This paper proposes an online learning boosting method based on kernel regression for robust visual tracking. Although
much progress has been made in using boosting for tracking, it remains a big challenge to get a robust tracker that is
insensitive to illumination change, clutter, object deformation, and occlusion. In this paper, we use a nonlinear version of
the recursive least square (RLS) algorithm so as to derive weak classifiers for visual tracking, which performs linear
regression in a high-dimensional feature space induced by a Mercer kernel. In order to alleviate the computational
burden and increase efficiency, we apply online sparsification to filter samples in feature space. In our boosting
framework, adaptive linear weak classifiers are performed, the form of which is modified adaptively to cope with scene
changes in every frame. Experimental results demonstrate that our proposed method has advantages in dealing with
complex background in visual tracking, and often outperforms the state of the art on the popular datasets.
This paper proposes a novel level set based image segmentation method by use of image second statistics and
logarithmic Euclidean metric. Different from previous tensor based image segmentation approaches, the proposed
method adopts covariance feature as region-level descriptor rather than pixel-level one. On the basis of feature image, we
utilize second order statistics of image feature, i.e., covariance matrix, to model image region representation, which is of
low dimension, invariant to uniform illumination change, insensitive to noise, and more importantly provide a natural
mechanism of incorporating different types of image features by modeling their correlations. We model image
segmentation problem as one finding the optimal segmentation that maximizes the covariance distance between
foreground region and background region. Typically, covariance matrices do not lie on Euclidean space. Our solution to
this is to exploit logarithmic Euclidean distance as a metric to compute the similarity between two matrices. The
experimental results show that covariance matrix as region descriptor do form an effective representation for image
segmentation problems, and the proposed image energy can be used to segment images and extract object boundaries
reliably and accurately.
KEYWORDS: 3D modeling, Principal component analysis, Head, Solid modeling, Matrices, Feature extraction, 3D scanning, Data modeling, Optical spheres, 3D image processing
With the general availability of 3D digitizers and scanners, 3D graphical models have been used widely in a variety of applications. This has led to the development of search engines for 3D models. Especially, 3D head model classification and retrieval have received more and more attention in view of their many potential applications in criminal identifications, computer animation, movie industry and medical industry. This paper addresses the 3D head model classification problem using 2D subspace analysis methods such as 2D principal component analysis (2D PCA[3]) and 2D fisher discriminant analysis (2DLDA[5]). It takes advantage of the fact that the histogram is a 2D image, and we can extract the most useful information from these 2D images to get a good result accordingingly. As a result, there are two main advantages: First, we can perform less calculation to obtain the same rate of classification; second, we can reduce the dimensionality more than PCA to obtain a higher efficiency.
Light scattering constant and scattering cross sections of human white blood cells were studied and measured. The functional relationship of cell number density concentration hematocrit and the intensities of scattered and transmitted was deduced. It is distinctive in this study that the optical parameters are represented by the logarithm of the ratio of scattered intensity to transmitted intensity, also by the logarithm of transmitted intensity as well.
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