Kernel covariance image region description for object tracking

This paper describes a novel approach for creating an efficient, general, and differentiable parameterization of large-scale non-Gaussian, non-stationary random fields (represented by multipoint geostatistics) that is capable of reproducing complex geological structures such as channels. Such parameterizations are appropriate for use with gradient-based algorithms applied to, for example, history-matching or uncertainty propagation. It is known that the standard Karhunen–Loeve (K–L) expansion, also called linear principal component analysis or PCA, can be used as a differentiable parameterization of input random fields defining the geological model. The standard K–L model is, however, limited in two respects. It requires an eigen-decomposition of the covariance matrix of the random field, which is prohibitively expensive for large models. In addition, it preserves only the two-point statistics of a random field, which is insufficient for reproducing complex structures. In this work, kernel PCA is applied to address the limitations associated with the standard K–L expansion. Although widely used in machine learning applications, it does not appear to have found any application for geological model parameterization. With kernel PCA, an eigen-decomposition of a small matrix called the kernel matrix is performed instead of the full covariance matrix. The method is much more efficient than the standard K–L procedure. Through use of higher order polynomial kernels, which implicitly define a high-dimensionality feature space, kernel PCA further enables the preservation of high-order statistics of the random field, instead of just two-point statistics as in the K–L method. The kernel PCA eigen-decomposition proceeds using a set of realizations created by geostatistical simulation (honoring two-point or multipoint statistics) rather than the analytical covariance function. We demonstrate that kernel PCA is capable of generating differentiable parameterizations that reproduce the essential features of complex geological structures represented by multipoint geostatistics. The kernel PCA representation is then applied to history match a water flooding problem. This example demonstrates that kernel PCA can be used with gradient-based history matching to provide models that match production history while maintaining multipoint geostatistics consistent with the underlying training image.

This paper presents a novel Gabor-based kernel Principal Component Analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.

We propose a new kernel entropy component analysis approach to object recognition with image set by fusing the information entropy. Since the geometry of Symmetric Positive Definite (SPD) matrices, we model the image set with its covariance matrix (nonsingular). Thus the object recognition with image set can be model as classifying problem on the riemannian manifold space. Given the proper kernel function derived from efficient metric for SPD matrices, points lie on manifold space represented by covariance matrices can be cast into high dimensional euclidean space. In this euclidean space, traditional methods used for dimensional reduction can be applied directly. Accounting for the measurability to information of the Renyi entropy, we fuse the information entropy to the dimensional reduction progress of our method. Similar to Kernel Principal Component Analysis (KPCA), the method accomplish data dimensionality reduction by projection onto a subset of entropy preserving KPCA axes. But this subset does not need to correspond to the top eigenvalues of the kernel matrix. The positive results of experiment on datasets demonstrated effectiveness of our method.

Principal component analysis (PCA) is linear method for feature extraction that is known as Karhonen Loove method. PCA was first proposed to recognize face by Turk and Pentland, and was also known as eigenface in 1991 [Turk, 1991]. However, PCA has some weaknesses. The first, it cannot capture the simplest invariance of the face image [Arif et al., 2008b] , when this information is not provided in the training data. The last, the result of feature extraction is global structure [Arif, 2008]. The PCA is very simple, has overcome curse of dimensionality problem, this method have been known and expanded by some researchers to recognize face such as Linear Discriminant Analysis (LDA)[Yambor, 2000; A.M. Martinez, 2003; J.H.P.N. Belhumeur 1998], Linear Preserving Projection that known Lapalacianfaces [Cai, 2005; Cai et al, 2006; Kokiopoulou, 2004; X. He et al., 2005], Independent Component Analysis, Kernel Principal Component Analysis [Scholkopf et al., 1998; Sch olkopf 1999], Kernel Linear Discriminant Analysis (KLDA) [Mika, 1999] and maximum feature value selection of nonlinear function based on Kernel PCA [Arif et al., 2008b]. As we know, PCA is dimensionality reduction method based on object appearance by projecting an original ndimensional (row*column) image into k eigenface where k<<n. Although PCA have been developed into some methods, but in some cases, PCA can outperform LDA, LPP and ICA when it uses small sample size.

Modeling Self-Occlusions/Disocclusions in Dynamic Shape and Appearance Tracking for Obtaining Precise Shape We present a method to determine the precise shape of a dynamic object from video. This problem is fundamental to computer vision, and has a number of applications, for example, 3D video/cinema post-production, activity recognition and augmented reality. Current tracking algorithms that determine precise shape can be roughly divided into two categories: 1) Global statistics partitioning methods, where the shape of the object is determined by discriminating global image statistics, and 2) Joint shape and appearance matching methods, where a template of the object from the previous frame is matched to the next image. The former is limited in cases of complex object appearance and cluttered background, where global statistics cannot distinguish between the object and background. The latter is able to cope with complex appearance and a cluttered background, but is limited in cases of camera viewpoint change and object articulation, which induce self-occlusions and self-disocclusions of the object of interest. The purpose of this thesis is to model self-occlusion/disocclusion phenomena in a joint shape and appearance tracking framework. We derive a non-linear dynamic model of the object shape and appearance taking into account occlusion phenomena, which is then used to infer self-occlusions/disocclusions, shape and appearance of the object in a variational optimization framework. To ensure robustness to other unmodeled phenomena 5 that are present in real-video sequences, the Kalman filter is used for appearance updating. Experiments show that our method, which incorporates the modeling of self-occlusion/disocclusion, increases the accuracy of shape estimation in situations of viewpoint change and articulation, and out-performs current state-of-the-art methods for shape tracking.

Design of conventional correlation filters requires explicit knowledge of the appearance and shape of a target object, so the performance of correlation filters is significantly affected by changes in the appearance of the object in the input scene. In particular, the performance of correlation filters worsens when objects to be recognized are partially occluded by other objects, and the input scene contains a cluttered background and noise. In this paper, we propose a new algorithm for the design of a system consisting of a set of adaptive correlation filters for recognition of partially occluded objects in noisy scenes. Since the input scene may contain different fragments of the target, false objects, and background to be rejected, the system is designed in such a manner to guarantee equally high correlation peaks corresponding to parts of the target in the scenes. The key points of the system are as follows: (i) it consists of a bank of composite optimum filters, which yield the best performance for different parts of the target; (ii) it includes a fragmentation of the target into a given number of parts in the training stage to provide equal intensity responses of the system for each part of the target. With the help of computer simulation, the performance of the proposed algorithm for recognition partially occluded objects is compared with that of common algorithms in terms of objective metrics.

This paper discusses the effects of introducing nonlinear interactions and noise-filtering to the covariance matrix used in Markowitz’s portfolio allocation model, evaluating the technique’s performances for daily data from seven financial markets between January 2000 and August 2018. We estimated the covariance matrix by applying Kernel functions, and applied filtering following the theoretical distribution of the eigenvalues based on the Random Matrix Theory. The results were compared with the traditional linear Pearson estimator and robust estimation methods for covariance matrices. The results showed that noise-filtering yielded portfolios with significantly larger risk-adjusted profitability than its non-filtered counterpart for almost half of the tested cases. Moreover, we analyzed the improvements and setbacks of the nonlinear approaches over linear ones, discussing in which circumstances the additional complexity of nonlinear features seemed to predominantly add more noise or predictive performance.

A new modeling and monitoring approach for multi-mode processes is proposed. The method of similarity measure(SM) and kernel principal component analysis (KPCA) are integrated to construct SM-KPCA monitoring scheme, where SM method serves as the separation of common subspace and specific subspace. Compared with the traditional methods, the main contributions of this work are: 1) SM consisted of two measures of distance and angle to accommodate process characters. The different monitoring effect involves putting on the different weight, which would simplify the monitoring model structure and enhance its reliability and robustness. 2) The proposed method can be used to find faults by the common space and judge which mode the fault belongs to by the specific subspace. Results of algorithm analysis and fault detection experiments indicate the validity and practicability of the presented method.

Object based video retrieval with local region tracking

Visual tracking is a challenging issue in surveillance, human–computer Interaction, and intelligent robotics, among others. Managing appearance changes of the target object, illumination changes, rotations, non-rigid deformations, partial or full occlusions, background clutter, fast motion, and so forth is generally difficult. Among the numerous existing trackers, the correlation-filter-based tracker can achieve appealing performance with a fast speed for fast Fourier transform. Motivated by this property, the spatio-temporal context (STC) learning algorithm was proposed with the consideration of the information from the context around the target, and this algorithm achieved good results. However, STC only utilizes the overall intensity information. In this paper, we propose a multi-channel features STC learning algorithm with an improved scale-adaptive scheme. Our algorithm integrates powerful features, including Histogram of Oriented Gradients and color naming, using kernel methods on the basis of the STC algorithm to further enhance the overall tracking performance. Extensive experimental results obtained from various benchmark data sets demonstrate that the proposed tracker is promising for various challenging scenarios and maintains real-time performance at an average speed of 78 frames/s. According to the test results, our algorithm outperforms the STC algorithm and achieves performance that is competitive with the state-of-the-art algorithms.

This paper examines the Kernel Principal Component Analysis (KPCA) feature detection and classification for underwater images. In Underwater images the numbers of distortion occurred are blurring of image, illumination of light and rotation of angle, noise etc. Features are normally extracted by the method called SIFT (Scale Invariant Feature Transform for underwater images). It is used for extracting distinctive invariant features from images that can be invariant to image scale and rotation. It is used in image mosaic, recognition, retrieval and etc. where PCA-SIFT (Principal Component Analysis-Scale Invariant Feature Transform) is also used for dimension reduction and feature detection for underwater images. In this paper we propose a method by combining KPCA and SIFT together called KPCA-SIFT feature detection for underwater images. It is well suited for blur, illumination change and rotation of the input image. When apply PCA to the normalized gradient patch it reduces the dimension of the feature extracted. The parameters used for evaluation are Precision Vs Recall curve and the parameter like elapsed time, sigma value and threshold value. PNN classification is used for underwater images. The proposed method gives desirable results with respect to parameters like Roc curve, Precision Vs Recall, Elapsed time and sigma value. It is more robust and distinctive image deformation and more compact. with increased accuracy and faster matching than the PCA SIFT and SIFT algorithms.

Thermal infrared (TIR) tracking can be utilized to track the target in the images generated by thermal infrared sensors due to the weak influence by illumination changes. However, there are still some challenges to do thermal infrared tracking when suffering drastic appearance variation, heavy occlusion and background clutters. The absence of RGB patterns and low resolution also constrain the tracking performance in complex scenarios. The deep convolutional features are widely utilized to solve visual tracking problems which successfully extracted the spatial and semantic information though object representation. Motivated by these methods, we firstly propose to combine multi-stages cascaded Siamese networks to achieve deep features fusion in three stages, then achieve the tracking procedure by candidates matching strategy. The final results are obtained by non-maximum suppression and scale penalty. The proposed method can inherit the advantages by fusing multi-stages deep features and achieve end-to-end learning simultaneously. The experiments are evaluated with state-of-the-art methods on VOT-TIR2016 benchmark and attributes based comparison. The tracking results demonstrate that our proposed method outperforms the compared methods in terms of accuracy and robustness.

Video object segmentation and tracking are two essential building blocks of smart surveillance systems. However, there are several issues that need to be resolved. Threshold decision is a difficult problem for video object segmentation with a multi-background model. In addition, some conditions make robust video object tracking difficult. These conditions include nonrigid object motion, target appearance variations due to changes in illumination, and background clutter. In this paper, a video object segmentation and tracking framework is proposed for smart cameras in visual surveillance networks with two major contributions. First, we propose a robust threshold decision algorithm for video object segmentation with a multi-background model. Second, we propose a video object tracking framework based on a particle filter with the likelihood function composed of diffusion distance for measuring color histogram similarity and motion clue from video object segmentation. The proposed framework can track nonrigid moving objects under drastic changes in illumination and background clutter. Experimental results show that the presented algorithms perform well for several challenging sequences, and our proposed methods are effective for the aforementioned issues.

Algorithms for several emerging large-scale problems in cheminformatics have as their rate-limiting step the evaluation of relatively slow chemical similarity measures, such as structural similarity or three-dimensional (3-D) shape comparison. In this article we present SCISSORS, a linear-algebraical technique (related to multidimensional scaling and kernel principal components analysis) to rapidly estimate chemical similarities for several popular measures. We demonstrate that SCISSORS faithfully reflects its source similarity measures for both Tanimoto calculation and rank ordering. After an efficient precalculation step on a database, SCISSORS affords several orders of magnitude of speedup in database screening. SCISSORS furthermore provides an asymptotic speedup for large similarity matrix construction problems, reducing the number of conventional slow similarity evaluations required from quadratic to linear scaling.

We propose a novel method for the illumination robust template matching in unconstrained environments, named illumination robust template matching (IRTM). IRTM is based on the patch diversity of the Nearest-Neighbor. A core component is to calculate the similarity between patch pairs. Without illumination change, the Euclidean distance of appearance and geometric features jointly measure the similarity well. However, the illumination change will significantly affect the confidence of measure. In this paper, based on the relative pixel values that do not change with illumination, we define an illumination-robust distance function. With experiments, IRTM is shown to be a well-performing similarity measure against illumination changes, non-rigid geometric deformations, as well as background clutter, and occlusions.