A spectral-spatial method based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection

Recently, some methods based on low-rank and sparse matrix decomposition (LRASMD) have been developed to improve the performance of hyperspectral anomaly detection (AD). However, these methods mainly take advantage of the spectral information in hyperspectral imagery (HSI), and ignore the spatial information. This article proposes an LRASMD-based spectral-spatial (LS-SS) method for hyperspectral AD. First, the Go Decomposition (GoDec) algorithm is employed to solve the low-rank background component and the sparse anomaly component. Next, the sparse component is explored to calculate the spectral sparsity divergence index (SDI). Based on spectral SDI, the detection result in the spectral domain and the reliable background points, which are employed as training data to construct the background manifold by linear local tangent space alignment (LLTSA), can also be obtained. Then, based on the background manifold and the transformation matrix, the low-dimensional manifold of the whole data is computed by linear mapping. After that, the kernel collaborative representation detector (KCRD) is used in the low-dimensional manifold of the whole data for the spatial SDI. Finally, SS SDI is computed for the final detection result. The theoretical analysis and experimental results demonstrate that the proposed LS-SS can achieve better performance when compared with the comparison algorithms.     

Generalized symmetry and its application to 3D shape generation

A new method for easily and rapidly generating three-dimensional shapes from two-dimensional line-drawings is presented. This method is based on the generalized symmetry constraint. Generalized symmetry is an extended concept of threedimensional symmetry and its axis is a 3D smooth curve. This paper first develops the definition and constraint of generalized symmetry, and then describes an algorithm which generates the three-dimensional shape of an object from its linedrawing. The generation algorithm is extended to generate generalized cylindrical objects from line-drawings. Several experiments by computer simulation verify that the algorithm can generate three-dimensional shapes from line-drawings.     

Anomaly detection based on spatio-temporal sparse representation and visual attention analysis

In this paper, we proposed a unified framework for anomaly detection and localization in crowed scenes. For each video frame, we extract the spatio-temporal sparse features of 3D blocks and generate the saliency map using a block-based center-surround difference operator. Two sparse coding strategies including off-line long-term sparse representation and on-line short-term sparse representation are integrated within our framework. Abnormality of each candidate is measured using bottom-up saliency and top-down fixation inference and further used to classify the frames into normal and anomalous ones by a binary classifier. Local abnormal events are localized and segmented based on the saliency map. In the experiments, we compared our method against several state-of-the-art approaches on UCSD data set which is a widely used anomaly detection and localization benchmark. Our method outputs competitive results with near real-time processing speed compared to state-of-the-arts.     

一种新的珍珠形状匀称性检测算法

珍珠形状匀称性是衡量珍珠质量的重要指标之一。提出了一种新的珍珠形状匀称性检测算法——四象限边缘链码自相似性检测算法,是通过计算各个象限中边缘链码之间的相似度来判定珍珠形状的匀称性算法。采用四个方向的边缘算子对珍珠形状特征面进行边缘检测、融合,把已融合的珍珠边缘划分在四个象限;通过旋转、翻转方法把不同象限的珍珠边缘表示在同一象限;对表示在同一象限中的珍珠边缘进行8方向数链码编码,根据边缘链码计算出珍珠边缘的自相似性。实验显示,四象限边缘链码自相似性能够很好地描述珍珠形状匀称性,具有一定的实用价值。     

3D symmetry detection using the extended Gaussian image

Symmetry detection is important in the area of computer vision. A 3D symmetry detection algorithm is presented in this paper. The symmetry detection problem is converted to the correlation of the Gaussian image. Once the Gaussian image of the object has been obtained, the algorithm is independent of the input format. The algorithm can handle different kinds of images or objects. Simulated and real images have been tested in a variety of formats, and the results show that the symmetry can be determined using the Gaussian image     

基于稀疏分解的超声无损检测信号处理

为了在复杂背景噪声情况下,对缺陷的大小和位置实现精确的识别,提出了一种基于Gabor原子库稀疏分解的信号处理方法,利用匹配追踪算法将信号在超完备Gabor原子库中进行稀疏表示.采用相干比阈值作为迭代终止条件,根据信号噪声水平自适应调整迭代次数.针对算法计算量大的缺点,引入遗传算法,大大提高了计算的效率.实验表明,该方法可以有效减小噪声的影响,具有计算效率高、稳定性好的特点.     

Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion

This paper proposes an approach for decentralized fault detection and diagnosis in process monitoring sensor networks. The sensor network is decomposed into multiple, potentially overlapping, blocks using the Sparse Principal Component Analysis algorithm. Local predictions are generated at each block using Support Vector Machine classifiers. The local predictions are then fused via a Maximum Entropy algorithm. Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability.     

Time-aware tensor decomposition for sparse tensors

Machine Learning - Given a sparse time-evolving tensor, how can we effectively factorize it to accurately discover latent patterns? Tensor decomposition has been extensively utilized for analyzing...     

基于稀疏低秩分解的杂草种子配准

针对杂草种子识别在实际应用中的困难,提出了一种适用于杂草种子配准的稀疏低秩分解算法。阐述了稀疏低秩算法的原理和求解方法,原本有等式约束且非凸的问题可以通过求解核范式和l1范式的无约束凸优化问题得到很好的配准结果。为了验证配准工作的重要性,运用k折交叉检验对比配准前后的识别率差异。实验结果表明,基于稀疏低秩分解的配准算法能够提高杂草种子的识别率,为实际中的杂草种子识别提供了可行方案。     

Real-time face tracking and recognition by sparse eigentracker with associative mapping to 3D shape

This paper proposes a novel framework of real-time face tracking and recognition by combining two eigen-based methods. The first method is a novel extension of eigenface called augmented eigenface and the second method is a sparse 3D eigentemplate tracker controlled by a particle filter. The augmented eigenface is an eigenface augmented by an associative mapping to 3D shape that is specified by a set of volumetric face models. This paper discusses how to make up the augmented eigenface and how it can be used for inference of 3D shape from partial images. The associative mapping is also generalized to subspace-to-one mappings to cover photometric image changes for a fixed shape. A novel technique, called photometric adjustment, is introduced for simple implementation of associative mapping when an image subspace should be combined to a shape. The sparse 3D eigentemplate tracker is an extension of the 3D template tracker proposed by Oka et al. In combination with the augmented eigenface, the sparse 3D eigentemplate tracker facilitates real-time 3D tracking and recognition when a monocular image sequence is provided. In the tracking, sparse 3D eigentemplate is updated by the augmented eigenface while face pose is estimated by the sparse eigentracker. Since the augmented eigenface is constructed on the conventional eigenfaces, face identification and expression recognition are also accomplished efficiently during the tracking. In the experiment, an augmented eigenface was constructed from 25 faces where 24 images were taken in different lighting conditions for each face. Experimental results show that the augmented eigenface works with the 3D eigentemplate tracker for real-time tracking and recognition.     

3D shape metamorphosis based on T-spline level sets

We propose a new method for 3D shape metamorphosis, where the in-between objects are constructed by using T-spline scalar functions. The use of T-spline level sets offers several advantages: First, it is convenient to handle complex topology changes without the need of model parameterization. Second, the constructed objects are smooth (C² in our case). Third, high quality meshes can be easily obtained by using the marching triangulation method. Fourth, the distribution of the degrees of freedom can be adapted to the geometry of the object. Given one source object and one target object, we firstly find a global coordinate transformation to approximately align the two objects. The T-spline control grid is adaptively generated according to the geometry of the aligned objects, and the initial T-spline level set is found by approximating the signed distance function of the source object. Then we use an evolution process, which is governed by a combination of the signed distance function of the target object and a curvature-dependent speed function, to deform the T-spline level set until it converges to the target shape. Additional intermediate objects are inserted at the beginning/end of the sequence of generated T-spline level sets, by gradually projecting the source/target object to the initial/final T-spline level set. A fully automatic algorithm is developed for the above procedures. Experimental results are presented to demonstrate the effectiveness of our method.     

3D shape recognition based on multi-modal information fusion

The classification and retrieval of 3D models have been widely used in the field of multimedia and computer vision. With the rapid development of computer graphics, different algorithms corresponding to different representations of 3D models have achieved the best performance. The advances in deep learning also encourage various deep models for 3D feature representation. For multi-view, point cloud, and PANORAMA-view, different models have shown significant performance on 3D shape classification. However, There’s not a way to consider utilizing the fusion information of multi-modal for 3D shape classification. In our opinion, We propose a novel multi-modal information fusion method for 3D shape classification, which can fully utilize the advantage of different modal to predict the label of class. More specifically, the proposed can effectively fuse more modal information. it is easy to utilize in other similar applications. We have evaluated our framework on the popular dataset ModelNet40 for the classification task on 3D shape. Series experimental results and comparisons with state-of-the-art methods demonstrate the validity of our approach.

     

A survey of content based 3D shape retrieval methods

Recent developments in techniques for modeling, digitizing and visualizing 3D shapes has led to an explosion in the number of available 3D models on the Internet and in domain-specific databases. This has led to the development of 3D shape retrieval systems that, given a query object, retrieve similar 3D objects. For visualization, 3D shapes are often represented as a surface, in particular polygonal meshes, for example in VRML format. Often these models contain holes, intersecting polygons, are not manifold, and do not enclose a volume unambiguously. On the contrary, 3D volume models, such as solid models produced by CAD systems, or voxels models, enclose a volume properly. This paper surveys the literature on methods for content based 3D retrieval, taking into account the applicability to surface models as well as to volume models. The methods are evaluated with respect to several requirements of content based 3D shape retrieval, such as: (1) shape representation requirements, (2) properties of dissimilarity measures, (3) efficiency, (4) discrimination abilities, (5) ability to perform partial matching, (6) robustness, and (7) necessity of pose normalization. Finally, the advantages and limitations of the several approaches in content based 3D shape retrieval are discussed.

基于Kinect人体脚型三维构建

针对传统测量方法成本高、操作复杂、图像特征匹配率准确度不高等问题,提出了一种将Kinect传感器与计算机视觉技术相结合的构建人体三维脚型的方法。利用Kinect传感器搭建测量系统,获取不同角度的深度信息图像,通过改进的Harris-SIFT算法对特征点进行提取与匹配,并通过迭代最近点(ICP)算法完成对点云数据的拼接。结果表明:所提方法能够便捷、准确地实现人体三维脚型的构建。