基于区域相似特征的憎水性图像分割算法

绝缘子憎水性能检测是判断绝缘子抗污闪性能的重要手段。传统的分割算法容易产生误分割而影响判别。为此提出了一种基于目标区域相似性的图像分割算法,根据水珠（水迹)内部具有灰度相似性和梯度相似性的原理进行区域增长,并且运用数学形态学进行图像修正。实验结果表明,该方法实现了目标区域的有效分割,为后续绝缘子性能的判定提供依据。     

基于photoshop实现浮雕图像“数字拓片”的技术研究

拓片作为我国特有的传统艺术形式,目前正面临着一系列尴尬问题.一方面,无论是其自身作为非物质文化宝贵遗产,还是对于文化艺术及其它领域的重要研究价值而言,拓片艺术都应当得到弘扬与发展.然而另一方面,拓片的传统制作方式又与珍贵文物的保护之间有着十分明显的矛盾和冲突.基于此,充分发挥艺术专业学科的优势,结合先进的计算机技术,提出了一套将浮雕物的"实物照片"转化成"数字拓片"的方案,并通过试验初步验证了该方案的有效性.     

基于相似性叙词表的模糊集合模型

传统的模糊集合模型基于词词关联矩阵来实现模糊检索,词词关联矩阵只考虑语词在文献内部的同时出现.本文提出了一个基于相似性叙词表的模糊集合模型,考虑语词与查询之间的相似性,并将查询扩展包含在此模型中,从而在一定程度上提高了检索性能.     

CBR快速检索算法在时间序列预测中的应用

随着CBR应用的推广,涉及越来越多的时态信息需要处理.探讨了一种基于时间序列数据的时态CBR,提出了一种基于卷积的时态CBR快速检索算法.其思路是利用时序范例之间的时间约束关系,去除检索中求取相似度的冗余计算,并利用卷积的傅立叶变换性质,在频域求解相似度以减少计算时间复杂度.实验证明.在匹配较长的序列时,快速算法可以显著的提高时态CBR的检索效率.在CBR快速检索算法的基础上,以证券价格预测问题作为应用,借鉴流形学习理论中LLE算法的思想,设计了一种基于时态CBR的时间序列预测算法.实验证明,这种基于时态CBR的时间序列预测方法与前述CBR快速检索算法相配合,取得了较好的预测效果和预测效率.     

时间序列相似性度量在水文数据挖掘中的应用研究

相似性度量方法是时间序列相似性研究的重要课题,同时也是水文时间序列相似性挖掘的关键问题之一.充分分析目前相似性度量的研究成果,结合水文时间序列相似性挖掘模型,通过实验探索适合水文数据特点的相似性度量方法.     

IPC-Net: Incomplete point cloud classification network based on data augmentation and similarity measurement

Existing point cloud classification researches are usually conducted on datasets with complete structure and clear semantics. However, in real point cloud scenes, the occlusion and truncation may destroy the completeness of objects affecting the classification performance. To solve this problem, we propose an incomplete point cloud classification network (IPC-Net) with data augmentation and similarity measurement. The proposed network learns the feature representation of incomplete point clouds and the semantic differences compared to the complete ones for classification. Specifically, IPC-Net adopts a random erasing-based data augmentation to deal with incomplete point clouds. IPC-Net also introduces an auxiliary loss function weighted by attention scores to measure the similarity between the incomplete and the complete point clouds. Extensive experiments verify that IPC-Net has the ability to classify incomplete point clouds and significantly improves the robustness of point cloud classification under different completeness.     

A marker-less assembly stage recognition method based on corner feature

Fast and robust product assembly stage recognition is a key step in human–machine cooperative assembly. To solve the recognition problem of similar adjacent assembly stages, a marker-less assembly stage recognition method is proposed based on corner feature between an assembling product and a digital model. Considering the geometric features of mechanical products, a corner identification method is proposed based on the circumferential angle difference (CADF). Then a corner matching method based on distance constraint is studied for ICP registration to realize the point cloud registration between the product and digital model. Based on the registration relationship, a similarity algorithm based on proximity point proportion is used to calculate the similarity between models and the input assembling product. The model with the greatest similarity is taken as the stage recognition result. In experiments on four group assembling products, the average stage recognition accuracy is 96.15%, which indicates that the proposed method can solve the stage recognition problem. The corner identification method based on the CADF outperforms the Harris-3D corner detection method in the efficiency of assembly stage recognition.     

SRI-Net: Similarity retrieval-based inference network for light field salient object detection

The cutting-edge RGB saliency models are prone to fail for some complex scenes, while RGB-D saliency models are often affected by inaccurate depth maps. Fortunately, light field images can provide a sufficient spatial layout depiction of 3D scenes. Therefore, this paper focuses on salient object detection of light field images, where a Similarity Retrieval-based Inference Network (SRI-Net) is proposed. Due to various focus points, not all focal slices extracted from light field images are beneficial for salient object detection, thus, the key point of our model lies in that we attempt to select the most valuable focal slice, which can contribute more complementary information for the RGB image. Specifically, firstly, we design a focal slice retrieval module (FSRM) to choose an appropriate focal slice by measuring the foreground similarity between the focal slice and RGB image. Secondly, in order to combine the original RGB image and the selected focal slice, we design a U-shaped saliency inference module (SIM), where the two-stream encoder is used to extract multi-level features, and the decoder is employed to aggregate multi-level deep features. Extensive experiments are conducted on two widely used light field datasets, and the results firmly demonstrate the superiority and effectiveness of the proposed SRI-Net.     

信号相关杂波背景中极化雷达发射波形优化

波形优化可有效抑制干扰,显著改善雷达探测性能.针对全极化雷达,考虑发射波形满足能量和相似性双重约束,以最大化信杂噪比为准则,对发射波形和接收滤波器进行联合优化.该文设计了一种波形和滤波器的迭代优化算法,该方法序贯提高输出信杂噪比.算法的每一次迭代需要分别解决一个凸问题和隐凸问题,整个算法的计算量与迭代次数和接收滤波器长度分别呈线性和多项式关系.最后,通过仿真实验分析了算法的收敛性、优化波形的模糊度函数方面的性质,与其他算法进行了对比,结果表明:与现有方法相比,该文方法可实现信杂噪比的有效提升.     

一种基于语义网的相似度计算模型

在检索信息时我们经常简单的依靠关键词匹配,往往导致检索结果不全或者不准。随着语义网概念的提出,为我们提供了新的研究方向。本文在传统的计算句子相似度的基础上,提出了把语义网和改进的相似度计算方法相结合,并把该计算模型应用在依靠论文摘要来检索相关论文上。由于语义网的引入,使得搜索变得智能化,从简单的依靠关键词匹配提高到语义层面,从而提高了查全率和查准率。