3D Slicer as an image computing platform for the Quantitative Imaging Network.

While different optical flow techniques continue to appear, there has been a lack of quantitative evaluation of existing methods. For a common set of real and synthetic image sequences, we report the results of a number of regularly cited optical flow techniques, including instances of differential, matching, energy-based, and phase-based methods. Our comparisons are primarily empirical, and concentrate on the accuracy, reliability, and density of the velocity measurements; they show that performance can differ significantly among the techniques we implemented.

Performance of optical flow techniques

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

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Computer Vision: Algorithms and Applications

The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known. Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy. We enumerate and classify many of these variants, and evaluate their effect on the speed with which the correct alignment is reached. In order to improve convergence for nearly-flat meshes with small features, such as inscribed surfaces, we introduce a new variant based on uniform sampling of the space of normals. We conclude by proposing a combination of ICP variants optimized for high speed. We demonstrate an implementation that is able to align two range images in a few tens of milliseconds, assuming a good initial guess. This capability has potential application to real-time 3D model acquisition and model-based tracking.

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Efficient variants of the ICP algorithm

This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

Detection of abrupt changes: theory and application

We investigate the global alignment of some 3-D spatial points, knowing the motion between pairwise nearby positions. A common application is to determine the trajectory of a mobile vision-based system from the scene images acquired along its track. Exploiting redundant measurements and the rigid body motion constraint as the observation model, we apply the mixed adjustment model paradigm to develop recursive estimation algorithms under various scenarios. Results of experiments are given to demonstrate the performance for different noise levels in the observations and the improvements in the position estimation. We also present the results of an experiment with underwater images in the construction of the camera platform trajectory.

Global alignment of sensor positions with noisy motion measurements

We study the problem of exploiting image shading for the recovery of an object's 3D shape underwater. This requires the employment of image models that take into account shading effects due to the medium optical properties, as well as the surface shape and reflectance properties. A simplified image model that incorporates the attenuation of the incident light due to beam spreading, as well as medium absorption and forward-scattering of the incident and reflected light, have been employed to recover the orientation of a planar surface from image shading [Yu and Negahdaripour 1991]. We investigate the application of this model for the recovery of the 3D shape of curved surfaces. To do this, we have implemented two methods based on the generalization of the shape from shading method of Tsai and Shah [1994]. One iterative method involves recursive adjustment of the underwater image, based on the estimate of the target's 3D shape, to correct for the shading effects due to illumination attenuation. Convergence is reached when the recovered shape, after image shading correction for the medium effects, is consistent with the object's reflectance map. A second method estimates the object's 3D shape directly from the underwater image shading model. Convergence properties and the accuracy of the solutions from the two methods are demonstrated using synthetic data. Results from experiments with the images of an underwater scene are presented to show performance with real data.

Recovery of 3D depth map from image shading for underwater applications

There has been an increasing interest in developing vision systems and technologies that support the operation of unmanned submersible platforms. Selected examples include the detection of obstacles and tracking of moving targets, station keeping and positioning, pipeline following, navigation, and mapping. These developments rely on images from standard CCD cameras. We propose the use of these images for the construction of panoramic views that are then utilized for a number of applications, capabilities, and operational modes of underwater vehicles; scenarios that are also common in airborne and space robotics applications. We describe two prototype imaging systems in forward-looking and down-looking configurations, designed and built from 6 off-the-shelf security cameras, also addressing a number of issues for preprocessing the data to construct these views. We study each of a number of critical capabilities and describe the potential use of the panoramic imaging system, presenting various examples to demonstrate the applications.

Utilizing panoramic views for visually guided tasks in underwater robotics applications

We investigate the global alignment of some 3-D spatial points, knowing the motion between pairwise nearby positions. A common application is to determine the trajectory of a mobile vision-based system from the scene images acquired along its track. Exploiting redundant measurements and the rigid body transformation Pj = R(i,J)Pi+t(i,J) as the observation model, we apply the mixed-model least squares estimation paradigm to develop recursive estimation algorithms under various scenarios. Results of experiments are given to demonstrate the performance for different noise levels in the observations and the improvements in the position estimation.

The depth and heading control of an autonomous underwater vehicle (AUV) are considered to follow the predetermined depth and heading angle. The proposed control algorithm was based on a sliding mode control, using estimated hydrodynamic coefficients. The hydrodynamic coefficients were estimated employing conventional nonlinear observer techniques, such as sliding mode observer and extended Kalman filter. Using the estimated coefficients, a sliding mode controller was constructed for a combined diving and steering maneuver. The simulated results of the proposed control system were compared with those of a control system that employed true coefficients. This paper demonstrated the proposed control system, and discusses the mechanisms that make the system stable and accurately follow the desired depth and heading angle in the presence of parameter uncertainty.

Shahriar Negahdaripour

Papers

Global alignment of sensor positions with noisy motion measurements

Recovery of 3D depth map from image shading for underwater applications

Utilizing panoramic views for visually guided tasks in underwater robotics applications

Global alignment of sensor positions with noisy motion measurements

Controller design for an autonomous underwater vehicle using nonlinear observers