Sami S. Brandt

A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses

Fish-eye lenses are convenient in such applications where a very wide angle of view is needed, but their use for measurement purposes has been limited by the lack of an accurate, generic, and easy-to-use calibration procedure. We hence propose a generic camera model, which is suitable for fish-eye lens cameras as well as for conventional and wide-angle lens cameras, and a calibration method for estimating the parameters of the model. The achieved level of calibration accuracy is comparable to the previously reported state-of-the-art

Statistical shape features in content-based image retrieval

In this article the use of shape features in content-based image retrieval is studied. The emphasis is on techniques which do not demand object segmentation. PicSOM, the image retrieval system used in the experiments, requires that features are represented by constant-sized feature vectors for which the Euclidean distance can be used as a similarity measure. The shape features suggested here are edge histograms and Fourier transform based features computed for an edge image in Cartesian and polar coordinate planes. The results show that both local and global shape features are important clues of shapes in an image.

A generic camera calibration method for fish-eye lenses

Fish-eye lenses are convenient in such computer vision applications where a very wide angle of view is needed. However, their use for measurement purposes is limited by the lack of an accurate, generic, and easy-to-use calibration procedure. We hence propose a generic camera model for cameras equipped with fish-eye lenses and a method for calibration of such cameras. The calibration is possible by using only one view of a planar calibration object but more views should be used for better results. The proposed calibration method was evaluated with real images and the obtained results are promising. The calibration software becomes commonly available at the authors Web page.

Quasi-Dense Wide Baseline Matching Using Match Propagation

In this paper we propose extensions to the match propagation algorithm which is a technique for computing quasi-dense point correspondences between two views. The extensions make the match propagation applicable for wide baseline matching, i.e., for cases where the camera pose can vary a lot between the views. Our first extension is to use a local affine model for the geometric transformation between the images. The estimate of the local transformation is obtained from affine covariant interest regions which are used as seed matches. The second extension is to use the second order intensity moments to adapt the current estimate of the local affine transformation during the propagation. This allows a single seed match to propagate into regions where the local transformation between the views differs from the initial one. The experiments with real data show that the proposed techniques improve both the quality and coverage of the quasi-dense disparity map.

Automatic TEM image alignment by trifocal geometry

Here we propose a novel method for automatic, markerless, feature‐based alignment of TEM images suitable for electron tomography. The proposed method, termed trifocal alignment, is more accurate than the previous markerless methods. The key components developed are: (1) a reliable multi‐resolution algorithm for matching feature points between images; (2) a robust, maximum‐likelihood‐based estimator for determining the geometry of three views – the trifocal constraint – required for validating the correctness of the matches; and (3) a robust, large‐scale optimization framework to compute the alignment parameters from hundreds of thousands of feature point measurements from a few hundred images. The ability to utilize such a large number of measurements successfully compensates for point localization errors. The method was experimentally confirmed with electron tomography tilt series of biological and material sciences samples, consisting of from 40 to 150 images. The results show that, with this feature‐based alignment approach, a level of accuracy comparable with fiducial marker alignment can be achieved.

Object recognition and segmentation by non-rigid quasi-dense matching

In this paper, we present a non-rigid quasi-dense matching method and its application to object recognition and segmentation. The matching method is based on the match propagation algorithm which is here extended by using local image gradients for adapting the propagation to smooth non-rigid deformations of the imaged surfaces. The adaptation is based entirely on the local properties of the images and the method can be hence used in non-rigid image registration where global geometric constraints are not available. Our approach for object recognition and segmentation is directly built on the quasi-dense matching. The quasi-dense pixel matches between the model and test images are grouped into geometrically consistent groups using a method which utilizes the local affine transformation estimates obtained during the propagation. The number and quality of geometrically consistent matches is used as a recognition criterion and the location of the matching pixels directly provides the segmentation. The experiments demonstrate that our approach is able to deal with extensive background clutter, partial occlusion, large scale and viewpoint changes, and notable geometric deformations.

Statistical Shape Features for Content-Based Image Retrieval

In this article the use of statistical, low-level shape features in content-based image retrieval is studied. The emphasis is on such techniques which do not demand object segmentation. PicSOM, the image retrieval system used in the experiments, requires that features are represented by constant-sized feature vectors for which the Euclidean distance can be used as a similarity measure. The shape features suggested here are edge histograms and Fourier-transform-based features computed from the image after edge detection in Cartesian or polar coordinate planes. The results show that both local and global shape features are important clues of shapes in an image.

Geometric Camera Calibration

Geometric camera calibration is a prerequisite for making accurate geometric measurements from image data, and hence it is a fundamental task in computer vision. This article gives a discussion about the camera models and calibration methods used in the field. The emphasis is on conventional calibration methods in which the parameters of the camera model are determined by using images of a calibration object whose geometric properties are known. The presented techniques are illustrated with real calibration examples in which several different kinds of cameras are calibrated using a planar calibration object. Keywords: camera calibration; camera model; computer vision; photogrammetry; central camera; omnidiractional vision; catadioptivic camera; fish.eye camera

Markerless Alignment in Electron Tomography

In computing high-accuracy reconstructions from transmission electron microscope (TEM) tilt series, image alignment currently has an important role. Though most are automated devices today, the imaging systems have certain non-idealities which give rise to abrupt shifts, rotations and magnification changes in the images. Thus, the geometric relationships between the object and the obtained projections are not precisely known initially. In this chapter, image alignment refers to the computation of the projection geometry of the tilt series so that most of the above deviations from the assumed ideal projection geometry could be rectified by using simple 2D geometric transformations for the images before computing a tomographic reconstruction.

Measuring and modelling sewer pipes from video

This article presents a system for the automatic measurement and modelling of sewer pipes. The system recovers the interior shape of a sewer pipe from a video sequence which is acquired by a fish-eye lens camera moving inside the pipe. The approach is based on tracking interest points across successive video frames and posing the general structure-from-motion problem. It is shown that the tracked points can be reliably reconstructed despite the forward motion of the camera. This is achieved by utilizing a fish-eye lens with a wide field of view. The standard techniques for robust estimation of the two- and three-view geometry are modified so that they can be used for calibrated fish-eye lens cameras with a field of view less than 180°. The tubular arrangement of the reconstructed points allows pipe shape estimation by surface fitting. Hence, a method for modelling such surfaces with a locally cylindrical model is proposed. The system is demonstrated with a real sewer video and an error analysis for the recovered structure is presented.