The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper proposes a novel kernel similarity modeling of texture pattern flow (KSM-TPF) for background modeling and motion detection in complex and dynamic environments. The texture pattern flow encodes the binary pattern changes in both spatial and temporal neighborhoods. The integral histogram of texture pattern flow is employed to extract the discriminative features from the input videos. Different...
Dataset shift from the training data in a source domain to the data in a target domain poses a great challenge for many statistical learning methods. Most algorithms can be viewed as exploiting only the first-order statistics, namely, the empirical mean discrepancy to evaluate the distribution gap. Intuitively, considering only the empirical mean may not be statistically efficient. In this paper,...
This paper proposes a new tracking algorithm which combines object and background information, via building object and background appearance models simultaneously by non-parametric kernel density estimation. The major contribution is a novel bidirectional learning framework for discrimination between the object and background. It has the following advantages: 1) it embeds background information, unlike...
Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study used three strategies to construct the hybrid FSVM-based credit scoring models to evaluate the applicant's credit...
The kernel function and parameters selection is a key problem in the research of support vector machine. After discussing the influence of support vector machine on kernel parameters and error penalty factors, a new kernel function CombKer was proposed and constructed. The CombKer kernel function is a kind of combination kernel function, which combines the Gaussian RBF kernel function that has the...
In this paper, a novel adaptive bandwidth mean shift algorithm toward 2D object detection (ABMSOD) is proposed. It can not only identify whether an object of certain classes exists or not, but also get the scale and orientation besides position very fast. The feature histogram weighted by a kernel with adaptive bandwidth is used for representing the target object model and the candidate object model...
In this paper, a novel adaptive bandwidth mean shift algorithm toward 2D object tracking is proposed. It can simultaneously tracks the scale and orientation besides position in real time. The feature histogram weighted by a kernel with adaptive bandwidth is used for representing the target and the candidate target. The similarity of the target model and the candidate model is measured by the Bhattacharyya...
We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector set with a Gaussian mixture model (GMM) and to compute a probabilistic measure of similarity between the GMMs. The main contribution of this paper is to model each vector set with a GMM adapted from a common ldquouniversalrdquo...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.