Directive local color transfer based on dynamic look-up table

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SPOT: Sliced Partial Optimal Transport

International audienceOptimal transport research has surged in the last decade with wide applications in computer graphics. In most cases, however, it has focused on the special case of the so-called ``balanced'' optimal transport problem, that is, the problem of optimally matching positive measures of equal total mass. While this approach is suitable for handling probability distributions as their total mass is always equal to one, it precludes other applications manipulating disparate measures.Our paper proposes a fast approach to the optimal transport of constant distributions supported on point sets of different cardinality via one-dimensional slices. This leads to one-dimensional partial assignment problems akin to alignment problems encountered in genomics or text comparison. Contrary to one-dimensional balanced optimal transport that leads to a trivial linear-time algorithm, such partial optimal transport, even in 1-d, has not seen any closed-form solution nor very efficient algorithms to date.We provide the first efficient 1-d partial optimal transport solver. Along with a quasilinear time problem decomposition algorithm, it solves 1-d assignment problems consisting of up to millions of Dirac distributions within fractions of a second in parallel.We handle higher dimensional problems via a slicing approach, and further extend the popular iterative closest point algorithm using optimal transport -- an algorithm we call Fast Iterative Sliced Transport. We illustrate our method on computer graphics applications such a color transfer and point cloud registration

CG2Real: Improving the Realism of Computer Generated Images using a Large Collection of Photographs

Computer Graphics (CG) has achieved a high level of realism, producing strikingly vivid images. This realism, however, comes at the cost of long and often expensive manual modeling, and most often humans can still distinguish between CG images and real images. We present a novel method to make CG images look more realistic that is simple and accessible to novice users. Our system uses a large collection of photographs gathered from online repositories. Given a CG image, we retrieve a small number of real images with similar global structure. We identify corresponding regions between the CG and real images using a novel mean-shift cosegmentation algorithm. The user can then automatically transfer color, tone, and texture from matching regions to the CG image. Our system only uses image processing operations and does not require a 3D model of the scene, making it fast and easy to integrate into digital content creation workflows. Results of a user study show that our improved CG images appear more realistic than the originals

Multi-scale Image Harmonization

Traditional image compositing techniques, such as alpha matting and gradient domain compositing, are used to create composites that have plausible boundaries. But when applied to images taken from different sources or shot under different conditions, these tech- niques can produce unrealistic results. In this work, we present a framework that explicitly matches the visual appearance of images through a process we call image harmonization, before blending them. At the heart of this framework is a multi-scale technique that allows us to transfer the appearance of one image to another. We show that by carefully manipulating the scales of a pyramid decomposition of an image, we can match contrast, texture, noise, and blur, while avoiding image artifacts. The output composite can then be reconstructed from the modified pyramid coefficients while enforcing both alpha-based and seamless boundary constraints. We show how the proposed framework can be used to produce realistic composites with minimal user interaction in a number of different scenarios.Engineering and Applied Science

Atoms in microcavities : detection and spectroscopy

This thesis presents work undertaken with cold rubidium atoms interacting with an optical microcavity. The optical microcavity used is unique in its design, being formed between an optical fibre and silicon micromirror. This allows direct optical access to the cavity mode, whilst the use of microfabrication techniques in the design means that elements of the system are inherently scalable. In addition, the parameters of the system are such that a single atom has a substantial impact on the cavity field. In this system, two types of signal arise from the atoms' interaction with the cavity field; a `reflection' signal and a `fluorescence' signal. A theoretical description for these signals is presented, followed by experiments which characterise the signals under a variety of experimental conditions. The thesis then explores two areas: the use of the microcavity signals for atom detection and the investigation of how higher atom numbers and, as a result, a larger cooperative interaction between the atoms and the cavity field, impacts the signals. First, the use of these signals to detect an effective single atom and individual atoms whilst falling and trapped is explored. The effectiveness of detection is parameterised in terms of detection confidence and signal to noise ratio, detection fidelity and dynamic range. In the second part of this thesis, the effect of higher atom numbers on the reflection and fluorescence signals is investigated. A method for increasing the atom number is presented, alongside experiments investigating the impact on the measured signals. This is followed by experiments which explore the dispersive nature of the atom-cavity interaction by measuring the excitation spectrum of the system in reflection and fluorescence. In doing so, it is shown that, for weak coupling, these two signals are manifestly different

Incorporating prior knowledge into deep neural networks without handcrafted features

Deep learning (DL) is currently the largest area of research within artificial intelligence (AI). This success can largely be attributed to the data-driven nature of the DL algorithms themselves: unlike previous approaches in AI which required handcrafting and significant human intervention, DL models can be implemented and trained with little to no human involvement. The lack of handcrafting, however, can be a two-edged sword. DL algorithms are notorious for producing uninterpretable features, generalising badly to new tasks and relying on extraordinarily large datasets for training. In this thesis, on the assumption that these shortcomings are symptoms of the under-constrained training setup of deep networks, we address the question of how to incorporate knowledge into DL algorithms without reverting to complete handcrafting in order to train more data efficient algorithms. % In this thesis we consider different alternatives to this problem. We start by motivating this line of work with an example of a DL architecture which, inspired by symbolic AI, aims at extracting symbols from a simple environment and using those for quickly learning downstream tasks. Our proof-of-concept model shows that it is possible to address some of the data efficiency issues as well as obtaining more interpretable representations by reasoning at this higher level of abstraction. Our second approach for data-efficiency is based on pre-training: the idea is to pre-train some parts of the DL network on a different, but related, task to first learn useful feature extractors. For our experiments we pre-train the encoder of a reinforcement learning agent on a 3D scene prediction task and then use the features produced by the encoder to train a simulated robot arm on a reaching task. Crucially, unlike previous approaches that could only learn from fixed view-points, we are able to train an agent using observations captured from randomly changing positions around the robot arm, without having to train a separate policy for each observation position. Lastly, we focus on how to build in prior knowledge through the choice of dataset. To this end, instead of training DL models on a single dataset, we train them on a distribution over datasets that captures the space of tasks we are interested in. This training regime produces models that can flexibly adapt to any dataset within the distribution at test time. Crucially they only need a small number of observations in order to adapt their predictions, thus addressing the data-efficiency challenge at test time. We call this family of meta-learning models for few-shot prediction Neural Processes (NPs). In addition to successfully learning how to carry out few-shot regression and classification, NPs produce uncertainty estimates and can generate coherent samples at arbitrary resolutions.Open Acces

Bridgehead substituted scorpionates providing helically chiral complexes

Tripodal borate ligands, including Tp and Tm, are some of the most widely used in organometallic chemistry and were originally prepared, as anions, from the reaction of the relevant heterocycle with an alkali metal borohydride. However, an alternate route, allowing access to zwitterionic, charge-neutral, scorpionates was recently developed within the Bailey group using tris(dimethylamino)borane as the boron source. This thesis describes the expansion of the borane synthetic route to create new, charge-neutral, zwitterionic, tris(methimazolyl)borate (ZTm) ligands containing B-N, B-O and B-C coordinate bonds. Unusual reactivity with isonitrile donors is also presented which has allowed access to boron substituted anionic Tm ligands from the charge-neutral starting material, (HNMe2)ZTm. Attempts to control the helical chirality of ZTm complexes, by using chiral imidazoline donors on the central boron are also described. The borane synthetic route has allowed access to the novel ligand ZThp, the first example of a tripod based on 2-hydroxypyridine ligand arms. As with Tm, this ligand exhibits helical chirality upon complexation and demonstrates how individual atom hybridisation within the ligand arms affects the helicity and thus the chirality of flexible scorpionate ligands. Coordination studies of both zwitterionic and boron-substituted anionic Tm ligands have shown a tendency for the formation of ‘sandwich’ complexes of the form L2M with some metal precursors, whilst the formation of the corresponding ‘half-sandwich’ complexes of these ligands with ruthenium and rhodium was found to be disfavoured

Development of Optogenetic Tools for Manipulating Neuronal Activity and Behaviour in Zebrafish

Integration of sensory input and computation of behavioural output is a dynamic process involving diverse populations of cells often distributed throughout the brain. To study this topic, monitoring neuronal activity from a large population of cells and manipulating targeted neuronal activity in a behaving animal is crucial. This is possible in zebrafish, due to its small and transparent larval brain and its genetic malleability, by making use of optogenetic tools that allow reversible light-dependent activation and inhibition of neuronal activity, and genetically encoded calcium indicators (GECI) that enable non-invasive activity recording. State-of-the-art optogenetic tools with faster kinetics and higher sensitivity facilitate reliable manipulation of activity with high temporal precision during behaviour. Such tools have been developed to be compatible with better calcium indicators to successfully manipulate and optically record neuronal activity simultaneously. In this project, the latest developed optogenetic tools - activators ChrimsonR, C1V1(t/t) and Chronos, inhibitor Jaws, red calcium sensor jRCaMP1b and nuclear markers H2B-RFP and H2B-mCherry - were optimized to be expressed in zebrafish. Behavioural assays to characterize the activating and inhibitory optogenetic tools ChrimsonR and Jaws were established. An escape response of short latency could reliably be evoked in transgenic animals with stable expression of ChrimsonR in trigeminal neurons. Combination of this fast, sensitive and red-shifted tool with GCaMP calcium imaging opens the possibility to simultaneously manipulate and record activity with high spatial and temporal precision from a large population of neurons to study their dynamic interactions during behaviour