Object recognition in images involves identifying objects with partial occlusions, viewpoint changes, varying illumination, cluttered backgrounds. Recent work in object recognition uses machine learning techniques SVM-KNN, Local Ensemble Kernel Learning, Multiple Kernel Learning. In this paper, we want to utilize SVM as week learners in AdaBoost. Experiments are done with classifiers like neare...

Computational steering is a valuable mechanism for scientific investigation in which the parameters of a running program can be altered and the results visualized immediately. In an earlier paper we put forward an architecture for computational steering that recognises visualization as both the logical output and the input route. In this paper we investigate the practicalities of applying this ...

In this paper, kinetic and kinematic modeling of a 4 wheel steering vehicle is done and its movement is controlled in an optimal way using Linear Quadratic Regulator (LQR). The results are compared with the same control of two-wheel steering case and the advantages are analyzed. In 4 wheel steering vehicles which are nowadays more applicable the number of controlling actuators are more than the...

‎In this article‎, ‎we develop two nonparametric smoothing estimators for parameter of a time-variant parametric model‎. ‎This parameter can be from any parametric family or from any parametric or semi-parametric regression model‎. ‎Estimation is based on a two-step procedure‎, ‎in which we first get the raw estimate of the parameter at a set of disjoint time...

This paper describes an investigation into using kernel methods for extracting semantic information from images. The specific problem addressed is the local extraction of ‘man-made’ vs ‘natural’ information. Kernel linear discriminant and support vector methods are compared to the standard linear discriminant using a multi-level hierarchy. The two kernel methods are found to perform similarly a...

As sample quantiles can be obtained as maximum likelihood estimates of location parameters in suitable asymmetric Laplace distributions, so kernel estimates of quantiles can be obtained as maximum likelihood estimates of location parameters in a general class of distributions with simple exponential tails. In this paper, this observation is applied to kernel quantile regression. In so doing, a ...

Functional data analysis commonly relies on the incorporation of basis functions having subject-specific coefficients, with the choice of basis and random effects distribution important. To allow the random effects distribution to be unknown, while inducing subject-specific basis selection and local borrowing of information across subjects, this article proposes a kernel local partition process...