Results 1  10
of
32
A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry
, 1994
"... ..."
Automatic Camera Recovery for Closed or Open Image Sequences
 In Proc. ECCV
, 1998
"... . We describe progress in completely automatically recovering 3D scene structure together with 3D camera positions from a sequence of images acquired by an unknown camera undergoing unknown movement. The main departure from previous structure from motion strategies is that processing is not sequenti ..."
Abstract

Cited by 217 (18 self)
 Add to MetaCart
. We describe progress in completely automatically recovering 3D scene structure together with 3D camera positions from a sequence of images acquired by an unknown camera undergoing unknown movement. The main departure from previous structure from motion strategies is that processing is not sequential. Instead a hierarchical approach is employed building from image triplets and associated trifocal tensors. This is advantageous both in obtaining correspondences and also in optimally distributing error over the sequence. The major step forward is that closed sequences can now be dealt with easily. That is, sequences where part of a scene is revisited at a later stage in the sequence. Such sequences contain additional constraints, compared to open sequences, from which the reconstruction can now benefit. The computed cameras and structure are the backbone of a system to build texture mapped graphical models directly from image sequences. 1 Introduction The goal of this work is to obtain ...
3D Model Acquisition from Extended Image Sequences
, 1995
"... This paper describes the extraction of 3D geometrical data from image sequences, for the purpose of creating 3D models of objects in the world. The approach is uncalibrated  camera internal parameters and camera motion are not known or required. Processing an image sequence is underpinned by token ..."
Abstract

Cited by 203 (25 self)
 Add to MetaCart
This paper describes the extraction of 3D geometrical data from image sequences, for the purpose of creating 3D models of objects in the world. The approach is uncalibrated  camera internal parameters and camera motion are not known or required. Processing an image sequence is underpinned by token correspondences between images. We utilise matching techniques which are both robust (detecting and discarding mismatches) and fully automatic. The matched tokens are used to compute 3D structure, which is initialised as it appears and then recursively updated over time. We describe a novel robust estimator of the trifocal tensor, based on a minimum number of token correspondences across an image triplet; and a novel tracking algorithm in which corners and line segments are matched over image triplets in an integrated framework. Experimental results are provided for a variety of scenes, including outdoor scenes taken with a handheld camcorder. Quantitative statistics are included to asses...
Sequential updating of projective and affine structure from motion
 International Journal of Computer Vision
, 1997
"... A structure from motion algorithm is described which recovers structure and camera position, modulo a projective ambiguity. Camera calibration is not required, and camera parameters such as focal length can be altered freely during motion. The structure is updated sequentially over an image sequenc ..."
Abstract

Cited by 141 (4 self)
 Add to MetaCart
A structure from motion algorithm is described which recovers structure and camera position, modulo a projective ambiguity. Camera calibration is not required, and camera parameters such as focal length can be altered freely during motion. The structure is updated sequentially over an image sequence, in contrast to schemes which employ a batch process. A specialisation of the algorithm to recover structure and camera position modulo an affine transformation is described, together with a method to periodically update the affine coordinate frame to prevent drift over time. We describe the constraint used to obtain this specialisation. Structure is recovered from image corners detected and matched automatically and reliably in real image sequences. Results are shown for reference objects and indoor environments, and accuracy of recovered structure is fully evaluated and compared for a number of reconstruction schemes. A specific application of the work is demonstrated  affine structure is used to compute free space maps enabling navigation through unstructured environments and avoidance of obstacles. The path planning involves only affine constructions.
A Computational Approach for Corner and Vertex Detection
 International Journal of Computer Vision
, 1992
"... Corners and vertices are strong and useful features in Computer Vision for scene analysis, stereo matching and motion analysis. This paper deals with the development of a computational approach to these important features. We consider first a corner model and study analytically its behavior once it ..."
Abstract

Cited by 108 (1 self)
 Add to MetaCart
Corners and vertices are strong and useful features in Computer Vision for scene analysis, stereo matching and motion analysis. This paper deals with the development of a computational approach to these important features. We consider first a corner model and study analytically its behavior once it has been smoothed using the wellknown Gaussian filter. This allows us to clarify the behavior of some well known cornerness measure based approaches used to detect these points of interest. Most of these classical approaches appear to detect points that do not correspond to the exact position of the corner. A new scalespace based approach that combines useful properties from the Laplacian and Beaudet's measure [Bea78] is then proposed in order to correct and detect exactly the corner position. An extension of this approach is then developed to solve the problem of trihedral vertex characterization and detection. In particular, it is shown that a trihedral vertex has two elliptic maxima on ...
Modelling and interpretation of architecture from several images
"... The modelling of 3dimensional (3D) environments has become a requirement for many applications in engineering design, virtual reality, visualisation and entertainment. However the scale and complexity demanded from such models has risen to the point where the acquisition of 3D models can require a ..."
Abstract

Cited by 82 (6 self)
 Add to MetaCart
The modelling of 3dimensional (3D) environments has become a requirement for many applications in engineering design, virtual reality, visualisation and entertainment. However the scale and complexity demanded from such models has risen to the point where the acquisition of 3D models can require a vast amount of specialist time and equipment. Because of this much research has been undertaken in the computer vision community into automating all or part of the process of acquiring a 3D model from a sequence of images. This thesis focuses specifically on the automatic acquisition of architectural models from short image sequences. An architectural model is defined as a set of planes corresponding to walls which contain a variety of labelled primitives such as doors and windows. As well as a label defining its type, each primitive contains parameters defining its shape and texture. The key advantage of this representation is that the model defines not only geometry and texture, but also an interpretation of the scene. This is crucial as it enables reasoning about the scene; for instance, structure and texture can be inferred in areas of the model which are unseen in any
Finding corners
 Image and Vision Computing Journal
, 1988
"... Many important image cues such as 'T','X' and 'L' junctions have a local twodimensional structure. Conventional edge detectors are designed for onedimensional 'events'. Even the best edge operators can not reliably detect these twodimensional features. This contribution proposes a solution to ..."
Abstract

Cited by 60 (0 self)
 Add to MetaCart
Many important image cues such as 'T','X' and 'L' junctions have a local twodimensional structure. Conventional edge detectors are designed for onedimensional 'events'. Even the best edge operators can not reliably detect these twodimensional features. This contribution proposes a solution to the twodimensional problem. In this paper, I address the following: • 'L'junction detection. Previous attempts, relying on the second differentials of the image surface have essentially measured image curvature. Recently Harris [Harris 87] implemented a 'corner ' detector that is based only on first differentials. I provide a mathematical proof to explain how this algorithm estimates image curvature. Although this algorithm will isolate image 'L'junctions, its performance cannot be predicted for T'junctions and other higher order image structures. • Instead, an image representation is proposed that exploits the richness of the local differential geometrical 'topography ' of the intensity surface. Theoretical and experimental results are presented which demonstrate how idealised instances of twodimensional surface features such as junctions can be characterised by the differential geometry of a simple facet model. • Preliminary results are very encouraging. Current studies are concerned with the extension to real data. I am investigating statistical noise models to provide a measure of 'confidence' in the geometric labelling. The richness and sparseness of a twodimensional structure can be exploited in many highlevel vision processes. I intend to use my representation to explore some of these fields in future work.
The Problem of Degeneracy in Structure and Motion Recovery from Uncalibrated Image Sequences
 International Journal of Computer Vision
, 2000
"... . The aim of this work is the recovery of 3D structure and camera projection matrices for each frame of an uncalibrated image sequence. In order to achieve this, correspondences are required throughout the sequence. A significant and successful mechanism for automatically establishing these correspo ..."
Abstract

Cited by 46 (5 self)
 Add to MetaCart
. The aim of this work is the recovery of 3D structure and camera projection matrices for each frame of an uncalibrated image sequence. In order to achieve this, correspondences are required throughout the sequence. A significant and successful mechanism for automatically establishing these correspondences is by the use of geometric constraints arising from scene rigidity. However, problems arise with such geometry guided matching if general viewpoint and general structure are assumed whilst frames in the sequence and/or scene structure do not conform to these assumptions. Such cases are termed degenerate. In this paper we describe two important cases of degeneracy and their effects on geometry guided matching. The cases are a motion degeneracy where the camera does not translate between frames, and a structure degeneracy where the viewed scene structure is planar. The effects include the loss of correspondences due to under or over fitting of geometric models estimated from image dat...
Underwater video mosaics as visual navigation maps
 Möller and S. Posch: Iconic Scene Memory for HRI 22 [HGS02
"... This paper presents a set of algorithms for the creation of underwater mosaics and illustrates their use as visual maps for underwater vehicle navigation. First, we describe the automatic creation of video mosaics, which deals with the problem of image motion estimation in a robust and automatic way ..."
Abstract

Cited by 34 (11 self)
 Add to MetaCart
This paper presents a set of algorithms for the creation of underwater mosaics and illustrates their use as visual maps for underwater vehicle navigation. First, we describe the automatic creation of video mosaics, which deals with the problem of image motion estimation in a robust and automatic way. The motion estimation is based on a initial matching of corresponding areas over pairs of images, followed by the use of a robust matching technique, which can cope with a high percentage of incorrect matches. Several motion models, established under the projective geometry framework, allow for the creation of high quality mosaics where no assumptions are made about the camera motion. Several tests were run on underwater image sequences, testifying to the good performance of the implemented matching and registration methods. Next, we deal with the issue of determining the 3D position and orientation of a vehicle from new views of a previously created mosaic. The problem of pose estimation is tackled, using the available information on the camera intrinsic parameters. This information ranges from the full knowledge to the case where they are estimated using a selfcalibration technique based on the analysis of an image sequence captured under pure rotation. The performance of the 3D positioning algorithms is evaluated using images for which accurate ground truth is available. c ○ 2000 Academic Press
A New Multistage Approach to Motion and Structure Estimation: From Essential Parameters to Euclidean Motion Via Fundamental Matrix
, 1996
"... The classical approach to motion and structure estimation problem from two perspective projections consists of two stages: (i) using the 8point algorithm to estimate the 9 essential parameters defined up to a scale factor, which is a linear estimation problem; (ii) refining the motion estimation ba ..."
Abstract

Cited by 20 (1 self)
 Add to MetaCart
The classical approach to motion and structure estimation problem from two perspective projections consists of two stages: (i) using the 8point algorithm to estimate the 9 essential parameters defined up to a scale factor, which is a linear estimation problem; (ii) refining the motion estimation based on some statistically optimal criteria, which is a nonlinear estimation problem on a fivedimensional space. Unfortunately, the results obtained using this approach are often not satisfactory, especially when the motion is small or when the observed points are close to a degenerate surface (e.g. plane). The problem is that the second stage is very sensitive to the initial guess, and that it is very difficult to obtain a precise initial estimate from the first stage. This is because we perform a projection of a set of quantities which are estimated in a space of 8 dimensions, much higher than that of the real space which is fivedimensional. We propose in this paper a novel approach by introducing...