Stereoscopic view synthesis based on region-wise rendering and sparse representation

Depth image-based rendering (DIBR), which is used to render virtual views with a color image and the corresponding depth map, is one of the key techniques in the 2D to 3D conversion process. One of the main problems in DIBR is how to reduce holes that occur on the generated virtual view images. In this paper, we make two main contributions to deal with the problem. Firstly, a region-wise rendering framework, which divides the original image regions into three special classes and renders each with optimal adaptive process respectively, is introduced. Then, a novel sparse representation-based inpainting method, which can yield visually satisfactory results with less computational complexity for high quality 2D to 3D conversion, is proposed. Numerical experimental results demonstrate the good performance of the proposed methods.     

基于多重稀疏字典的行人检测方法

针对于稀疏编码在行人检测问题中提取的特征维数高和不能够有效描述行人的问题,提出了一种基于多重稀疏字典直方图的特征提取方法。通过稀疏表示方法,预先学习多个不同稀疏度的字典,分别利用每一个字典对行人图像进行稀疏编码,统计每个字典中对应稀疏编码单元的分布直方图作为行人图像的特征描述子。该方法提取到的特征维数低,并且能够有效地描述行人,具有良好的检测性能。     

稀疏阈值的超分辨率图像重建

为了解决基于字典学习的超分辨重构算法耗时过长的问题,提出了基于稀疏阈值模型的图像超分辨率重建方法。首先,将联合字典理论与图像块稀疏阈值方法相结合,训练得到高、低分辨率过完备图像字典对。接着,通过稀疏阈值OMP算法对图像特征块进行稀疏表示。然后,通过高分辨率字典重构出初始的超分辨图像。最后,通过改进迭代反投影算法对初始的超分辨图像进行全局优化,从而进一步提高图像重构质量。实验结果表明,超分辨图像重构平均峰值信噪比(PSNR)为30.1 d B,平均结构自相似度(SSIM)为0.937 9,平均计算时间为10.2 s。有效提高了超分辨重构的速度,改善了重构高分辨图像的质量。     

游客情感计算的文本大数据挖掘方法比较研究

旅游文本大数据以其方便、快捷和低门槛的特点为游客情感计算提供了极大便利,已经成为旅游大数据的主要来源之一。基于大数据理论和情感理论,以文本大数据为数据源,在全面梳理国内外情感计算相关成果的基础上,利用人工智能中的逻辑/算法编程方法、机器学习方法、深度学习方法对旅游文本大数据进行挖掘,探索最佳的基于文本大数据的游客情感计算方法。研究发现：（1）基于情感词典的游客情感计算模型,其核心是构建情感词典和设计情感计算规则,方法简单,容易实现,适用语料范围广。（2）机器学习,用统计学方法抽取文本中的特征项,具有非线性特征,可靠性较线性特征的情感词典方法高。（3）基于深度学习技术的游客情感计算,效果良好,准确率在85%以上。训练多领域的文本语料易于移植,实用性强,且泛化能力好,较适合大数据时代游客情感计算研究。     

A Method of Domain Dictionary Construction for Electric Vehicles Disassembly

Currently, there is no domain dictionary in the field of electric vehicles disassembly and other domain dictionary construction algorithms do not accurately extract terminology from disassembly text, because the terminology is complex and variable. Herein, the construction of a domain dictionary for the disassembly of electric vehicles is a research work that has important research significance. Extracting high-quality keywords from text and categorizing them widely uses information mining, which is the basis of named entity recognition, relation extraction, knowledge questions and answers and other disassembly domain information recognition and extraction. In this paper, we propose a supervised learning dictionary construction algorithm based on multi-dimensional features that combines different features of extraction candidate keywords from the text of each scientific study. Keywords recognition is regarded as a binary classification problem using the LightGBM model to filter each keyword, and then expand the domain dictionary based on the pointwise mutual information value between keywords and its category. Here, we make use of Chinese disassembly manuals, patents and papers in order to establish a general corpus about the disassembly information and then use our model to mine the disassembly parts, disassembly tools, disassembly methods, disassembly process, and other categories of disassembly keywords. The experiment evidenced that our algorithms can significantly improve extraction and category performance better than traditional algorithms in the disassembly domain. We also investigated the performance algorithms and attempts to describe them. Our work sets a benchmark for domain dictionary construction in the field of disassembly of electric vehicles that is based on the newly developed dataset using a multi-class terminology classification.     

Fast Super‐Resolution Algorithm Based on Dictionary Size Reduction Using k‐Means Clustering

This paper proposes a computationally efficient learning‐based super‐resolution algorithm using k‐means clustering. Conventional learning‐based super‐resolution requires a huge dictionary for reliable performance, which brings about a tremendous memory cost as well as a burdensome matching computation. In order to overcome this problem, the proposed algorithm significantly reduces the size of the trained dictionary by properly clustering similar patches at the learning phase. Experimental results show that the proposed algorithm provides superior visual quality to the conventional algorithms, while needing much less computational complexity.     

某型电子信息系统模块级故障诊断技术研究

针对电子信息系统结构复杂、难于建立精确数学模型等特点，以某型电子信息系统为例，采用故障字典和BP神经网络相结合的方法，利用Multisim软件进行电路仿真。再由实测样本数据对BP神经网络进行训练，完成对网络各参数的设置。仿真结果表明，该方法可以较好地将故障定位到元器件，同时可为其他电子信息系统模块级故障诊断技术研究提供借鉴。     

基于文本理解的自动文摘系统研究与实现

本系统模拟人的理解、知识获取认知过程，利用全信息词典有效地组织语言学知识。背景知识和领域知识，利用部分分析器算法，实现语法语义语用分析一体化，从文本中提取重要信息，将任一篇文章转换为计算机内的一个信息提取框架实体．根据填充情况，由文摘生成器产生合适的文摘．目前已实现计算机病毒领域文章的自动文摘，验证了文摘模型的可行性和有效性．文摘系统模型与领域无关．     

一种新的数据持久层设计方法与实现

针对目前数据库模型改动将导致业务层和数据库层的大量数据修改的不足这一问题,提出建立新的框架即双重数据持久层框架的设计方法,该方法联合运用iBATIS数据持久层框架和数据字典技术来实现.当数据库模型需要变更时,可较方便地通过操作数据字典,来实现对数据持久层和数据库模型的同步维护,较大程度提高了工作效率,可移植性增强.     

移动基站电费管控措施探讨

从分析移动基站电费现状入手,深入研究了转供电改造模型的建立,通过对直供电改造中基站耗电量、电费单价、改造投资金额与投资回收年限关系的分析,提出直供电改造投资模型字典,可作为基站转供电改造选择依据,并对管理节能及电费管控的相关内容进行了深入研究。