Abstract

Configurations of dynamic points viewed by one or more cameras have not been studied much. In this paper, we present several view and time-independent constraints on different configurations of points moving on a plane. We show that 4 points with constant independent velocities or accelerations under affine projection can be characterized in a view independent manner using 2 views. Under perspective projection, 5 coplanar points under uniform linear velocity observed for 3 time instants in a single view have a view-independent characterization. The best known constraint for this case involves 6 points observed for 35 frames. Under uniform acceleration, 5 points in 5 time instants have a view-independent characterization. We also present constraints on a point undergoing arbitrary planar motion under affine projections in the Fourier domain. The constraints introduced in this paper involve fewer points or views than similar results reported in the literature and are simpler to compute in most cases. The constraints developed can be applied to many aspects of computer vision. Recognition constraints for several planar point configurations of moving points can result from them. We also show how time-alignment of views captured independently can follow from the constraints on moving point configurations.

Abstract

We address a few problems in navigation of automated vehicles using images captured by a mounted camera. Specifically, we look at the recognition of sign boards, rectification of planar objects imaged by the camera, and estimation of the position of a vehicle with respect to a fixed sign board. Our solutions are based on contour correspondence between a reference view and the current view. A mapping between corresponding points of a planar object in two different views is a
matrix called the homography. A novel two-step linear algorithm for homography calculation from contour correspondence is developed first. Our algorithm requires the identification of an image contour as the projections of a known planar world contour and the selection of a known starting point. The homography between the reference view and the target view is applied to several reallife navigation applications, results of which are presented in this paper

Abstract

Annotated datasets of handwriting are a prerequisite for the design and training of handwriting recognition algorithms. In this paper, we briefly describe an XML representation for annotation of online handwriting data that uses the emerging Digital Ink Markup Language (InkML) standard from W3C for the representation of handwriting data. We then describe a tool based on the proposed representation that can be used for annotation of digital ink. Ease and speed of annotation are emphasized in the design of the tool. Together, the representation and the tool attempt to address the requirements of creation of annotated datasets of handwritten data in different scripts around the worldwid

Abstract

This paper presents several results on images of various configurations of conics. We extract information about the plane from single and multiple views of known and unknown conics, based on planar homography and conic correspondences. We show that a single conic section cannot provide sufficient information. Metric rectification of the plane can be performed from a single view if two conics can be identified to be images of circles without knowing their centers or radii. The homography between two views of a planar scene can be computed if two arbitrary conics are identified in them without knowing anything specific about them. The scene can be reconstructed from a single view if images of a pair of circles can be identified in two planes. Our results are simpler and require less information from the images than previously known results. The results presented here involve univariate polynomial equations of degree 4 or 8 and always have solutions. Applications to metric rectification, homography calculation, 3D reconstruction, and projective OCR are presented to demonstrate the usefulness of our scheme

Abstract

Recognition of discrete planar contours under similarity transformations has received a lot of attention but little work has been reported on recognizing them under more general transformations. Planar object boundaries undergo projective or affine transformations across multiple views. We present two methods to recognize discrete curves in this paper. The first method computes a piecewise parametric approximation of the discrete curve that is projectively invariant. A polygon approximation scheme and a piecewise conic approximation scheme are presented here. The second method computes an invariant sequence directly from the sequence of discrete points on the curve in a Fourier transform space. The sequence is shown to be identical up to a scale factor in all affine related views of the curve. We present the theory and demonstrate its applications to several problems including numeral recognition, aircraft recognition, and homography computation. q 2004 Elsevier B.V. All rights reserved

Abstract

Computing the transformation between two views of a planar scene is an important step in many computer vision applications. Spatial approaches to solve this problem need corresponding sets of primitives – points, lines, conics, etc. Identification of corresponding primitives in two images is non-trivial, limiting the applicability of such approaches. In this paper, we present a novel Fourier domain based approach that makes use of image intensities for computing the image-to-image transformation. Our approach transforms the images to the Fourier domain and then represents them in a coordinate system in which the affine transformation is reduced to an anisotropic scaling. The anisotropic scale factors can be computed using cross correlation methods, and working backwards from this, we compute the entire transformation. It does not require any correspondences thereby making it practically very useful. Applications to registration and recognition are discussed.

Abstract

There has been an increasing need to find effective ways to impart Software Engineering Education. This paper discusses the problems the different approaches, both conventional and non conventional, face and proposes a case study approach that addresses these issues. This research aims to create a teaching methodology that will assist the teachers to create an effective learning environment and help the students to gain realistic exposure to concepts of Software Engineering as they are applied in the real world. The focus of the paper is to emphasize on creating a context to learn Software Engineering through case studies that imbibe the best practices from real world experiences.

Abstract

n this paper, we have proposed speculative locking (SL) protocols to improve the performance of distributed databasesystems (DDBSs) by trading extra processing resources. In SL, a transaction releases the lock on the data object whenever itproduces corresponding after-image during its execution. By accessing both before and after-images, the waiting transaction carriesout speculative executions and retains one execution based on the termination (commit or abort) mode of the preceding transactions.By carrying out multiple executions for a transaction, SL increases parallelism without violating serializability criteria. Under the naiveversion of SL, the number of speculative executions of the transaction explodes with data contention. By exploiting the fact that asubmitted transaction is more likely to commit than abort, we propose the SL variants that process transactions efficiently bysignificantly reducing the number of speculative executions. The simulation results indicate that even with manageable extraresources, these variants significantly improve the performance over two-phase locking in the DDBS environments where transactionsspend longer time for processing and transaction-aborts occur frequently.

Abstract

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Abstract

In this papcr, wc propose an approach for continuous speech recognition by spotting consonant-vowel (CV) units. The niain issues in spotting CV units are the location of anchor points and labelling the regions around these anchor points using suitable classifiers. The vowel onset points (VOPs) trave bcen used as an- chor points. The distribution capturing ability of autoassociative neural network (AA”) models is explored for detection of VOPs in continnous speech. We consider support vector machine (SVM) based classifiers due to their ability of generalisation from limited training data and also due to their inherent discriminative learn- ing. The CV spotting approach for continuous speech recognition has bccn dcmonstratcd for scntcnccs in Indian languages