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

Development of OCRs for Indian script is an active area of activity today. Indian scripts present great challenges to an OCR designer due to the large number of letters in the alphabet, the sophisticated ways in which they combine, and the complicated graphemes they result in. The problem is compounded by the unstructured manner in which popular fonts are designed. There is a lot of common structure in the different Indian scripts. In this paper, we argue that a number of automatic and semi-automatic tools can ease the development of recognizers for new font styles and new scripts. We discuss briefly three such tools we developed and show how they have helped build new OCRs. An integrated approach to the design of OCRs for all Indian scripts has great benefits. We are building OCRs for all Indian languages following this approach as part of a system to provide tools to create content in them.

Abstract

This paper describes the character recognition process from printed documents containing Hindi and Telugu text. Hindi and Telugu are among the most popular languages in India. The bilingual recognizer is based on Principal Component Analysis followed by support vector classification. This attains an overall accuracy of approximately 96.7%. Extensive experimentation is carried out on an independent test set of approximately 200000 c

Abstract

In this paper, a rule-based approach is introduced for retrieving images from an image database. Compared to image-based queries, this approach allows the user to query in a more natural language. Performance is demonstrated with simple queries on a generic image database and sophisticated queries on a database of face images.

Abstract

—In this paper, we propose a novel method for compression of multiple view images using algebraic constraints. The redundancy present in multiview images is considerably different from that in isolated images or video streams. A three view relationship based on a Trilinear Tensor is employed for view prediction and residual computation. The geometric redundancy in the form of common world structure is exploited during this.

Abstract

This paper describes a new approach for script separation. A characterlevel script separation scheme is combined with a Viterbi algorithm to get an optimal sequence of scripts which could generate such a text. This method complements the popular approaches for script separation at paragraph level using texture features or at line level using structural features.