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

No Results Found

Abstract

Multiview analysis of scenes includes the study of sceneindependent constraints satisfied by a configuration of cameras for all types of scenes as well as the study of viewindependent constraints satisfied by any camera on a configuration of points. In this paper, we derive new constraints involving configurations of points that move with constant velocity, with constant acceleration, and for unconstrained planar motion. We show how these constraints can be applied to problems like motion recognition, frame alignment, etc.

Abstract

Polygon approximation is an important step in the recognition of planar shapes. Traditional polygonal approximation algorithms handle only images that are related by a similarity transformation. The transformation of a planar shape as the viewpoint changes with a perspective camera is a general projective one. In this paper, we present a novel method for polygonal approximation of closed curves that is invariant to projective transformation. The polygons generated by our algorithm from two images, related by a projective homography, are isomorphic. We also describe an application of this in the form of numeral recognition. We demonstrate the importance of this algorithm for real-life applications like number plate recognition, aircraft recognition and metric rectification.

Abstract

Correspondence between pixels is an important problem in stereo vision. Several algorithms have been proposed to carry out this task in literature. Almost all of them employ only gray-values. We show here that addition of primary or secondary evidence maps can improve the correspondence computation. However any particular combination is not guaranteed to provide proper results in a general sitiuation. What one needs is a mechanism to select the evidences which are apropriate for a particular pair of images. We present an algorithm for stereo correspondence that can take advantage of different image features adaptively for matching. A match measure combining different individual measures computed from different featuresis used by our algorithm. The advantages of each feature can be combined in a single correspondence computation. We describe an unsupervised scheme to compute the relevance of each feature to a particular situation, given a set of possibly useful features. We present an implementation of the scheme using dynamic programming for pixel-to-pixel correspondence

Abstract

We present an algorithm for stereo correspondence that can take advantage of different image features adaptively for matching. A match measure combining different match measures computed from different features is used by our algorithm. It is possible to compute correspondences using the gray value, multispectral components, derived features such as the edge strength, texture, etc., in a flexible manner using this algorithm. The advantages of each feature can be combined in a single correspondence computation. We describe a non-supervised scheme to compute the relevance of each feature to a particular situation, given a set of possibly useful features. We present an implementation of the scheme using dynamic programming for pixel-to-pixel correspondence. Results demonstrate the advantages of our scheme under different conditions.

Abstract

Computing correspondences between pairs of images is fundamental to all structures from motion algorithms. Correlation is a popular method to estimate similarity between patches of images. In the standard formulation, the correlation function uses only one feature such as the gray level values of a small neighbourhood. Research has shown that different features—such as colour, edge strength, corners, texture measures—work better under di0erent conditions. We propose a framework of generalized correlation that can compute a real valued similarity measure using a feature vector whose components can be dissimilar. The framework can combine the e0ects of di0erent image features, such as multi-spectral features, edges, corners, texture measures, etc., into a single similarity measure in a 3exible manner. Additionally, it can combine results of di0erent window sizes used for correlation with proper weighting for each. Relative importances of the features can be estimated from the image itself for accurate correspondence. In this paper, we present the framework of generalised correlation, provide a few examples demonstrating its power, as well as discuss the implementation issues. ? 2002 Published by Elsevier Science Ltd on behalf of Pattern Recognition Society.

Abstract

Multiview studies in Computer Vision have concentrated on the constraints satisfied by individual primitives such as points and lines. Not much attention has been paid to the properties of a collection of primitives in multiple views, which could be studied in the spatial domain or in an appropriate transform domain. We derive an algebraic constraint for planar shape recognition across multiple views based on the rank of a matrix of Fourier domain descriptor coefficients of the shape in different views. We also show how correspondence between points on the boundary can be computed for matching shapes using the phase of a measure for recognition.