Selective nonanal molecular recognition with SnO2 nanosheets for lung cancer sensor

SnO₂ nanosheets were developed to detect nonanal gas in the order of ppb which was a marker of lung cancer. The nanosheets showed higher resistance change in nonanal gas than that in carbon monoxide (CO), nitrogen dioxide (NO₂), acetone (CH₃COCH₃), hydrogen (H₂), ethanol (C₂H₆O), ammonia (NH₃), hydrogen sulfide (H₂S), formaldehyde (HCHO), acetaldehyde (CH₃CHO), or butanal (C₄H₈O). Crystal surfaces of the nanosheets would be effective for adsorption of nonanal molecules. Furthermore, it was shown that resistance changed with an increase in carbon number in aldehyde. The nanosheets had molecular selectivity for a series of aldehyde molecules. Molecular recognition of the nanosheets gave us a great advantage to detect nonanal gas which was produced by lung cancer.     

基于人体关节点数据的攻击性行为识别

针对人体攻击性行为识别问题，提出一种基于人体关节点数据的攻击性行为识别方法。首先，利用OpenPose获得单帧图像中的人体关节点数据，并通过最近邻帧特征加权法和分段多项式回归完成由人体自遮挡和环境因素所导致缺失值的补全；然后，对每个人体定义动态"安全距离"阈值，如果两人真实距离小于阈值，则构建行为特征矢量，其中包括帧间人体重心位移、人体关节旋转角角速度和发生交互时的最小攻击距离等；最后，提出改进的LightGBM算法w-LightGBM，并对攻击性行为进行识别。采用公共数据集UT-interaction对所提出的攻击性行为分类识别方法进行测试实验，准确率达到95.45%。实验结果表明，所提方法能够有效识别各种角度的攻击性行为。     

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.     

Emotion recognition at the edge with AI specific low power architectures

Nowadays Deep Learning is applied in almost every research field and helps getting amazing results in a great number of challenging tasks. The main problem is that this kind of learning and consequently Neural Networks that can be defined deep, are resource intensive. They need specialized hardware to perform computation in a reasonable time. Many tasks are mandatory to be as much real-time as possible . It is needed to optimize many components such as code, algorithms, numeric accuracy and hardware, to make them “efficient and usable”. All these optimizations can help us to produce incredibly accurate and fast learning models. The paper reports a study in this direction for the challenging face detection and emotion recognition tasks.     

基于秩序与非秩序理论的文创产品卡通形象设计研究

目的 从秩序与非秩序理论出发,研究文创产品卡通形象的设计方法.方法 对秩序与非秩序理论的视觉机制和创制理论进行梳理,结合实例,阐述了其与文创产品卡通形象设计之间的联系,分析文创产品卡通形象中的秩序与非秩序的表现形式,提出基于秩序与非秩序理论文创产品卡通形象设计的主要原则,归纳出一套基于秩序与非秩序理论的文创产品卡通形象设计流程与方法,指导设计实践,并验证这些理论方法在设计实践中有效性与可行性.结论 通过对秩序与非秩序理论的研究,文创产品卡通形象设计时应注意提高辨识度、亲和力和便于衍生设计的原则.探索基于秩序与非秩序理论的设计流程与方法,按照界定主题、主体形象设计、主体形象拓展、衍生品应用四个环节开展整体方案设计,为文创产品卡通形象设计提供新的思路与方法.     

Sub-band coding of hexagonal images

According to the circle-packing theorem, the packing efficiency of a hexagonal lattice is higher than an equivalent square tessellation. Consequently, in several contexts, hexagonally sampled images compared to their Cartesian counterparts are better at preserving information content. In this paper, novel mapping techniques alongside the wavelet compression scheme are presented for hexagonal images. Specifically, we introduce two tree-based coding schemes, referred to as SBHex (spirally-mapped branch-coding for hexagonal images) and BBHex (breadth-first block-coding for hexagonal images). Both of these coding schemes respect the geometry of the hexagonal lattice and yield better compression results. Our empirical results show that the proposed algorithms for hexagonal images produce better reconstruction quality at low bits per pixel representations compared to the tree-based coding counterparts for the Cartesian grid.