An image processing method utilizing multi-dimensional empirical mode decomposition is developed to decompose wind-wave surface imageries attributed to various flow processes governing interfacial transport, including gravity waves, capillary ripples, Langmuir cells, and quasi-streamwise turbulent eddies. The decomposition and combination strategies are based on the characteristic length scales and directionalities of the signatures induced by the flow processes. The decomposed imagery thus provides quantification of the contribution partition to the interfacial signatures by individual flow processes. Analyses of infrared images taken in the wind wave facility Aeolotron at Heidelberg University reveal: quasi-streamwise eddies, which arise from the turbulent shear layer, dominate the contribution to interfacial signatures at low wind speeds. For nonbreaking waves at intermediate wind speeds, the contribution partitions to interfacial signatures by the four flow processes are of the same order of magnitude. For microscale breaking waves, the dominant contribution is attributed to boundary layer disruption in the wake of spilling breakers. For breaking waves at high wind speeds, transverse turbulent eddies, which induce similar surface signatures as those by capillary ripples, and streamwise vortices caused by breaking also contribute to the transport; the decomposed imageries are thus induced by multiple flow processes.

We address the problem of depth estimation from light-field images. Our main contribution is a new way to handle occlusions which improves general accuracy and quality of object borders. In contrast to all prior work we work with a model which directly incorporates both depth and occlusion, using a local optimization scheme based on the PatchMatch algorithm. The key benefit of this joint approach is that we utilize all available data, and not erroneously discard valuable information in pre-processing steps. We see the benefit of our approach not only at improved object boundaries, but also at smooth surface reconstruction, where we outperform even methods which focus on good surface regularization. We have evaluated our method on a public light-field dataset, where we achieve state-of-the-art results in nine out of twelve error metrics, with a close tie for the remaining three.

Light-field imaging is based on images taken on a regular grid. Thus, high-quality 3D reconstructions are obtainable by analyzing orientations in epipolar plane images (EPIs). Unfortunately, such data only allows to evaluate one side of the object. Moreover, a constant intensity along each orientation is mandatory for most of the approaches. This paper presents a novel method which allows to reconstruct depth information from data acquired with a circular camera motion, termed circular light fields. With this approach it is possible to determine the full 360° view of target objects. Additionally, circular light fields allow retrieving depth from datasets acquired with telecentric lenses, which is not possible with linear light fields. The proposed method finds trajectories of 3D points in the EPIs by means of a modified Hough transform. For this purpose, binary EPI-edge images are used, which not only allow to obtain reliable depth information, but also overcome the limitation of constant intensity along trajectories. Experimental results on synthetic and real datasets demonstrate the quality of the proposed algorithm.

In contrast to traditional binocular or multi-view stereo approaches, the adequately sampled space of observations in light-field imaging allows, to obtain dense and high quality depth maps. It also extends capabilities beyond those of traditional methods. Previously, constant intensity has been assumed for estimating disparity of orientation in most approaches to analyze epipolar plane images (EPIs). Here, we introduce a modified structure tensor approach which improves depth estimation. This extension also includes a model of non-constant intensity on EPI manifolds. We derive an approach to estimate high quality depth maps in luminance-gradient light fields, as well as in color-filtered light fields. Color-filtered light fields pose particular challenges due to the fact that structures can change significantly in appearance with wavelength and can completely vanish at some wavelength. We demonstrate solutions to this challenge and obtain a dense sRGB image reconstruction in addition to dense depth maps.

Active thermography is a non-contact instrumentation that can be used to investigate small-scale air-sea interaction processes at a footprint of a square meter and with time constants of minutes. It can be used also in large wind/wave facilities. Therefore it is a very useful instrument to investigate the mechanisms of air-sea gas transfer. The principles of the measurement technique are explained. Measurements from the Baltic Sea and the large annular Heidelberg Air-Sea Interaction Facility (Aeolotron) show the large variability of the gas exchange with the wave field and surface films.

3D Time-of-Flight cameras are able to deliver robust depth maps of dynamic scenes. The frame rate, however, is limited because today's systems utilizing two-tap sensors need to acquire the required raw images in multiple instances in order to compute one depth map. These multiple raw images allow canceling out systematic errors introduced by asymmetries in the two taps, which otherwise would distort the reconstructed depth map. This work presents a method to implicitly calibrate these asymmetries of multi-tap 3D Time-of-Flight sensors. The calibration data are gathered from arbitrary live acquisitions possibly in real-time. The proposed correction of raw data supersedes the commonly used averaging technique. Thus it is possible to compute multiple depth maps from a single set of raw images. This increases the frame rate by at least a factor of two. The method is verified using real camera data and is evaluated quantitatively.

In this paper, a comparison of five state of the art region detectors is presented with regard to localization accuracy in position and region shape. Based on carefully estimated ground truth homographies, correspondences between frames are assigned using geometrical region overlap. Significant differences between detectors exist, depending on the type of images. Also, it is shown that localization accuracy linearly depends on region scale for some detectors, which may thus be used as a pre-selection criterion for the removal of error-prone regions. The presented results serve as a supplement to existing comparative studies, and can be used to facilitate the selection of an appropriate detector for a specific target application. When descriptor distance is used as assignment criterion instead of region overlap, a different set of correspondences results with lower accuracy. Set differences (and thus localization accuracy) are directly related to the density of regions in a local neighborhood. Based on the latter, a novel measure for the identification of error-prone regions - shape uniqueness - is introduced. In contrast to existing methods that are based on the descriptor distance of region correspondences, the new measure is pre-computed on each image individually. Thus, the complexity of the subsequent matching task can be significantly reduced.

This paper deals with several aspects towards a more objective performance analysis of low-level motion analysis. A generalized spatiotemporal filter formulation and motion modeling in image sequences is introduced and used to design optimal motion estimators and to perform analytic studies.

The combined use of intensity and depth information greatly helps in the estimation of the local 3D movements (range flow) of moving surfaces. We demonstrate how the two can be combined in both: a local total least squares algorithm, and an iterative global variational technique. While the former assumes locally constant flow, the latter relies on a smoothly varying flow field. The improvement achieved through incorporating intensity is illustrated qualitatively and quantitatively on synthetic and real test data.

Low-level motion estimators based on differential, tensor and phase methods are reformulated in a unified way as filter methods in the continuous space-time domain. This approach allows inherent conceptual deficits to be distinguished from those related to the implementation in discrete space. A detailed analytical error analysis is performed. All techniques yield unbiased motion estimates for areas of constant velocity with any type of gray value structure in continuous space. Errors are only introduced by an inadequate discrete implementation. Further investigated are the influence of zero-mean normal distributed noise, spatially and temporally (accelerated) varying motion, motion discontinuities, and illumination changes. The main results of the paper are also illustrated with real-world image sequences.>

A novel depth-from-focus technique is introduced that needs only a single image. It is based on a precise knowledge of the 3-D point spread function and requires objects of uniform brightness and simple shapes. Using adequate low-level image processing techniques, the true area of the object and the distance from the focal plane is obtained from parameters such as the apparent (blurred) area of the object and the mean brightness in this area. The technique has been applied to measure the size distribution of bubbles submerged by breaking waves. A depth criterion is used to define a virtual measuring volume that is roughly proportional to the size of the bubbles.>