基于背景先验与中心先验的显著性目标检测北大核心CSCD

针对传统的图流行排序显著性目标检测算法存在先验信息单一,显著目标检测不完整的问题,提出一种新的基于背景先验与中心先验的显著性目标检测算法。首先将图像边界节点作为背景种子进行流行排序获得粗略的前景区域,将其再次流行排序得到初步显著图;然后利用Harris角点检测、聚类实现中心先验显著性检测,捕获中心显著信息;最后在初步显著图上融合图像中心显著性,得到最终显著图。本文对综合指标、精确率-召回率曲线、F-measure值以及平均绝对误差(mean absolute error,MAE)值进行实验评估,在公开数据集MSRA-10K和ECSSD上进行的实验结果表明:对比10种主流算法,本文算法在不同的评估指标上都具有较好的表现,且能准确地突出显著目标,提升背景抑制效果。     

基于LRC与多样本扩充的指静脉识别方法北大核心CSCD

针对目前手指静脉识别由于训练样本不足引起图像识别率低的问题,提出基于线性回归分类(linear regression classification,LRC)与多样本扩充的指静脉识别方法。首先,利用矩阵变换生成原始图像的镜像,训练原始图像与镜像,增加指静脉图像中包含的有用信息;然后,基于LRC对测试和训练样本进行分类;最后,通过计算偏差得到最终分类结果,求出识别率。此外,设计了一种指静脉采集装置收集得到自建指静脉数据库。实验结果表明:所提算法在自建指静脉数据库、山东大学指静脉数据库、马来西亚理工大学指静脉数据库上的识别率分别达到98.93%、98.89%、99.67%,最低等误率为2.3888%。实验结果与其他传统和流行算法相比具有明显优势,拥有良好的实际应用价值。     

图像分类问题的几种深度学习策略

针对图像分类学习不够深入的问题,提出图像分类问题的几种深度学习策略研究。通过分析当前主流的主动深度学习图像、多标签图像和多尺度网络图像三种深度学习方法的工作原理和存在的优势与不足,探讨图像分类问题的优化学习策略。随后采用图像分类问题的几种深度学习策略实验的方式加以对比,实验结果表明,参数共享的深度学习图像分类方法不仅提高了预测速度,而且还能确保模型的准确性。     

Efficient graph attentional network for 3D object detection from Frustum-based LiDAR point clouds

LiDAR-based 3D object detection is important for autonomous driving scene perception, but point clouds produced by LiDAR are irregular and unstructured in nature, and cannot be adopted by the conventional Convolutional Neural Networks (CNN). Recently, Graph Convolutional Networks (GCN) has been proved as an ideal way to handle non-Euclidean structure data, as well as for point cloud processing. However, GCN involves massive computation for searching adjacent nodes, and the heavy computational cost limits its applications in processing large-scale LiDAR point cloud in autonomous driving. In this work, we adopt a frustum-based point cloud-image fusion scheme to reduce the amount of LiDAR point clouds, thus making the GCN-based large-scale LiDAR point clouds feature learning feasible. On this basis, we propose an efficient graph attentional network to accomplish the goal of 3D object detection in autonomous driving, which can learn features from raw LiDAR point cloud directly without any conversions. We evaluate the model on the public KITTI benchmark dataset, the 3D detection mAP is 63.72% on KITTI Cars, Pedestrian and Cyclists, and the inference speed achieves 7.9 fps on a single GPU, which is faster than other methods of the same type.     

Motion detection in moving camera videos using background modeling and FlowNet

Real-time moving object detection is challenging for moving cameras due to the moving background. Many studies use homography matrix to compensate for global motion by warping the background model to the current frame. Then, the pixel difference between the current frame and the background model is used for background subtraction. Moving pixels are extracted by applying adaptive threshold and some post-processing techniques. On the other hand, deep learning-based dense optical flow can be efficient enough to extract the moving pixels, but it increases computational cost. This study proposes a method to enhance a classical background modeling method with deep learning-based dense optical flow. The main contribution of this paper is to propose a fusing algorithm for dense optical flow and background modeling approach. The background modeling methods are error-prone, especially with continuous camera movement, while the optical flow method alone may not always be efficient. Our hybrid method fuses both techniques to improve the detection accuracy. We propose a software architecture to run background modeling and dense optical flow methods in parallel processes. The proposed implementation approach significantly increases the method’s working speed, while the proposed fusion and combining strategy improve detection results. The experimental results show that the proposed method can run at high speed and has satisfying performance against the methods in the literature.     

Effective electromagnetic and chiral parameters of chiral composite material

The effective parameters of chiral composite are studied using a simple model, that is, randomly oriented non-interacting wire helices embedded in a nonchiral host medium. It is found that both the effective permittivity and permeability are independent on the handedness of the chiral objects while the effective chirality admittance is dependent. It is also found that when the ratio of the radius of the chiral helix to its pitch is about 0.23, maximum chirality admittance is achieved. The effective parameters of equichiral sample are also discussed.     

Orientation-reversing involutions on handlebodies

The observation that the quotient orbifold of an orientation- reversing involution on a 3-dimensional handlebody has the structure of a compression body leads to a strong classification theorem, and general structure theorems. The structure theorems decompose the action along invariant discs into actions on handlebodies which preserve the -fibers of some -bundle structure. As applications, various results of R. Nelson are proved without restrictive hypotheses.

     

Universal system for liquid stationary phase classification on the basis of thermodynamical data

Summary Equations modifying Rohrschneider's relationship between I and thermodynamical characteristics values are derived. Polarity in chromatography is discussed as a possibility of stationary phases or absorbents to enter into intermolecular interactions and is determined on the basis of the partial molar free energies of solution (G) of six test substances. A simple method for the calculation of stationary phase polarity in terms of G from retention indices and McReynolds constants is given. Equations are presented for the calculation of stationary phase selectivity utilizing G data tabulated for selected test substances. The thermodynamical basis of selecting a reference phase for a unified system expressing the selectivity of stationary phases which can also be applied for adsorbents is suggested. The energy equivalent to an index unit, G_i.u., is discussed.     

磷脂酶A2家族的功能性分类研究

采用“结合强度指纹图谱分析”方法, 通过对多重分子对接得到的作用强度数据进行聚类矩阵分析对蛋白质进行功能分类. 着重研究了磷脂酶A₂家族基于抑制剂作用强度的功能分类, 并且与基于序列的聚类结果进行比较, 成功地解决了序列比对方法不能处理的远源蛋白(cPLA₂)的分类问题.     

西松烷型二萜内酯的全合成进展

按照内酯环的大小综述了西松烷二萜的合成方法。