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This paper presents a method that embeds stability-sensitive filter in the temporal matching kernel with explicit feature maps (TE). The added filter improves the robustness of TE to noise for content-based video retrieval. Originally TE embeds temporal information of frame descriptors by using explicit feature mapping in a fixed length video vector by using a temporal invariant match kernel. TE matches...
Singular value decomposition (SVD) has been used widely in the literature to recover the missing entries of a matrix. The basic principle in such methods is to assume that the correlated data is distributed with a low-rank structure. The knowledge of the low-rank structure is then used to predict the missing entries. SVD is based on the assumption that the data (user ratings) are distributed on a...
Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable...
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision. As soon as more than two viewpoints are available, one faces the very basic question how to measure similarity across >2 image patches. Surprisingly, no direct solution exists, instead it is common to fall back to more or less robust averaging of two-view similarities. Encouraged by the success of machine...
The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convolution with additive Gaussian noise. However, including even a small number of outliers to this model in the kernel estimation process can significantly reduce the resulting image quality...
Convolutional neural networks (CNNs) provide the current state of the art in visual object classification, but they are far less accurate when classifying partially occluded objects. A straightforward way to improve classification under occlusion conditions is to train the classifier using partially occluded object examples. However, training the network on many combinations of object instances and...
With the growing number of automated welding systems present throughout manufacturing, achieving high precision is naturally a key objective. The alignment of weld tip to weld seam, particularly in very long welds (such as in pipes), is a technical challenge in which computer vision has much to offer. This paper introduces a real-time methodology for weld-seam tracking. The key challenge associated...
Representation of data is very important in case of machine learning. Better the representation, the classifiers will give better results. Contractive autoencoders are used to learn the representation of data which are robust to small changes in the input. This paper uses contractive autoencoder and SVM classifier for handwritten Devanagari numerals recognition. The accuracy obtained using CAE+SVM...
In this paper, we propose a novel point set matching algorithm to improve the matching precision in the presence of non-Gaussian noises and outliers. In our method, a non-second order similarity measure known as Kernel Mean p-Power Error (KMPE) loss is employed as the matching cost function. We introduce a local optimal solution for computing the rigid transform by repeating the correspondence estimation...
Crowd counting on still images is very challenging due to heavy occlusions and scale variations. In this paper, we aim to develop a method that can accurately estimate the crowd count from a still image. Recently, convolutional neural networks have been shown effective in many computer vision tasks including crowd counting. To this end, we propose a fully convolutional network (FCN) architecture to...
Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense...
In this paper, we aim to improve the overall performance of kernel adaptive filters by adaptively combining several component filters with different parameters setting in the practical applications. The convex combination scheme is exploited to incorporate any two parallel diversity branches which could be the component filter or the output of previous combination layer. The proposed convex combination...
Deblurring images with outliers has attracted considerable attention recently. However, existing algorithms usually involve complex operations which increase the difficulty of blur kernel estimation. In this paper, we propose a simple yet effective blind image deblurring algorithm to handle blurred images with outliers. The proposed method is motivated by the observation that outliers in the blurred...
This paper presents the time series cluster kernel (TCK) for multivariate time series with missing data. Our approach leverages the missing data handling properties of Gaussian mixture models (GMM) augmented with empirical prior distributions. Further, we exploit an ensemble learning approach to ensure robustness to parameters by combining the clustering results of many GMM to form the final kernel...
Unlike Support Vector Machine (SVM), Kernel Minimum Classification Error (KMCE) training frees kernels from training samples and jointly optimizes weights and kernel locations. Focusing on this feature of KMCE training, we propose a new method for developing compact (small scale but highly accurate) kernel classifiers by applying KMCE training to support vectors (SVs) that are selected (based on the...
Common spatial patterns (CSP) is a widely used method in the field of electroencephalogram (EEG) signal processing. The goal of CSP is to find spatial filters that maximize the ratio between the variances of two classes. The conventional CSP is however sensitive to outliers because it is based on the L2-norm. Inspired by the correntropy induced metric (CIM), we propose in this work a new algorithm,...
Recently, kernelized correlation Filter-based trackers have aroused the interest of many researchers and achieved good results in the field of tracking. However, the current tracking model based on kernelized correlation filters can not deal with the changes of the target appearance and scale effectively. Therefore, in this paper, we intend to solve these two problems and improve the robustness of...
The problem of securely storing and processing a hierarchy of sensitive data is of paramount importance in many sectors, like Defense or Critical Infrastructures, just to name a few. Since years Industry and Academia have been involved in researches focused on multilevel security, formal languages, and new platform modeling frameworks. The Multiple Independent Levels of Security/Safety is a new paradigm...
Codes that aim to detect any error regardless of its multiplicity are referred to as security oriented codes. Most of these codes are designed to protect uniformly distributed codewords; there are few solutions which are used in protecting systems with non-uniformly distributed words. The paper introduces a new encoding method, termed “Level-Out encoding”, for cases in which some words are more likely...
Dependence-maximization clustering is another line of clustering framework, which clusters samples by maximizing the statistical dependence on samples in the same group. Recently, dependence-maximization clustering method based on least-squares quadratic mutual information (LSQMI), called LSQMI based clustering (LSQMIC), was proposed. A notable advantage of LSQMIC over other dependence-maximization...
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