. First, we apply Deeplab v3+ with an Inception-ResNet-v2 backbone for pixel segmentation of skin wrinkles and cells. Then, LightGBM and MP are used to enhance the pixel segmentation quality. Finally, we determine several skin features based on the results of wrinkle and cell segmentation. Our proposed segmentation scheme achieved a mean accuracy of 0.854, mean of intersection over union of 0.749, and mean boundary F1 score of 0.852, which achieved 1.1%, 6.7%, and 14.8% improvement over the panoptic-based semantic segmentation method, respectively.     

Digital image correlation strain measurement of thick adherend shear test specimen joined with an epoxy film adhesive

Structural bonding and bonded repairs of composite materials become more and more important. Understanding the strain within the bondline leads to suitable bonding design. For new design approaches the strain distribution within the bondline has to be analyzed. Thus, often finite element analysis (FE) are used. However, a huge challenge is the availability of reliable material properties for the adhesives and their validation. Previous work has shown that it is possible to measure the small displacements resulting within thin epoxy film adhesives using high resolution digital image correlation (DIC). In this work a 2D DIC setup with a high resolution consumer camera is used to visualize the strain distribution within the bondline over the length of the joint as well as over the adhesive thickness. Therefore, single lap joints with thick aluminum adherends according to ASTM D 5656 are manufactured and tested. Local 2D DIC strain measurements are performed and analyzed. Two different camera setups are used and compared. The evaluation provides reliable material data and enables a look insight the bondline. The results of the full field strain data measured with DIC are compared with numerical simulations. Thus, material models as well as chosen parameters for the adhesive are validated. Compared to extensometers, giving only point-wise information for fixed measuring points, the DIC allows a virtual point-wise inspection along the complete bondline. Furthermore, it allows measuring close to the bondline to reduce the influence of adherend deformation.     

基于文本与语义相关性分析的图像检索

为了更加有效地检索到符合用户复杂语义需求的图像，提出一种基于文本描述与语义相关性分析的图像检索算法。该方法将图像检索分为两步：基于文本语义相关性分析的图像检索和基于SIFT特征的相似图像扩展检索。根据自然语言处理技术分析得到用户文本需求中的关键词及其语义关联，在选定图像库中通过语义相关性分析得到“种子”图像；接下来在图像扩展检索中，采用基于SIFT特征的相似图像检索，利用之前得到的“种子”图像作为查询条件，在网络图像库中进行扩展检索，并在结果集上根据两次检索的图像相似度进行排序输出，最终得到更加丰富有效的图像检索结果。为了证明算法的有效性，在标准数据集Corel5K和网络数据集Deriantart8K上完成了多组实验，实验结果证明该方法能够得到较为精确地符合用户语义要求的图像检索结果，并且通过扩展算法可以得到更加丰富的检索结果。     

基于Marr小波改进的SIFT算法的遥感影像配准

〖HTH〗通讯作者〖HTSS〗:[ZK（]金〓燕(1991-)，女，山东济宁人，硕士研究生，主要从事图形图像处理，图像与视觉信息计算方面的研究。E\|mail:jyan0529@163.com。[ZK）] 〖ZW)〗〖HT〗 〖AM〗〖HT5SS〗〖MM(〗〖ZZ(S〗〖HT5”〗〖SX(B〗第34卷〓第3期〖〗2019年6月〖SX)〗[KG0.2mm]〖KG7*3〗〖HT〗〖SX(B〗遥〓感〓技〓术〓与〓应〓用〖〗〖WT5，6〗REMOTE SENSING TECHNOLOGY AND APPLICATION〖SX)〗〓〓〓〓〖KG6*2〗〖WT5”BX〗〖SX(B〗Vol.34〓No.3〖〗Jun.2019〖WT〗〖SX)〗〖ZZ)〗〖MM)〗〖HT〗 〖HT2H〗〖JZ(〗〖WTHZ〗〖STHZ〗 基于Marr小波改进的SIFT算法的遥感影像配准 〖STBZ〗〖WTBZ〗〖HT4K〗 张海涛，金〓燕，刘万军 〖HT5K〗 (辽宁工程技术大学 软件学院，辽宁 葫芦岛〓125105) 〖JZ)〗〖ＨＴ５Ｈ〗〖GK2!2〗摘要〖HTK〗: [KG(0.1mm]针对遥感图像配准方法中错误匹配点对过多、配准效率低和其他性能，提出了一种基于小波的遥感图像配准方法。首先，利用尺度空间理论下的Marr小波对参考图像和待配准图像进行特征提取，然后利用欧氏距离对参考图像和待配准图像的特征点进行初配准，再根据随机采样一致法，对初配准结果进行精配准。为了验证方法的有效性，选择无人机实时航拍图像、不同时相变化遥感图像以及遥感不同高度的遥感图像。实验结果表明：该方法与SIFT(Scale Invariant Feature Transform)算法以及其他改进SIFT算法相比可以有效剔除错误匹配点对，提高了配准精度，同时提高配准效率两倍以上。该方法可以应用于不同遥感数据源，能够有效地提高配准精度，降低配准时间。[KG)] 〖ＨＴＨ〗关〓键〓词〖ＨＴ５Ｋ〗: 遥感图像配准;Marr小波;欧氏距离;随机采样一致法 〖ＨＴＨ〗中图分类号〖HTSS〗:TP79〓〓〖HTH〗文献标志码〖HTSS〗:A〓〓〖HTH〗文章编号〖HTSS〗:1004 0323(2019)03 0622 08 〖HK〗〖HT5SS〗     

Low-light image enhancement via deep Retinex decomposition and bilateral learning

Low-light images enhancement is a challenging task because enhancing image brightness and reducing image degradation should be considered simultaneously. Although existing deep learning-based methods improve the visibility of low-light images, many of them tend to lose details or sacrifice naturalness. To address these issues, we present a multi-stage network for low-light image enhancement, which consists of three sub-networks. More specifically, inspired by the Retinex theory and the bilateral grid technique, we first design a reflectance and illumination decomposition network to decompose an image into reflectance and illumination maps efficiently. To increase the brightness while preserving edge information, we then devise an attention-guided illumination adjustment network. The reflectance and the adjusted illumination maps are fused and refined by adversarial learning to reduce image degradation and improve image naturalness. Experiments are conducted on our rebuilt SICE low-light image dataset, which consists of 1380 real paired images and a public dataset LOL, which has 500 real paired images and 1000 synthetic paired images. Experimental results show that the proposed method outperforms state-of-the-art methods quantitatively and qualitatively.     

Building a text collection for Urdu information retrieval

Urdu is a widely spoken language in the Indian subcontinent with over 300 million speakers worldwide. However, linguistic advancements in Urdu are rare compared to those in other European and Asian languages. Therefore, by following Text Retrieval Conference standards, we attempted to construct an extensive text collection of 85 304 documents from diverse categories covering over 52 topics with relevance judgment sets at 100 pool depth. We also present several applications to demonstrate the effectiveness of our collection. Although this collection is primarily intended for text retrieval, it can also be used for named entity recognition, text summarization, and other linguistic applications with suitable modifications. Ours is the most extensive existing collection for the Urdu language, and it will be freely available for future research and academic education.