A New Ranking Approach and a Revisited Ratio Test for Improving Content-Based Image Retrieval

Geometric Verification (GV) is the last step for most visual search systems. It consists of two parts: first, ratio test is used to find matches between feature descriptors; second, a geometric consistency check is applied. Both steps are computationally expensive, but all the attempts made to speed up the process deal with the geometric check part only. In this work, we focus indeed on ratio test. Using simple PCA and other tricks, a speed-up of an order of magnitude is achieved preserving good retrieval accuracy. Moreover, we propose a modified ranking approach which exploits distance information between descriptors and further improves retrieval performance

La identidad actoral en el campo teatral porteño: avances y retrocesos en la compleja configuración del actor como trabajador

El presente artículo tiene como objetivo central indagar en la problemática situación identitaria de los actores en tanto trabajadores. Con ese propósito, se realizará un recorrido desde las primeras décadas hasta mediados del siglo XX, con el afán de establecer en qué medida los roles jugados por el campo teatral y el Estado en los años '40 y '50 impidieron o promovieron la configuración de una identidad del actor como trabajador.Fil: Leonardi, Yanina Andrea. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mauro, Karina Mariel. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Multi-Class Semantic Segmentation of Faces

In this paper the problem of multi-class face segmentation is introduced. Differently from previous works which only consider few classes - typically skin and hair - the label set is extended here to six categories: skin, hair, eyes, nose, mouth and background. A dataset with 70 images taken from MIT-CBCL and FEI face databases is manually annotated and made publicly available1. Three kind of local features - accounting for color, shape and location - are extracted from uniformly sampled square patches. A discriminative model is built with random decision forests and used for classification. Many different combinations of features and parameters are explored to find the best possible model configuration. Our analysis shows that very good performance (~ 93% in accuracy) can be achieved with a fairly simple model

ADS-B/MLAT surveillance system from high altitude platform systems

In this work the potential usage of ADS-Band Wide Area Multilateration(WAM)Surveillance with High Altitude Platform Systems(HAPS)is considered.The paper investigates the possible configuration ofthesystem,thelinkbudget,thege-ometryandthelimitationduetotherandomaccesstothechannelbytheModeSSignals(capacity).ThesurveillanceperformanceoftheproposedarchitectureinaWideAreaMultilaterationcontextisevaluatedbybothsimulationandstatisticalanalysis(CramerRaoLowerBound)

Head pose estimation through multi-class face segmentation

The aim of this work is to explore the usefulness of face semantic segmentation for head pose estimation. We implement a multi-class face segmentation algorithm and we train a model for each considered pose. Given a new test image, the probabilities associated to face parts by the different models are used as the only information for estimating the head orientation. A simple algorithm is proposed to exploit such probabilites in order to predict the pose. The proposed scheme achieves competitive results when compared to most recent methods, according to mean absolute error and accuracy metrics. Moreover, we release and make publicly available a face segmentation dataset consisting of 294 images belonging to 13 different poses, manually labeled into six semantic regions, which we used to train the segmentation models

A unified framework for content-aware view selection and planning through view importance

In this paper we present new algorithms for Next-Best-View (NBV) planning and Image Selection (IS) aimed at image-based 3D reconstruction. In this context, NBV algorithms are needed to propose new unseen viewpoints to improve a partially reconstructed model, while IS algorithms are useful for selecting a subset of cameras from an unordered image collection before running an expensive dense reconstruction. Our methods are based on the idea of view importance: how important is a given viewpoint for a 3D reconstruction? We answer this by proposing a set of expressive quality features and formulate both problems as a search for views ranked by importance. Our methods are automatic and work directly on sparse Structure-from-Motion output. We can remove up to 90% of the images and demonstrate improved speed at comparable reconstruction quality when compared with state of the art on multiple datasets

An Integer Linear Programming Model for View Selection on Overlapping Camera Clusters

Multi-View Stereo (MVS) algorithms scale poorly on large image sets, and quickly become unfeasible to run on a single machine with limited memory. Typical solutions to lower the complexity include reducing the redundancy of the image set (view selection), and dividing the image set in groups to be processed independently (view clustering). A novel formulation for view selection is proposed here. We express the problem with an Integer Linear Programming (ILP) model, where cameras are modeled with binary variables, while the linear constraints enforce the completeness of the 3D reconstruction. The solution of the ILP leads to an optimal subset of selected cameras. As a second contribution, we integrate ILP camera selection with a view clustering approach which exploits Leveraged Affinity Propagation (LAP). LAP clustering can efficiently deal with large camera sets. We adapt the original algorithm so that it provides a set of overlapping clusters where the minimum and maximum sizes and the number of overlapping cameras can be specified. Evaluations on four different dataset show our solution provides significant complexity reductions and guarantees near-perfect coverage, making large reconstructions feasible even on a single machine

In Pursuit of Aviation Cybersecurity: Experiences and Lessons From a Competitive Approach

The passive and independent localization of aircraft has been the subject of much cyberphysical security research. We designed a multistage open competition focusing on the offline batch localization problem using opportunistic data sources. We discuss setup, results, and lessons learned

Evaluation of clustering algorithms at the < 1 GeV energy scale for the electromagnetic calorimeter of the PADME experiment

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Improvement of multilateration(MLAT) accuracy and convergence for airport surveillance

AbstractIn this paper, westudy, evaluate and develop the use of regularization methods to solve the location problem in multilateration systems using Mode-S signals. The Tikhonov method has been implemented as a first applicationto solve the classical system of hyperbolic equations in multilateration systems. Some simulations are obtained and the results are compared with those obtained by the well established Taylor linearization and with the Cramér-Rao Lower Bound analysis. Significant improvements are found for the applicationof Tikhonov method