Siamese visual tracking with multilayer feature fusion and corner distance IoU loss

The tracker based on the Siamese network regards tracking tasks as solving a similarity problem between the target template and search area. Using shallow networks and offline training, these trackers perform well in simple scenarios. However, due to the lack of semantic information, they have difficulty meeting the accuracy requirements of the task when faced with complex backgrounds and other challenging scenarios. In response to this problem, we propose a new model, which uses the improved ResNet-22 network to extract deep features with more semantic information. Multilayer feature fusion is used to obtain a high-quality score map to reduce the influence of interference factors in the complex background on the tracker. In addition, we propose a more powerful Corner Distance IoU (intersection over union) loss function so that the algorithm can better regression to the bounding box. In the experiments, the tracker was extensively evaluated on the object tracking benchmark data sets, OTB2013 and OTB2015, and the visual object tracking data sets, VOT2016 and VOT2017, and achieved competitive performance, proving the effectiveness of this method.     

磁场对含稀土铝锂合金断裂特征的影响

以含铈铝锂合金为研究对象，研究了磁场对含稀土铝锂合金时效过程的影响，从断裂特征及微观组织两方面分析了磁场作用规律。实验结果表明：未加磁场时，合金的断裂特征以理断裂为主；扁平晶粒厚度不匀，在磁场作用下，此合金的断裂特征与微观组织发生明显改观，随磁感应强度增强，合金主断裂面上准解理断裂特征减弱，同时二次裂纹增多，分层比较升高；扁平晶粒厚度变薄，尺寸趋于均匀。用少体物理理论，探讨了磁场对含稀土合金原子扩散过程的影响，为进一步研究稀土元素在磁场中的行为及其局域效应提供了一定的实验依据。     

探地雷达回波信号的特征提取和分类

探地雷达的回波信号具有非平稳特征.为实现非平稳信号的检测和分类,采用了二进小波变换、主分量分析与Fisher线性判别分析和前馈多层感知器分类器分别对探地雷达回波信号进行小波分解、去相关与特征选择和分类.实验结果表明,该算法具有很好的分类效果.     

MPEG-2压缩域的视频数字水印新算法

视频数字水印技术是当前水印研究中的一个热点.本文提出一种视频数字水印新算法,直接对压缩后的视频数据流进行操作,根据运动矢量的特征值,将水印信息嵌入到运动矢量上,不需要完全解码和再编码过程.所提出的水印算法简单、有效,实现了水印盲检.与嵌入水印前的原视频图像相比,嵌入水印后的视频图像信噪比损失很小.     

Learning hierarchical spatio-temporal pattern for human activity prediction

Human activity prediction has become increasingly valuable in many applications. This paper, initially from the perspective of cognition science, presents a novel approach to learning a hierarchical spatio-temporal pattern of human activities to predict ongoing activities from videos that contain only the onsets of the activities. Spatio-temporal pattern can be learned by a Hierarchical Self-Organizing Map (HSOM), which consists of two self-organizing maps (i.e., action map and actionlet map) connected via associative links trained by Hebbian learning. Ongoing activities can be predicted by Variable order Markov Model (VMM), which provides the means for capturing both large and small order Markov dependencies based on the training actionlet sequences. Experiments of the proposed method on four challenging 3D action datasets captured by commodity depth cameras show promising results.     

一种不同地面分辨率图像的拼接算法

航空遥感监测中变焦镜头的广泛应用以及飞行高度的变化会获得不同地面分辨率的航空影像,因此为了实时监控到飞行区域的整体情况,需要对不同地面分辨率的图像进行拼接。提出了针对不同地面分辨率图像进行拼接的方法。首先用四分法对图像进行分割,检测变化倍率,选择配准策略;然后用基于加速鲁棒性特征(Speeded-Up Robust Features,SURF)的算法对相邻图像进行拼接。获得了焦距变化从高倍过渡到低倍,或从低倍过渡到高倍的拼接图像。通过航空变焦模拟影像的拼接实验和地面变焦影像的拼接实验表明,该方法能够对不同地面分辨率的图像进行拼接,且拼接结果均以高分辨率为基准。     

Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra

The nonlinear, nonnegative single‐mixture blind source separation problem consists of decomposing observed nonlinearly mixed multicomponent signal into nonnegative dependent component (source) signals. The problem is difficult and is a special case of the underdetermined blind source separation problem. However, it is practically relevant for the contemporary metabolic profiling of biological samples when only one sample is available for acquiring mass spectra; afterwards, the pure components are extracted. Herein, we present a method for the blind separation of nonnegative dependent sources from a single, nonlinear mixture. First, an explicit feature map is used to map a single mixture into a pseudo multi‐mixture. Second, an empirical kernel map is used for implicit mapping of a pseudo multi‐mixture into a high‐dimensional reproducible kernel Hilbert space. Under sparse probabilistic conditions that were previously imposed on sources, the single‐mixture nonlinear problem is converted into an equivalent linear, multiple‐mixture problem that consists of the original sources and their higher‐order monomials. These monomials are suppressed by robust principal component analysis and hard, soft, and trimmed thresholding. Sparseness‐constrained nonnegative matrix factorizations in reproducible kernel Hilbert space yield sets of separated components. Afterwards, separated components are annotated with the pure components from the library using the maximal correlation criterion. The proposed method is depicted with a numerical example that is related to the extraction of eight dependent components from one nonlinear mixture. The method is further demonstrated on three nonlinear chemical reactions of peptide synthesis in which 25, 19, and 28 dependent analytes are extracted from one nonlinear mixture mass spectra. The goal application of the proposed method is, in combination with other separation techniques, mass spectrometry‐based non‐targeted metabolic profiling, such as biomarker identification studies. Copyright © 2015 John Wiley & Sons, Ltd.     

结合RANSAC的SIFT算法在交通视频检测中的稳像技术研究

交通视频监控摄像头在大风条件下存在抖动问题,给后续的视频分析带来了困难。针对这一问题,文中首先介绍了3种相关方法：RANSAC、SIFT和最小二乘法。并在此基础上提出了一个视频稳像算法,实验证明该方法能较好地解决路面上的摄像头画面抖动问题。