Asif Masood

Optimized polygonal approximation by dominant point deletion

An algorithm for polygonal approximation based on dominant point (DP) deletion is presented in this paper. The algorithm selects an initial set of DPs and starts eliminating them one by one depending upon the error associated with each DP. The associated error value is based on global measure. A local optimization of few neighboring points is performed after each deletion. Although the algorithm does not guarantee an optimal solution, the combination of local and global optimization is expected to produce optimal results. The algorithm is extensively tested on various shapes with varying number of DPs and error threshold. In general, optimal results were observed for about 96% of the times. A good comparative study is also presented in this paper

Dominant point detection by reverse polygonization of digital curves

A polygonal approximation technique using reverse polygonization is presented in this paper. The reverse polygonization starts from an initial set of dominant points i.e. break points. In that, dominant points are deleted (one in each iteration) such that the maximal perpendicular distance of approximating straight line from original curve is minimal. A comparative study with some commonly referred algorithms is also presented, which shows that this technique can produce better approximation results. The algorithm has additional advantages like simplicity, polygonal approximation with any number of dominant points and up to any error value, and computational efficiency.

A novel approach to polygonal approximation of digital curves

A new approach to polygonal approximation is presented in this paper. It starts from an initial set of dominant points (break points), where the integral square error from a given shape is zero. The proposed algorithm iteratively deletes most redundant dominant points till required approximation is achieved. Stabilization algorithm after elimination of each dominant point ensures high quality of approximation. Results of proposed algorithm are compared with classical algorithms. The proposed algorithm has additional benefits like polygonal approximation with any number of dominant points and up to any error value and robustness of results.

Technical Section: Corner detection by sliding rectangles along planar curves

A new corner detector for planar curves is presented in this paper. This algorithm finds corner by sliding set of three rectangles along the curve and counting number of contour points lying in each rectangle. This structure incorporates more than one view of given shape which is a key to find all corners successfully. Proposed technique was found very consistent with human vision system. This is an efficient method, as it does not involve calculation of cosine angle and curvature. Criteria for evaluation of corner detection algorithms are proposed in this paper. A comparative study of six corner detectors (including proposed algorithm) is also presented. This technique is very useful to detect corners from noisy shapes and natural object boundaries.

Capturing outlines using cubic Bezier curves

Capturing outlines is a useful method for shape compression and digitization for computer storage and for subsequent computational efficiency. This work presents an optimal cubic Bezier curve design, which is particularly useful for capturing outlines of 2D shapes including hand-drawn shapes and fonts. The methodology, discussed in this paper differs from traditional approaches in that, it is not aimed to find characteristic points, which are interpolated to get approximated curve. The proposed method is used to find suitable location of control points for any curve such that a simple Bezier curve generated over these control points approximates the original curve.

A locally constrained radial basis function for registration and warping of images

The work in this paper presents a non-rigid registration approach using proposed radial basis function (RBF). The RBF is based on locally constrained cosines. The proposed function is designed to overcome the weaknesses, observed in previous RBFs. The criteria to evaluate the accuracy of transformation functions are also proposed in this paper. Results of the proposed RBF are analyzed and compared with the existing RBFs, based on the proposed evaluation criteria and some other similarity measures. We demonstrate the applicability of proposed approach for registration of medical images and image warping.

Technical Section: Capturing outlines of planar images using Bézier cubics

A technique has been designed for capturing outlines by exploiting the properties of cubic Bezier curves. The planar images are used to extract the outline. The outline is divided into curve segments at high curvature points and each segment is processed independently for piecewise curve approximation. The proposed algorithm avoids the traditional method of least square fitting which is time consuming and presents a simple and efficient approach to find the control points Bezier curves. Recursive segment subdivision is used to keep the approximation error within specified threshold limits. The ideas of interpolation, control point estimation and optimization of approximation curve are used. A comparative study of results is also provided at the end and various benefits of the proposed algorithm like computational efficiency, accurate representation and low approximation error are highlighted.

Piecewise polygonal approximation of digital curves

In this paper, we present a recursive algorithm for piecewise polygonal approximation of a digital curves. The idea behind is to look for an optimal solution while approximating the given curve segment with a set of longest and minimum line segments such that the maximum squared error is less than the given threshold. The given curve is divided into pieces and parallel processing can be applied to each piece of curve thus making it computationally more efficient. Experimental results show that the proposed method has promising results.

Capturing outlines of 2D objects with Bézier cubic approximation

An object capturing technique using cubic Bezier is presented in this paper. Proposed technique produces set of data points which are the control points of approximating Bezier curve. The control points are determined by an efficient search algorithm producing optimal curves. Approximation process is simplified by decomposition of outline into smaller curves. The decomposition/subdivision is performed on detected corner points as a preprocessing step. Further subdivision is done by recursive algorithm during the approximation process. Proposed algorithm has various advantages like computational efficiency, better shape representation, low approximation error and high compression ratio. This is demonstrated in comparison with other algorithms.

Host based intrusion detection using RBF neural networks

A novel approach of host based intrusion detection is suggested in this paper that uses Radial basis Functions Neural Networks as profile containers. The system works by using system calls made by privileged UNIX processes and trains the neural network on its basis. An algorithm is proposed that prioritize the speed and efficiency of the training phase and also limits the false alarm rate. In the detection phase the algorithm provides implementation of window size to detect intrusions that are temporally located. Also a threshold is implemented that is altered on basis of the process behavior. The system is tested with attacks that target different intrusion scenarios. The result shows that the radial Basis Functions Neural Networks provide better detection rate and very low training time as compared to other soft computing methods. The robustness of the training phase is evident by low false alarm rate and high detection capability depicted by the application