Arbitrary Optics for Gaussian Splatting Using Space Warping.

Due to recent advances in 3D reconstruction from RGB images, it is now possible to create photorealistic representations of real-world scenes that only require minutes to be reconstructed and can be rendered in real time. In particular, 3D Gaussian splatting shows promising results, outperforming preceding reconstruction methods while simultaneously reducing the overall computational requirements. The main success of 3D Gaussian splatting relies on the efficient use of a differentiable rasterizer to render the Gaussian scene representation.

When Two Eyes Don't Suffice-Learning Difficult Hyperfluorescence Segmentations in Retinal Fundus Autofluorescence Images via Ensemble Learning.

Hyperfluorescence (HF) and reduced autofluorescence (RA) are important biomarkers in fundus autofluorescence images (FAF) for the assessment of health of the retinal pigment epithelium (RPE), an important indicator of disease progression in geographic atrophy (GA) or central serous chorioretinopathy (CSCR). Autofluorescence images have been annotated by human raters, but distinguishing biomarkers (whether signals are increased or decreased) from the normal background proves challenging, with borders being particularly open to interpretation. Consequently, significant variations emerge among different graders, and even within the same grader during repeated annotations.

Mind the Exit Pupil Gap: Revisiting the Intrinsics of a Standard Plenoptic Camera.

Among the common applications of plenoptic cameras are depth reconstruction and post-shot refocusing. These require a calibration relating the camera-side light field to that of the scene. Numerous methods with this goal have been developed based on thin lens models for the plenoptic camera's main lens and microlenses.

ASMNet: Action and Style-Conditioned Motion Generative Network for 3D Human Motion Generation.

Extensive research has explored human motion generation, but the generated sequences are influenced by different motion styles. For instance, the act of walking with joy and sorrow evokes distinct effects on a character's motion. Due to the difficulties in motion capture with styles, the available data for style research are also limited.

Detection of hypoplastic left heart syndrome anatomy from cardiovascular magnetic resonance images using machine learning.

Objective: The prospect of being able to gain relevant information from cardiovascular magnetic resonance (CMR) image analysis automatically opens up new potential to assist the evaluating physician. For machine-learning-based classification of complex congenital heart disease, only few studies have used CMR.

Materials And Methods: This study presents a tailor-made neural network architecture for detection of 7 distinctive anatomic landmarks in CMR images of patients with hypoplastic left heart syndrome (HLHS) in Fontan circulation or healthy controls and demonstrates the potential of the spatial arrangement of the landmarks to identify HLHS.

Automated classification of nanoparticles with various ultrastructures and sizes via deep learning.

Accurately measuring the size, morphology, and structure of nanoparticles is very important, because they are strongly dependent on their properties for many applications. In this paper, we present a deep-learning based method for nanoparticle measurement and classification trained from a small data set of scanning transmission electron microscopy images including overlapping nanoparticles. Our approach is comprised of two stages: localization, i.

Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean.

Mapping and monitoring of seafloor habitats are key tasks for fully understanding ocean ecosystems and resilience, which contributes towards sustainable use of ocean resources. Habitat mapping relies on seafloor classification typically based on acoustic methods, and ground truthing through direct sampling and optical imaging. With the increasing capabilities to record high-resolution underwater images, manual approaches for analyzing these images to create seafloor classifications are no longer feasible.

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course.

Optical coherence tomography (OCT) and fundus autofluorescence (FAF) are important imaging modalities for the assessment and prognosis of central serous chorioretinopathy (CSCR). However, setting the findings from both into spatial and temporal contexts as desirable for disease analysis remains a challenge due to both modalities being captured in different perspectives: sparse three-dimensional (3D) cross sections for OCT and two-dimensional (2D) en face images for FAF. To bridge this gap, we propose a visualisation pipeline capable of projecting OCT labels to en face image modalities such as FAF.

Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks.

Injurious pecking against conspecifics is a serious problem in turkey husbandry. Bloody injuries act as a trigger mechanism to induce further pecking, and timely detection and intervention can prevent massive animal welfare impairments and costly losses. Thus, the overarching aim is to develop a camera-based system to monitor the flock and detect injuries using neural networks.

Fuzzy Overclustering: Semi-Supervised Classification of Fuzzy Labels with Overclustering and Inverse Cross-Entropy.

Deep learning has been successfully applied to many classification problems including underwater challenges. However, a long-standing issue with deep learning is the need for large and consistently labeled datasets. Although current approaches in semi-supervised learning can decrease the required amount of annotated data by a factor of 10 or even more, this line of research still uses distinct classes.

Learn to Train: Improving Training Data for a Neural Network to Detect Pecking Injuries in Turkeys.

This study aimed to develop a camera-based system using artificial intelligence for automated detection of pecking injuries in turkeys. Videos were recorded and split into individual images for further processing. Using specifically developed software, the injuries visible on these images were marked by humans, and a neural network was trained with these annotations.

Panoptic Segmentation of Individual Pigs for Posture Recognition.

Behavioural research of pigs can be greatly simplified if automatic recognition systems are used. Systems based on computer vision in particular have the advantage that they allow an evaluation without affecting the normal behaviour of the animals. In recent years, methods based on deep learning have been introduced and have shown excellent results.

MorphoCluster: Efficient Annotation of Plankton Images by Clustering.

In this work, we present MorphoCluster, a software tool for data-driven, fast, and accurate annotation of large image data sets. While already having surpassed the annotation rate of human experts, volume and complexity of marine data will continue to increase in the coming years. Still, this data requires interpretation.

Robust Depth Estimation for Light Field Microscopy.

Light field technologies have seen a rise in recent years and microscopy is a field where such technology has had a deep impact. The possibility to provide spatial and angular information at the same time and in a single shot brings several advantages and allows for new applications. A common goal in these applications is the calculation of a depth map to reconstruct the three-dimensional geometry of the scene.

Improved wavefront correction for coherent image restoration.

Coherent imaging has a wide range of applications in, for example, microscopy, astronomy, and radar imaging. Particularly interesting is the field of microscopy, where the optical quality of the lens is the main limiting factor. In this article, novel algorithms for the restoration of blurred images in a system with known optical aberrations are presented.

Pose Estimation from Line Correspondences: A Complete Analysis and a Series of Solutions.

In this paper we deal with the camera pose estimation problem from a set of 2D/3D line correspondences, which is also known as PnL (Perspective-n-Line) problem. We carry out our study by comparing PnL with the well-studied PnP (Perspective-n-Point) problem, and our contributions are three-fold: (1) We provide a complete 3D configuration analysis for P3L, which includes the well-known P3P problem as well as several existing analyses as special cases. (2) By exploring the similarity between PnL and PnP, we propose a new subset-based PnL approach as well as a series of linear-formulation-based PnL approaches inspired by their PnP counterparts.

The Bubble Box: Towards an Automated Visual Sensor for 3D Analysis and Characterization of Marine Gas Release Sites.

Several acoustic and optical techniques have been used for characterizing natural and anthropogenic gas leaks (carbon dioxide, methane) from the ocean floor. Here, single-camera based methods for bubble stream observation have become an important tool, as they help estimating flux and bubble sizes under certain assumptions. However, they record only a projection of a bubble into the camera and therefore cannot capture the full 3D shape, which is particularly important for larger, non-spherical bubbles.

Truthful Color Reproduction in Spatial Augmented Reality Applications.

Spatial augmented reality is especially interesting for the design process of a car, because a lot of virtual content and corresponding real objects are used. One important issue in such a process is that the designer can trust the visualized colors on the real object, because design decisions are made on basis of the projection. In this paper, we present an interactive visualization technique which is able to exactly compute the RGB values for the projected image, so that the resulting colors on the real object are equally perceived as the real desired colors.

Geometric calibration of head-mounted displays and its effects on distance estimation.

Head-mounted displays (HMDs) allow users to observe virtual environments (VEs) from an egocentric perspective. However, several experiments have provided evidence that egocentric distances are perceived as compressed in VEs relative to the real world. Recent experiments suggest that the virtual view frustum set for rendering the VE has an essential impact on the user's estimation of distances.

Safety limits of cathodal transcranial direct current stimulation in rats.

Objective: The aim of this rat study was to investigate the safety limits of extended transcranial direct current stimulation (tDCS). tDCS may be of therapeutic value in several neuro-psychiatric disorders. For its clinical applicability, however, more stable effects are required, which may be induced by intensified stimulations.

Anticonvulsant effects of transcranial direct-current stimulation (tDCS) in the rat cortical ramp model of focal epilepsy.

Purpose: Weak direct currents induce lasting alterations of cortical excitability in animals and humans, which are controlled by polarity, duration of stimulation, and current strength applied. To evaluate its anticonvulsant potential, transcranial direct current stimulation (tDCS) was tested in a modified cortical ramp-stimulation model of focal epilepsy.

Methods: The threshold for localized seizure activity (TLS) was determined in freely moving rats by applying a single train of rising bipolar pulses through a unilateral epicranial electrode.

Circadian distribution of motor-activity in unilaterally 6-hydroxy-dopamine lesioned rats.

Sleep abnormalities in idiopathic Parkinson's disease (PD) frequently consist in a reduction of total sleep time and efficacy and subsequent excessive daytime sleepiness. As it remains unclear whether these phenomena are part of the disease itself or result from pharmacological treatment, animal models for investigating the pathophysiology of sleep alterations in PD may add knowledge to this research area. In the present study, we investigate whether changes in circadian motor activity occur in 6-OHDA-lesioning model for PD, and allow a screening for disturbed sleep-waking behaviour.