Spare L1-norm-based maximum margin criterion

Maximum margin criterion (MMC) is a popular method for dimensionality reduction or feature extraction. MMC can alleviate the small size sample (SSS) problem encountered by linear discriminant analysis (LDA) and extract more discriminant vectors than LDA. However, the objective function of MMC is derived from L2-norm, which makes MMC be sensitive to noise and outliers. Besides, the basis vectors of MMC are dense, which makes it hard to explain the obtained features. To address the drawbacks of MMC, in this paper, we propose a novel sparse L1-norm-based maximum margin criterion (SMMC-L1). L1-norm rather than L2-norm is used in the objective function of SMMC-L1. Besides, L1-norm is also used as a lasso penalty to regularize the basis vectors. An iterative algorithm for solving SMMC-L1 is proposed. Experiment results on some databases show the effectiveness of the proposed SMMC-L1.     

Kernel-based feature extraction under maximum margin criterion

In this paper, we study the problem of feature extraction for pattern classification applications. RELIEF is considered as one of the best-performed algorithms for assessing the quality of features for pattern classification. Its extension, local feature extraction (LFE), was proposed recently and was shown to outperform RELIEF. In this paper, we extend LFE to the nonlinear case, and develop a new algorithm called kernel LFE (KLFE). Compared with other feature extraction algorithms, KLFE enjoys nice properties such as low computational complexity, and high probability of identifying relevant features; this is because KLFE is a nonlinear wrapper feature extraction method and consists of solving a simple convex optimization problem. The experimental results have shown the superiority of KLFE over the existing algorithms.     

局部二值模式超声斑点降噪

斑点噪声压缩和组织结构保留对超声图像的临床诊断非常重要,因此提出一种基于改良局部二值模式的超声斑点降噪方法。该方法通过计算像素点的局部二值模式值,将超声图像归类为斑点噪声和组织结构,并用双边滤波滤除噪声。物理体膜图像和人类肝脏图像的实验结果表明该方法可以有效滤除斑点噪声和保持图像边缘。     

基于二维最大边缘准则和DCT的人脸识别方法

提出了一种基于离散余弦变换(DCT)和二维最大边缘准则(2DMMC)的2DDM特征提取算法,证明了2DMMC可以直接应用于DCT域,利用欧氏距离测度进行分类的结果与在空域中进行得到的结果完全相同。2DMMC方法可直接应用于基于DCT压缩的JPEG格式的图像。在ORL和Yale人脸数据库上的实验结果表明,在空域2DMMC的识别率高于2DPCA和2DLDA,2DDM的识别率又高于2DMMC,而且2DDM的耗时要低于2DMMC。     

最大边缘准则图形嵌入在人脸识别中的应用

局部线性嵌入是一种有效地非线性维数约减方法,它能保持降维后的数据与原空间有相同的拓扑关系。但是这种方法在降维处理、可视化以及数据分类方面应用不是很广泛,针对上述问题,提出了一种新的、有效的降维以及数据分类方法——基于最大边缘准则图形嵌入方法。该方法首先构建最近邻关系图聚合数据点之间的最近邻样本,同时最大化类间间隔,保证不同类之间数据可分性大,从而更好地实现数据分类。最后,该方法的有效性分别在ORL及Yale两大人脸库上得到了验证。     

Optimization of signal-code constructions using the maximum efficiency criterion

Signal-code constructions based on some known and new formats of digital signal modulation have been studied. It was established that the signal-code constructions based on the amplitude modulation of many components were characterized by larger information efficiency as compared with the case of using the quadrature amplitude modulation.     

Face recognition algorithm using local and global information

A successful algorithm for the recognition of partially distorted human face images is presented. The key to this approach is to combine both local and global information. The results are compared with traditional elastic matching algorithms and a decreased mismatch rate is reported     

局部阴影下光伏阵列全局最大功率跟踪技术研究

光伏发电系统中,光伏阵列在受到阴影遮挡时,其输出功率和电压特性不再是单一电池板的单峰值特性,采用以往的MPPT算法会使系统陷入局部的最大功率点。为了实现多峰值最大功率点的跟踪,文章首先建立了光伏阵列的数学模型,仿真分析了其多峰值特性。研究了一种基于POC的全局最大功率点跟踪算法,该算法从开路电压、短路电流向中间搜索,在光伏阵列输出特性的全范围内进行最大功率点的搜索,可优先避免陷入局部最大功率点的问题。     

Small vessel enhancement in MRA images using local maximum meanprocessing

The enhancement of small vessels in MRA imaging is an important problem. In this paper, we propose using local maximum mean (LMM) processing to enhance the detectability of small vessels. At each voxel in the original three-dimensional (3-D) data set, the LMM over the line segments in the cube centered at the voxel is taken and used to form the 3-D LMM data set. The maximum intensity projection (MIP) is then applied to the LMM data to produce the two-dimensional (2-D) LMM-MIP image. Through LMM processing, the variance of background tissue is reduced, thus increasing the detectability of small vessels. Moreover, the single bright voxels are suppressed and the disconnected small vessels can be connected. However, the LMM processing widens the larger, brighter vessels. To keep the advantages provided by both the LMM-MIP and MIP images, it is proposed that weight functions be used to combine them. The performance of the LMM-MIP algorithm is analyzed and compared with the performance of the MIP algorithm under three measures: The vessel voxel projection probability, the vessel receiver operating characteristic (ROC) curve and the vessel-tissue contrast-to-noise ratio (CNR). Closed forms of the three measures are obtained. It is shown that the LMM-MIP algorithm improves the detectability of small vessels under all three measures. The longer the projection path and the larger the CNR of the original data, then the greater the improvement. Confirming the theoretical analysis, results of an experiment utilizing practical MRA data demonstrate the improved visual quality of small vessels.     

Salient object detection using local,global and high contrast graphs

In this paper, we propose a novel multi-graph-based method for salient object detection in natural images. Starting from image decomposition via a superpixel generation algorithm, we utilize color, spatial and background label to calculate edge weight matrix of the graphs. By considering superpixels as the nodes and region similarities as the edge weights, local, global and high contrast graphs are created. Then, an integration technique is applied to form the saliency maps using degree vectors of the graphs. Extensive experiments on three challenging datasets show that the proposed unsupervised method outperforms the several different state-of-the-art unsupervised methods.     

Compressed domain video saliency detection using global and local spatiotemporal features

A compressed domain video saliency detection algorithm, which employs global and local spatiotemporal (GLST) features, is proposed in this work. We first conduct partial decoding of a compressed video bitstream to obtain motion vectors and DCT coefficients, from which GLST features are extracted. More specifically, we extract the spatial features of rarity, compactness, and center prior from DC coefficients by investigating the global color distribution in a frame. We also extract the spatial feature of texture contrast from AC coefficients to identify regions, whose local textures are distinct from those of neighboring regions. Moreover, we use the temporal features of motion intensity and motion contrast to detect visually important motions. Then, we generate spatial and temporal saliency maps, respectively, by linearly combining the spatial features and the temporal features. Finally, we fuse the two saliency maps into a spatiotemporal saliency map adaptively by comparing the robustness of the spatial features with that of the temporal features. Experimental results demonstrate that the proposed algorithm provides excellent saliency detection performance, while requiring low complexity and thus performing the detection in real-time.     

Passive detection of image forgery using DCT and local binary pattern

With the development of easy-to-use and sophisticated image editing software, the alteration of the contents of digital images has become very easy to do and hard to detect. A digital image is a very rich source of information and can capture any event perfectly, but because of this reason, its authenticity is questionable. In this paper, a novel passive image forgery detection method is proposed based on local binary pattern (LBP) and discrete cosine transform (DCT) to detect copy–move and splicing forgeries. First, from the chrominance component of the input image, discriminative localized features are extracted by applying 2D DCT in LBP space. Then, support vector machine is used for detection. Experiments carried out on three image forgery benchmark datasets demonstrate the superiority of the method over recent methods in terms of detection accuracy.     

Optimum selection combining using the maximum energy criterion for M-ary NCFSK in Rician fading channels

Optimum selection combining using the maximum energy (OSC-ME) criterion for M-ary noncoherent frequency shift keying signaling in independent and nonidentically distributed fading channels is examined. A weighted maximum energy selection combining (WMESC) scheme is proposed and found to provide virtually the same symbol error probability as OSC-ME in Rician fading channels. The WMESC always provides power gain over the classical selection combining regardless of diversity order and signal-to-noise ratio.     

Distortion-rate functions for vector sources and a maximum fidelity criterion

The problem of distortion-rate functions for vector sources is considered. Two fidelity criteria are treated. The first considers the maximum of the weighted component distortions and then takes the per-letter average. The second takes the maximum of the weighted component per-letter averages. In either case, a well-defined distortion-rate function results, giving the minimum possible distortion achievable at a given rate of transmission. Upper and lower bounds to these distortion-rate functions are derived in terms of more easily calculated functions.     

Fusion of face and iris biometrics using local and global feature extraction methods

Fusion of multiple biometrics combines the strengths of unimodal biometrics to achieve improved recognition accuracy. In this study, face and iris biometrics are used to obtain a robust recognition system by using several feature extractors, score normalization and fusion techniques. Global and local feature extractors are used to extract face and iris features separately, and then, the fusion of these modalities is performed on different subsets of face and iris image databases of ORL, FERET, CASIA and UBIRIS. The proposed method uses Local Binary Patterns local feature extractor and subspace Linear Discriminant Analysis global feature extractor on face and iris images, respectively. Face and iris scores are normalized using tanh normalization, and then, Weighted Sum Rule is applied for the fusion of these two modalities. Improved recognition accuracies are achieved compared to the individual systems and multimodal systems using other local or global feature extractors for both modalities.     

Visual servoing to catch fish using global/local GA search

This paper presents a vision-related technique for a real-time visual servoing. The method utilizes the global search feature of a genetic algorithm (GA) and a local search technique of the GA. In the GA process, the computation of the fitness function is based on the configuration of an object model designated as the surface-strips model. To evaluate the effectiveness of the proposed recognition method, experiments to track a fish by a hand-eye camera and catch the fish with a net attached to the hand of the manipulator have been performed.     

Saliency detection in video sequences using perceivable change encoded local pattern

The detection of salient objects in video sequence is an active computer vision research topic. One approach is to perform joint segmentation of objects and background. The background scene is learned and modeled. A pixel is classified as salient if its features do not match with the background model. The segmentation process faces many difficulties when the video sequence is captured under various dynamic circumstances. To tackle these challenges, we propose a novel local ternary pattern for background modeling. The features derived from the local pattern are robust to random noise, scale transform of intensity and rotational transform. We also propose a novel scheme for matching a pixel with the background model within a spatiotemporal domain. Furthermore, we devise two feedback mechanisms for maintaining the quality of the result over a long video. First, the background model is updated immediately based on the background subtraction result. Second, the detected object is enhanced by adjustment of the segmentation conditions in proximity via a propagation scheme. We compare our method with state-of-the-art background subtraction algorithms using various video datasets.