基于低秩稀疏分解的自适应运动目标检测算法

传统的低秩稀疏分解方法使用[l1]范数把场景中的运动目标建模为稀疏离群值,分离出低秩的背景成分与稀疏的运动目标成分。然而,在许多实际场景中往往会有动态背景的情形（例如水面波纹、树木摇动）,[l1]范数并不能区分出这些干扰与真实目标,从而大大影响检测效果。实际上,运动目标区域中的像素不仅仅具有稀疏性,还具有空间分布上的连续性。通过引入空间融合稀疏约束,在空间连续性和稀疏性两方面对运动目标进行建模,使模型更符合目标像素的分布规律。同时,设计了一种自适应的参数更新方法,使算法的鲁棒性进一步提升。在公共数据集上的大量实验表明,相比于传统方法,该算法在准确率和鲁棒性方法有很大提高。     

Multi-focus image fusion based on sparse decomposition and background detection

The goal of image fusion is to accurately and comprehensively describe complementary information of multiple source images in a new scene. Traditional fusion methods are easy to produce side-effects which cause artifacts and blurred edges. To solve these problems, a novel fusion algorithm based on robust principal component analysis (RPCA) and guided filter is proposed. The guided filter can preserve the edges effectively, which is often used to enhance the images without distort the details. Considering edges and flat area are treated differently by the guided filter, in this paper, sparse component of the source image is filtered by the guided filter to generate the enhanced image which contains the preserved edges and the enhanced background. And then the focused regions of the source images are detected by spatial frequency map of the difference images between the enhanced image and the corresponding source image. Finally, morphological algorithm is used to obtain precise fusion decision map. Experimental results show that the proposed method improves the fusion performance obviously which outperforms the current fusion methods.     

基于三维形状指数的肺结节自动检测方法

针对在肺结节计算机辅助检测中存在误诊率、假阳性率较高,检测准确率较低等问题,提出一种基于三维形状指数和Hessian矩阵特征值构建类球形滤波器的结节检测方法。首先,提取肺实质区域,并计算各体素点Hessian矩阵的特征值和特征向量;其次,通过二维形状指数推导出三维形状指数公式,构建改进的三维类球形滤波器;最后,在三维肺实质区域内检测疑似结节区域,去除较多的假阳性区域,针对三维体数据上检测出结节所在位置,将检测到的坐标作为置信连接的多种子点输入,进行三维体数据分割,最终分割出三维结节。实验结果表明,所提算法能够有效地检测出不同类型的肺结节,对较难检测的磨玻璃结节也有较好的检测效果,结节检测的假阳性低,最终能达到92.36%的准确率和96.52%的敏感度。     

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.     

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。     

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.     

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.     

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     

基于边长的三维形状插值

形状插值在计算机图形学和几何处理中是一个极其重要而基础的问题,在计算机动画等领域有 着广泛应用。注意到在平面三角网格和三维四面体网格插值问题中,对边长平方插值等价于对回拉度量进行插 值,因此具有等距扭曲和共形扭曲同时有界的良好性质。通过将其推广至曲面三角网格,提出了一种完全基于 边长的曲面三角网格插值算法。给定边长,在重建网格阶段,使用牛顿法对边长误差能量进行优化。并且给出 了其海森矩阵的解析正定化形式,从而避免了高代价的特征值分解步骤。注意到四面体网格的边长平方插值结 果具有极低曲率,意味着只需少许修改即可将其压平从而嵌入三维空间。因此提出先将曲面三角网格四面体化, 再从四面体网格的插值结果提取表面。然后将这表面作为初始化用于边长误差能量的牛顿迭代,从而使得收敛 结果更加接近全局最优。在一系列三角网格上进行了实验,结果说明了本文方法比之前方法的边长误差更小, 且得到的结果还是有界扭曲的。     

Efficient 3D reflection symmetry detection: A view-based approach

Symmetries exist in many 3D models while efficiently finding their symmetry planes is important and useful for many related applications. This paper presents a simple and efficient view-based reflection symmetry detection method based on the viewpoint entropy features of a set of sample views of a 3D model. Before symmetry detection, we align the 3D model based on the Continuous Principal Component Analysis (CPCA) method. To avoid the high computational load resulting from a directly combinatorial matching among the sample views, we develop a fast symmetry plane detection method by first generating a candidate symmetry plane based on a matching pair of sample views and then verifying whether the number of remaining matching pairs is within a minimum number. Experimental results and two related applications demonstrate better accuracy, efficiency, robustness and versatility of our algorithm than state-of-the-art approaches.     

融合稀疏点云补全的3D目标检测算法

基于雷达点云的3D目标检测方法有效地解决了RGB图像的2D目标检测易受光照、天气等因素影响的问题.但由于雷达的分辨率以及扫描距离等问题,激光雷达采集到的点云往往是稀疏的,这将会影响3D目标检测精度.针对这个问题,提出一种融合稀疏点云补全的目标检测算法,采用编码、解码机制构建点云补全网络,由输入的部分稀疏点云生成完整的密...     

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Ga-bor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.     

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.     

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...