Abstract

Large virtual environments arise in many applications and may be used by many users simultaneously for walkthroughs. It is not feasible to store a copy of the whole environment at each of the client locations due to their size. The dynamic nature of the environment also makes it more convenient for a central server to ensure consistency across different users. In this paper, we present a client-server based system for visualizing large environments from remote users. The client gets only the portion of the geometry necessary for navigating around the current user location. The system can adapt to different client parameters such as graphics capability, network bandwidth, communication latency, etc. We present the design and implementation of the system in this paper and produce relevant results using it

Abstract

Graphics models for virtual environments are increasingly getting massive, requiring large amounts of memory to store and high end graphics capabilities to render. It may not be possible or desirable to store the entire model in the station where it will be rendered, especially for environments that change. Such virtual models could be stored on a server and streamed to remote clients on demand. In this paper, we present the design and implementation of a client-server system to render large virtual environments. Our system can provide the best visualization quality for a wide range in connection bandwidth and latency and the rendering capacity of the client. The system uses a visibility-based geometry representation so that little invisible geometry is sent to the client. Incremental updating and client-side prediction of user motion optimize the communication requiremnts for a given client. We present results of the study conducted on a representative range of the relevant parameters in this paper

Abstract

Image Based Rendering holds a lot of promise for navigating through a real world scene without modeling it manually. Different representations have been proposed for IBR in the literature. In this paper, we argue that a representation using depth maps and texture images from a number of viewpoints is a rich and viable representation for IBR. We discuss different aspects of this representation including capture, representation, compression and rendering. We show several results to show how the representation can be used to model and render complex scenes.

Abstract

Recognition of planar shapes is an important problem in computer vision andpattern recognition. The same planar object contour imaged from di1erent cameras or from di1erent viewpoints looks di1erent and their recognition is non-trivial. Traditional shape recognition deals with views of the shapes that di1er only by simple rotations, translations, and scaling. However, shapes su1er more serious deformation between two general views and hence recognition approaches designed to handle translations, rotations, and/or scaling would prove to be insu5cient. Many algebraic relations between matching primitives in multiple views have been identi7edrecently. In this paper, we explore how shape properties andmultiview relations can be combinedto recognize planar shapes across multiple views. We propose novel recognition constraints that a planar shape boundary must satisfy in multiple views. The constraints are on the rank of a Fourier-domain measurement matrix computed from the points on the shape boundary. Our method can additionally compute the correspondence between the curve points after a match is established. We demonstrate the applications of these constraints experimentally on a number of synthetic and real images.

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

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.