Optical Flow Estimation in the Deep Learning Age

Akin to many subareas of computer vision, the recent advances in deep learning have also significantly influenced the literature on optical flow. Previously, the literature had been dominated by classical energy-based models, which formulate optical flow estimation as an energy minimization problem. However, as the practical benefits of Convolutional Neural Networks (CNNs) over conventional methods have become apparent in numerous areas of computer vision and beyond, they have also seen increased adoption in the context of motion estimation to the point where the current state of the art in terms of accuracy is set by CNN approaches. We first review this transition as well as the developments from early work to the current state of CNNs for optical flow estimation. Alongside, we discuss some of their technical details and compare them to recapitulate which technical contribution led to the most significant accuracy improvements. Then we provide an overview of the various optical flow approaches introduced in the deep learning age, including those based on alternative learning paradigms (e.g., unsupervised and semi-supervised methods) as well as the extension to the multi-frame case, which is able to yield further accuracy improvements.Comment: To appear as a book chapter in Modelling Human Motion, N. Noceti, A. Sciutti and F. Rea, Eds., Springer, 202

TVL₁ Planarity Regularization for 3D Shape Approximation

The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within. This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy

Urban vegetation extraction from VHR (tri-)stereo imagery : a comparative study in two central European cities

The present study proposes a workflow to extract vegetation height for urban areas from Pléiades stereo and tri-stereo satellite imagery. The workflow was applied on a stereo image pair for Szeged, Hungary and on tri-stereo imagery for Salzburg, Austria. Digital surface models (DSMs) of the study areas were computed using the semi-global matching algorithm. Normalised digital surface models (nDSMs) were then generated. Objects of vegetation and non-vegetation were delineated based on the spectral information of the multispectral images by applying multi-resolution segmentation and support vector machine classifier. Mean object height values were then computed from the overlaid pixels of the nDSMs and assigned to the objects. Finally, the delineated vegetation was classified into six vegetation height classes based on their assigned height values by using hierarchical classification. The vegetation discrimination resulted in very high accuracy, while the vegetation height extraction was moderately accurate. The results of the vegetation height extraction provided a vertical stratification of the vegetation in the two study areas which is readily applicable for decision support purposes. The elaborated workflow will contribute to a green monitoring and valuation strategy and provide input data for an urban green accessibility study.DK W 1237N23(VLID)251709

Evolution in the Brain, Evolution in the Mind: The Hierarchical Brain and the Interface between Psychoanalysis and Neuroscience

This article first aims to demonstrate the different ways the work of the English neurologist John Hughlings Jackson influenced Freud. It argues that these can be summarized in six points. It is further argued that the framework proposed by Jackson continued to be pursued by twentieth-century neuroscientists such as Papez, MacLean and Panksepp in terms of tripartite hierarchical evolutionary models. Finally, the account presented here aims to shed light on the analogies encountered by psychodynamically oriented neuroscientists, between contemporary accounts of the anatomy and physiology of the nervous system on the one hand, and Freudian models of the mind on the other. These parallels, I will suggest, are not coincidental. They have a historical underpinning, as both accounts most likely originate from a common source: John Hughlings Jackson's tripartite evolutionary hierarchical view of the brain

High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth

We present a structured lighting system for creating high- resolution stereo datasets of static indoor scenes with highly accurate ground-truth disparities. The system includes novel techniques for efficient 2D subpixel correspondence search and self-calibration of cameras and projectors with modeling of lens distortion. Combining disparity estimates from multiple projector positions we are able to achieve a dis- parity accuracy of 0.2 pixels on most observed surfaces, including in half- occluded regions. We contribute 33 new 6-megapixel datasets obtained with our system and demonstrate that they present new challenges for the next generation of stereo algorithms