Cell-based approach for 3D reconstruction from incomplete silhouettes

Shape-from-silhouettes is a widely adopted approach to compute accurate 3D reconstructions of people or objects in a multi-camera environment. However, such algorithms are traditionally very sensitive to errors in the silhouettes due to imperfect foreground-background estimation or occluding objects appearing in front of the object of interest. We propose a novel algorithm that is able to still provide high quality reconstruction from incomplete silhouettes. At the core of the method is the partitioning of reconstruction space in cells, i.e. regions with uniform camera and silhouette coverage properties. A set of rules is proposed to iteratively add cells to the reconstruction based on their potential to explain discrepancies between silhouettes in different cameras. Experimental analysis shows significantly improved F1-scores over standard leave-M-out reconstruction techniques

Construction Cost Sensitivity of a Lignocellulosic Ethanol Biorefinery

The technology has been developed to convert feedstock with cellulose content into ethanol. However, ethanol produced from cellulosic feedstock is the same as ethanol distilled from grain. The objective of research is to determine the price per gallon of ethanol needed so that producing lignocellulosic based ethanol become economically feasible.Environmental Economics and Policy, Production Economics,

Parameter-unaware autocalibration for occupancy mapping

People localization and occupancy mapping are common and important tasks for multi-camera systems. In this paper, we present a novel approach to overcome the hurdle of manual extrinsic calibration of the multi-camera system. Our approach is completely parameter unaware, meaning that the user does not need to know the focal length, position or viewing angle in advance, nor will these values be calibrated as such. The only requirement to the multi-camera setup is that the views overlap substantially and are mounted at approximately the same height, requirements that are satisfied in most typical multi-camera configurations. The proposed method uses the observed height of an object or person moving through the space to estimate the distance to the object or person. Using this distance to backproject the lowest point of each detected object, we obtain a rotated and anisotropically scaled view of the ground plane for each camera. An algorithm is presented to estimate the anisotropic scaling parameters and rotation for each camera, after which ground plane positions can be computed up to an isotropic scale factor. Lens distortion is not taken into account. The method is tested in simulation yielding average accuracies within 5cm, and in a real multi-camera environment with an accuracy within 15cm

Three «moral entrepreneurs» and the creation of a «criminal class» in England, c. 1790s-1840s

In the first half of the nineteenth century, English society developed a strong concern about the problem of crime fuelled by rising criminal statistics and pressures for regular paid police forces. By the 1840s, the dominant view of crime was that it was a product, not of poverty, but of weakness of character in the criminals. The three men analysed in this article – Colquhoun, Miles and Chadwick – all of whom advocated a state-run police to cope with the problem, contributed substantially to the creation of that image of crime and criminals; many contemporaries accepted that image as fundamentally correct. Through a detailed analysis of their writings, this article argues that these three men deliberately exaggerated that image, with inflated emotive language, to serve their own campaigns for police reform.Dans la première moitié du XIXe siècle, la société britannique s’est beaucoup intéressée à la question criminelle, en raison de la hausse des statistiques et des pressions en faveur de la création d’une police permanente et rétribuée. Vers 1840, la conception dominante voyait dans le crime un effet, non de la pauvreté, mais de la faiblesse de caractère des criminels. Les trois personnalités étudiées dans cet article – Colquhoun, Miles et Chadwick – qui étaient tous partisans d’une police d’État destinée à affronter ce problème, ont contribué de manière significative à forger cette image de la criminalité et du criminel, que de nombreux contemporains jugeaient fondamentalement juste. Cet article s’appuie sur une analyse détaillée des écrits de ces trois auteurs pour montrer qu’ils avaient consciemment exagéré cette représentation en usant un langage outrancièrement émotionnel, pour conforter leurs campagnes d’opinion en faveur de la réforme de la police

Self-learning voxel-based multi-camera occlusion maps for 3D reconstruction

The quality of a shape-from-silhouettes 3D reconstruction technique strongly depends on the completeness of the silhouettes from each of the cameras. Static occlusion, due to e.g. furniture, makes reconstruction difficult, as we assume no prior knowledge concerning shape and size of occluding objects in the scene. In this paper we present a self-learning algorithm that is able to build an occlusion map for each camera from a voxel perspective. This information is then used to determine which cameras need to be evaluated when reconstructing the 3D model at every voxel in the scene. We show promising results in a multi-camera setup with seven cameras where the object is significantly better reconstructed compared to the state of the art methods, despite the occluding object in the center of the room

Robust pan/tilt compensation for foreground-background segmentation

In this paper, we describe a robust method for compensating the panning and tilting motion of a camera, applied to foreground-background segmentation. First, the necessary internal camera parameters are determined through feature-point extraction and tracking. From these parameters, two motion models for points in the image plane are established. The first model assumes a fixed tilt angle, whereas the second model allows simultaneous pan and tilt. At runtime, these models are used to compensate for the motion of the camera in the background model. We will show that these methods provide a robust compensation mechanism and improve the foreground masks of an otherwise state-of-the-art unsupervised foreground-background segmentation method. The resulting algorithm is always able to obtain F1 scores above 80% on every daytime video in our test set when a minimal number of only eight feature matches are used to determine the background compensation, whereas the standard approaches need significantly more feature matches to produce similar results

Cooperative multi-sensor tracking of vulnerable road users in the presence of missing detections

This paper presents a vulnerable road user (VRU) tracking algorithm capable of handling noisy and missing detections from heterogeneous sensors. We propose a cooperative fusion algorithm for matching and reinforcing of radar and camera detections using their proximity and positional uncertainty. The belief in the existence and position of objects is then maximized by temporal integration of fused detections by a multi-object tracker. By switching between observation models, the tracker adapts to the detection noise characteristics making it robust to individual sensor failures. The main novelty of this paper is an improved imputation sampling function for updating the state when detections are missing. The proposed function uses a likelihood without association that is conditioned on the sensor information instead of the sensor model. The benefits of the proposed solution are two-fold: firstly, particle updates become computationally tractable and secondly, the problem of imputing samples from a state which is predicted without an associated detection is bypassed. Experimental evaluation shows a significant improvement in both detection and tracking performance over multiple control algorithms. In low light situations, the cooperative fusion outperforms intermediate fusion by as much as 30%, while increases in tracking performance are most significant in complex traffic scenes

Integer programming methods for large-scale practical classroom assignment problems

In this paper we present an integer programming method for solving the Classroom Assignment Problem in University Course Timetabling. We introduce a novel formulation of the problem which generalises existing models and maintains tractability even for large instances. The model is validated through computational results based on our experiences at the University of Auckland, and on instances from the 2007 International Timetabling Competition. We also expand upon existing results into the computational difficulty of room assignment problems