A Compression Scheme for Handwritten Patterns Based on Curve Fitting

An energy efficient image compression scheme for wireless multimedia sensor network using curve fitting technique

Abstract: Wireless multimedia sensor network (WMSN) comprising of miniature sensor nodes is capable of processing multimedia data traffic such as still images and video from the environment. There is a wide range of applications which get benefited from such network. Unprocessed multimedia transmission is always expensive in terms of processing power, storage, and bandwidth. So, data processing is a challenge in WMSN. Exploring low-overhead data compression technique is a solution towards this problem. In this work we propose an energy saving image compression technique for WMSN using curve fitting technique considering the application of post-disaster situation analysis through image capturing of the affected area. Upon employing the method on the macroblocks of sensory image, curve fitting coefficients are generated and transmitted towards the sink thereby saves energy by transmitting reduced volume of data. Finally the design feasibility along with simulation results including statistical analysis is presented to evaluate efficacy of the scheme in terms of two conflicting parameters viz. energy consumption and peak signal to noise ratio. The comparative results confirm our scheme’s supremacy in WMSN application domain over existing methods.

Representation of online handwriting using multi-component sinusoidal model

Abstract: The representation of online handwriting is an important aspect of handwriting applications, which involves the extraction of various spatial and temporal attributes for analysis and individualization of handwritten patterns. In this work, a model based representation is proposed for online handwriting using a multi-component sinusoidal model. The method extracts sinusoidal parameters from handwriting by modeling its horizontal and vertical velocities between each successive pair of zero crossing points with a half period of the sine function. Thus, each velocity profile is represented by the sinusoidal oscillations whose parameters are modulated at the zero-crossing points. The use of multiple oscillations to model the velocities results in a better representation of the complex trajectories. The parameters of the proposed model are computed iteratively from its residual signals. We hypothesize that the analysis of the sinusoidal components and its parameters may provide added dynamic information about the handwriting. The efficacy of the proposal is demonstrated for online signature representation and synthetic variability generation by modifying the extracted parameters. Further, the proposed feature set is also employed for online handwriting recognition task. It is observed that the multi-component sinusoidal representation combined with existing point-based features provide an improvement in the recognition performance.

A practical guide to splines

Abstract: This book is based on the author's experience with calculations involving polynomial splines. It presents those parts of the theory which are especially useful in calculations and stresses the representation of splines as linear combinations of B-splines. After two chapters summarizing polynomial approximation, a rigorous discussion of elementary spline theory is given involving linear, cubic and parabolic splines. The computational handling of piecewise polynomial functions (of one variable) of arbitrary order is the subject of chapters VII and VIII, while chapters IX, X, and XI are devoted to B-splines. The distances from splines with fixed and with variable knots is discussed in chapter XII. The remaining five chapters concern specific approximation methods, interpolation, smoothing and least-squares approximation, the solution of an ordinary differential equation by collocation, curve fitting, and surface fitting. The present text version differs from the original in several respects. The book is now typeset (in plain TeX), the Fortran programs now make use of Fortran 77 features. The figures have been redrawn with the aid of Matlab, various errors have been corrected, and many more formal statements have been provided with proofs. Further, all formal statements and equations have been numbered by the same numbering system, to make it easier to find any particular item. A major change has occured in Chapters IX-XI where the B-spline theory is now developed directly from the recurrence relations without recourse to divided differences. This has brought in knot insertion as a powerful tool for providing simple proofs concerning the shape-preserving properties of the B-spline series.

Online and off-line handwriting recognition: a comprehensive survey

Abstract: Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.

The state of the art in online handwriting recognition

Abstract: This survey describes the state of the art of online handwriting recognition during a period of renewed activity in the field. It is based on an extensive review of the literature, including journal articles, conference proceedings, and patents. Online versus offline recognition, digitizer technology, and handwriting properties and recognition problems are discussed. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined. >

Fitting B-spline curves to point clouds by curvature-based squared distance minimization

Abstract: Computing a curve to approximate data points is a problem encountered frequently in many applications in computer graphics, computer vision, CAD/CAM, and image processing. We present a novel and efficient method, called squared distance minimization (SDM), for computing a planar B-spline curve, closed or open, to approximate a target shape defined by a point cloud, that is, a set of unorganized, possibly noisy data points. We show that SDM significantly outperforms other optimization methods used currently in common practice of curve fitting. In SDM, a B-spline curve starts from some properly specified initial shape and converges towards the target shape through iterative quadratic minimization of the fitting error. Our contribution is the introduction of a new fitting error term, called the squared distance (SD) error term, defined by a curvature-based quadratic approximant of squared distances from data points to a fitting curve. The SD error term faithfully measures the geometric distance between a fitting curve and a target shape, thus leading to faster and more stable convergence than the point distance (PD) error term, which is commonly used in computer graphics and CAGD, and the tangent distance (TD) error term, which is often adopted in the computer vision community. To provide a theoretical explanation of the superior performance of SDM, we formulate the B-spline curve fitting problem as a nonlinear least squares problem and conclude that SDM is a quasi-Newton method which employs a curvature-based positive definite approximant to the true Hessian of the objective function. Furthermore, we show that the method based on the TD error term is a Gauss-Newton iteration, which is unstable for target shapes with high curvature variations, whereas optimization based on the PD error term is the alternating method that is known to have linear convergence.

Scalable vector graphics

Abstract: This article introduces the scalable vector graphics (SVG) standard by highlighting two main SVG themes: graphics capabilities and dynamism. SVG can satisfy needs for distributed, lightweight, graphics-centered applications. Its integration with other XML technologies make it an ideal fit for ever-expanding XML workflows.