Light field image processing : overview and research issues

Light field (LF) imaging first appeared in the computer graphics community with the goal of photorealistic 3D rendering [1]. Motivated by a variety of potential applications in various domains (e.g., computational photography, augmented reality, light field microscopy, medical imaging, 3D robotic, particle image velocimetry), imaging from real light fields has recently gained in popularity, both at the research and industrial level.peer-reviewe

Orientation Analysis in 4D Light Fields

This work is about the analysis of 4D light fields. In the context of this work a light field is a series of 2D digital images of a scene captured on a planar regular grid of camera positions. It is essential that the scene is captured over several camera positions having constant distances to each other. This results in a sampling of light rays emitted by a single scene point as a function of the camera position. In contrast to traditional images – measuring the light intensity in the spatial domain – this approach additionally captures directional information leading to the four dimensionality mentioned above. For image processing, light fields are a relatively new research area. In computer graphics, they were used to avoid the work-intensive modeling of 3D geometry by instead using view interpolation to achieve interactive 3D experiences without explicit geometry. The intention of this work is vice versa, namely using light fields to reconstruct geometry of a captured scene. The reason is that light fields provide much richer information content compared to existing approaches of 3D reconstruction. Due to the regular and dense sampling of the scene, aside from geometry, material properties are also imaged. Surfaces whose visual appearance change when changing the line of sight causes problems for known approaches of passive 3D reconstruction. Light fields instead sample this change in appearance and thus make analysis possible. This thesis covers different contributions. We propose a new approach to convert raw data from a light field camera (plenoptic camera 2.0) to a 4D representation without a pre-computation of pixel-wise depth. This special representation – also called the Lumigraph – enables an access to epipolar planes which are sub-spaces of the 4D data structure. An approach is proposed analyzing these epipolar plane images to achieve a robust depth estimation on Lambertian surfaces. Based on this, an extension is presented also handling reflective and transparent surfaces. As examples for the usefulness of this inherently available depth information we show improvements to well known techniques like super-resolution and object segmentation when extending them to light fields. Additionally a benchmark database was established over time during the research for this thesis. We will test the proposed approaches using this database and hope that it helps to drive future research in this field

SLFS: Semi-supervised light-field foreground-background segmentation

Efficient segmentation is a fundamental problem in computer vision and image processing. Achieving accurate segmentation for 4D light field images is a challenging task due to the huge amount of data involved and the intrinsic redundancy in this type of images. While automatic image segmentation is usually challenging, and because regions of interest are different for different users or tasks, this paper proposes an improved semi-supervised segmentation approach for 4D light field images based on an efficient graph structure and user's scribbles. The recent view-consistent 4D light field superpixels algorithm proposed by Khan et al. is used as an automatic pre-processing step to ensure spatio-angular consistency and to represent the image graph efficiently. Then, segmentation is achieved via graph-cut optimization. Experimental results for synthetic and real light field images indicate that the proposed approach can extract objects consistently across views, and thus it can be used in applications such as augmented reality applications or object-based coding with few user interactions.info:eu-repo/semantics/acceptedVersio

A topological solution to object segmentation and tracking

The world is composed of objects, the ground, and the sky. Visual perception of objects requires solving two fundamental challenges: segmenting visual input into discrete units, and tracking identities of these units despite appearance changes due to object deformation, changing perspective, and dynamic occlusion. Current computer vision approaches to segmentation and tracking that approach human performance all require learning, raising the question: can objects be segmented and tracked without learning? Here, we show that the mathematical structure of light rays reflected from environment surfaces yields a natural representation of persistent surfaces, and this surface representation provides a solution to both the segmentation and tracking problems. We describe how to generate this surface representation from continuous visual input, and demonstrate that our approach can segment and invariantly track objects in cluttered synthetic video despite severe appearance changes, without requiring learning.Comment: 21 pages, 6 main figures, 3 supplemental figures, and supplementary material containing mathematical proof

Semantic Attention Flow Fields for Monocular Dynamic Scene Decomposition

From video, we reconstruct a neural volume that captures time-varying color, density, scene flow, semantics, and attention information. The semantics and attention let us identify salient foreground objects separately from the background across spacetime. To mitigate low resolution semantic and attention features, we compute pyramids that trade detail with whole-image context. After optimization, we perform a saliency-aware clustering to decompose the scene. To evaluate real-world scenes, we annotate object masks in the NVIDIA Dynamic Scene and DyCheck datasets. We demonstrate that this method can decompose dynamic scenes in an unsupervised way with competitive performance to a supervised method, and that it improves foreground/background segmentation over recent static/dynamic split methods. Project Webpage: https://visual.cs.brown.edu/saffComment: International Conference on Computer Vision (ICCV) 2023; 10 pages, 8 figures, 3 table

Line Defects, Tropicalization, and Multi-Centered Quiver Quantum Mechanics

We study BPS line defects in N=2 supersymmetric four-dimensional field theories. We focus on theories of "quiver type," those for which the BPS particle spectrum can be computed using quiver quantum mechanics. For a wide class of models, the renormalization group flow between defects defined in the ultraviolet and in the infrared is bijective. Using this fact, we propose a way to compute the BPS Hilbert space of a defect defined in the ultraviolet, using only infrared data. In some cases our proposal reduces to studying representations of a "framed" quiver, with one extra node representing the defect. In general, though, it is different. As applications, we derive a formula for the discontinuities in the defect renormalization group map under variations of moduli, and show that the operator product algebra of line defects contains distinguished subalgebras with universal multiplication rules. We illustrate our results in several explicit examples.Comment: 76 pages, 10 figures; v2: minor revisions, correction to Coulomb branch calculation for defects in SU(2) SY