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1.
With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.  相似文献   
2.
Retina is the interior part of human's eye, has a vital role in vision. The digital image captured by fundus camera is very useful to analyze the abnormalities in retina especially in retinal blood vessels. To get information of blood vessels through fundus retinal image, a precise and accurate vessels segmentation image is required. This segmented blood vessel image is most beneficial to detect retinal diseases. Many automated techniques are widely used for retinal vessels segmentation which is a primary element of computerized diagnostic systems for retinal diseases. The automatic vessels segmentation may lead to more challenging task in the presence of lesions and abnormalities. This paper briefly describes the various publicly available retinal image databases and various machine learning techniques. State of the art exhibited that researchers have proposed several vessel segmentation methods based on supervised and supervised techniques and evaluated their results mostly on publicly datasets such as digital retinal images for vessel extraction and structured analysis of the retina. A comprehensive review of existing supervised and unsupervised vessel segmentation techniques or algorithms is presented which describes the philosophy of each algorithm. This review will be useful for readers in their future research.  相似文献   
3.
Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.  相似文献   
4.
声学海底底质分类对于海底环境和生态系统的研究具有重要意义,而多波束与侧扫声呐是目前最常用于探测海底底质的声呐设备.然而,多波束与侧扫声呐都严重地受到辐射畸变的影响,导致声呐图像噪声较大且难以消除,进而造成对海底底质的误判,并且多波束与侧扫声呐通用底质分类方法目前仍然较为缺乏.为此,本文提出了一种顾及辐射畸变的多波束和侧扫声呐的通用海底底质分类方法.该方法包括了对声呐图像中辐射畸变的改正、针对两种声呐数据特点的不同角度数据归一化、分类数自适应的非监督底质分类以及形态学去除底质图像噪声的步骤,并给出了完整的多波束与侧扫声呐通用海底底质分类流程.将本文方法应用于福建沿海同水域下实测的多波束与侧扫声呐数据得到了该水域的底质分类图像.实验结果表明了同区域下的多波束与侧扫声呐数据通过本文方法得到的底质分类结果具有较高的一致性,证明了通用底质分类方法的有效性,同时通过相互验证也提高了底质分类结果的可靠性.  相似文献   
5.
变分自编码器(VAE)作为深度隐空间生成模型的一种,近年来其表现性能取得了极大的成功,尤其是在图像生成方面。变分自编码器模型作为无监督式特征学习的重要工具之一,可以通过学习隐编码空间与数据生成空间的特征映射,进而在输出端重构生成输入数据。梳理了传统变分自编码器模型及其衍生变体模型的发展与研究现状,并就此做了总结和对比,最后分析了变分自编码器模型存在的问题与挑战,并就可能的发展趋势做了展望。  相似文献   
6.
当前,误植域名检测主要以计算域名对之间的编辑距离为基础,未能充分挖掘域名的上下文信息,且对短域名的检测易产生大量的假阳性结果。采集域名相关信息进行判定虽然有助于提高检测效果,却会引入较大的额外开销.本文采用了基于域名字符串的轻量级检测策略,并引入双向长短时记忆模型(LSTM,Long Short-Term Memory)来充分利用域名上下文,提升检测效果.本文还设计了面向域名的局部敏感哈希函数,以提高在大规模域名集合上进行误植域名检测的速度.在大量真实数据集上的实验结果表明,本文的工作改进了基于编辑距离检测方法的不足,能够有效地进行误植域名滥用检测.  相似文献   
7.
8.
Human–robot interaction during general service tasks in home or retail environment has been proven challenging, partly because (1) robots lack high-level context-based cognition and (2) humans cannot intuit the perception state of robots as they can for other humans. To solve these two problems, we present a complete robot system that has been given the highest evaluation score at the Customer Interaction Task of the Future Convenience Store Challenge at the World Robot Summit 2018, which implements several key technologies: (1) a hierarchical spatial concepts formation for general robot task planning and (2) a mixed reality interface to enable users to intuitively visualize the current state of the robot perception and naturally interact with it. The results obtained during the competition indicate that the proposed system allows both non-expert operators and end users to achieve human–robot interactions in customer service environments. Furthermore, we describe a detailed scenario including employee operation and customer interaction which serves as a set of requirements for service robots and a road map for development. The system integration and task scenario described in this paper should be helpful for groups facing customer interaction challenges and looking for a successfully deployed base to build on.  相似文献   
9.
In this paper we present a CNN based approach for a real time 3D-hand pose estimation from the depth sequence. Prior discriminative approaches have achieved remarkable success but are facing two main challenges: Firstly, the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand representation. Secondly, unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple hands. In contrast to these methods, this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision. The hand is modelled using a novel latent tree dependency model (LDTM) which transforms internal joint location to an explicit representation. Then the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration respectively. Finally, an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose. Experiments on three challenging public datasets, ICVL, MSRA, and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.   相似文献   
10.
针对纹理防伪标签在光线不足、视力不佳的情况下难以鉴别的问题,提出了一种基于DCT、感知哈希和视觉特征向量的纹理防伪标签鉴别算法.首先通过DCT和感知哈希提取图像的特征向量;然后建立特征数据库,通过特征向量之间的归一化相关系数(NC)实现纹理防伪标签的自动鉴别.实验结果表明,该方法可以有效抵抗常规攻击和几何攻击,可实现自动鉴别技术.与现有的纹理防伪技术相比,该算法可以减小占据的数据库存储空间,具有更好的实用性.  相似文献   
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