A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds

This paper proposes a segmentation-free, automatic and efficient procedure to detect general geometric quadric forms in point clouds, where clutter and occlusions are inevitable. Our everyday world is dominated by man-made objects which are designed using 3D primitives (such as planes, cones, spheres, cylinders, etc.). These objects are also omnipresent in industrial environments. This gives rise to the possibility of abstracting 3D scenes through primitives, thereby positions these geometric forms as an integral part of perception and high level 3D scene understanding. As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure. At the center of our approach lies a closed form 3D quadric fit, operating in both primal & dual spaces and requiring as low as 4 oriented-points. Around this fit, we design a novel, local null-space voting strategy to reduce the 4-point case to 3. Voting is coupled with the famous RANSAC and makes our algorithm orders of magnitude faster than its conventional counterparts. This is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes. Results on synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201

Infrastructure endowment and investment as determinants of regional growth in the European Union

This paper analyses the role of infrastructure endowment and investment in the genesis of regional growth in the European Union. It assesses the economic effects of the existence and improvement of transport networks in light of their interactions with innovation and local socio-economic conditions. The analysis accounts for spatial interactions between different regions in the form of spillovers and network externalities. The regression results highlight the impact of infrastructural endowment on regional economic performance, but also the weak contribution of additional investment. Regions having good transport infrastructure endowment and being well connected to regions with similar good endowments tend to grow faster. However, investment in infrastructure within a region or in neighbouring regions seems to leave especially peripheral regions more vulnerable to competition. Furthermore, the positive impact of infrastructure endowment on growth tends to wane quickly and is weaker than that of, for example, the level of human capital

CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection

Robust face detection in the wild is one of the ultimate components to support various facial related problems, i.e. unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenarios due to numerous challenges, e.g. heavy facial occlusions, extremely low resolutions, strong illumination, exceptionally pose variations, image or video compression artifacts, etc. In this paper, we present a face detection approach named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN) to robustly solve the problems mentioned above. Similar to the region-based CNNs, our proposed network consists of the region proposal component and the region-of-interest (RoI) detection component. However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. Firstly, the multi-scale information is grouped both in region proposal and RoI detection to deal with tiny face regions. Secondly, our proposed network allows explicit body contextual reasoning in the network inspired from the intuition of human vision system. The proposed approach is benchmarked on two recent challenging face detection databases, i.e. the WIDER FACE Dataset which contains high degree of variability, as well as the Face Detection Dataset and Benchmark (FDDB). The experimental results show that our proposed approach trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE Dataset by a large margin, and consistently achieves competitive results on FDDB against the recent state-of-the-art face detection methods

Pointless Global Bundle Adjustment With Relative Motions Hessians

Bundle adjustment (BA) is the standard way to optimise camera poses and to produce sparse representations of a scene. However, as the number of camera poses and features grows, refinement through bundle adjustment becomes inefficient. Inspired by global motion averaging methods, we propose a new bundle adjustment objective which does not rely on image features' reprojection errors yet maintains precision on par with classical BA. Our method averages over relative motions while implicitly incorporating the contribution of the structure in the adjustment. To that end, we weight the objective function by local hessian matrices - a by-product of local bundle adjustments performed on relative motions (e.g., pairs or triplets) during the pose initialisation step. Such hessians are extremely rich as they encapsulate both the features' random errors and the geometric configuration between the cameras. These pieces of information propagated to the global frame help to guide the final optimisation in a more rigorous way. We argue that this approach is an upgraded version of the motion averaging approach and demonstrate its effectiveness on both photogrammetric datasets and computer vision benchmarks

Mechanisms for the generation and regulation of sequential behaviour

A critical aspect of much human behaviour is the generation and regulation of sequential activities. Such behaviour is seen in both naturalistic settings such as routine action and language production and laboratory tasks such as serial recall and many reaction time experiments. There are a variety of computational mechanisms that may support the generation and regulation of sequential behaviours, ranging from those underlying Turing machines to those employed by recurrent connectionist networks. This paper surveys a range of such mechanisms, together with a range of empirical phenomena related to human sequential behaviour. It is argued that the empirical phenomena pose difficulties for most sequencing mechanisms, but that converging evidence from behavioural flexibility, error data arising from when the system is stressed or when it is damaged following brain injury, and between-trial effects in reaction time tasks, point to a hybrid symbolic activation-based mechanism for the generation and regulation of sequential behaviour. Some implications of this view for the nature of mental computation are highlighted

Indirect effects of an aid program: how do liquidity injections affect non-eligibles' consumption?

Aid programs in developing countries are likely to affect both the treated and the non-treated households living in the targeted areas. Studies that focus on the treatment effecton the treated may fail to capture important spillover effects. We exploit the unique designof an aid program's experimental trial to identify its indirect effect on consumption for non-eligible households living in treated areas. We find that this effect is positive, and that itoccurs through changes in the insurance and credit markets: non-eligible households receivemore transfers, and borrow more when hit by a negative idiosyncratic shock, because of theprogram liquidity injection; thus they can reduce their precautionary savings. We also testfor general equilibrium effects in the local labor and goods markets; we find no significantchanges in labor income and prices, while there is a reduction in earnings from sales ofagricultural products, which are now consumed rather than sold. We show that this classof aid programs has important positive externalities; thus their overall effect is larger thanthe effect on the treated. Our results confirm that a key identifying assumption - that thetreatment has no effect on the non-treated - is likely to be violated in similar policy designs. Aid programs in developing countries are likely to affect both the treated and the non-treated households living in the targeted areas. Studies that focus on the treatment effecton the treated may fail to capture important spillover effects. We exploit the unique designof an aid program's experimental trial to identify its indirect effect on consumption for non-eligible households living in treated areas. We find that this effect is positive, and that itoccurs through changes in the insurance and credit markets: non-eligible households receivemore transfers, and borrow more when hit by a negative idiosyncratic shock, because of theprogram liquidity injection; thus they can reduce their precautionary savings. We also testfor general equilibrium effects in the local labor and goods markets; we find no significantchanges in labor income and prices, while there is a reduction in earnings from sales ofagricultural products, which are now consumed rather than sold. We show that this classof aid programs has important positive externalities; thus their overall effect is larger thanthe effect on the treated. Our results confirm that a key identifying assumption - that thetreatment has no effect on the non-treated - is likely to be violated in similar policy designs