71,102 research outputs found

    Geant4-GATE Simulation of a Large Plastic Scintillator for Muon Radiography

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    Envisaging the possibility of using large-area plastic scintillator slabs as robust detectors for high spatial resolution muon radiography, and prior to prototype development, we study expected basic performance by Monte Carlo simulation. We present preliminary results for a scalable square footprint detector unit of similar to 1 m(2), defining a representative simulation model volume of 50 cm x 50 cm, with reflective surfaces and a light readout by direct coupling of 4 small PMTs (in a square arrangement) at one face of the scintillator slab. Light detection efficiency is calculated for several light collection configurations, considering different values of surface roughness, reflectivity, optical coupling index and scintillator thickness. Values maximizing photon detection have been identified. The light response function of 2.5-3.5 cm diameter PMTs for the proposed configuration has been determined. A detector intrinsic spatial resolution of the order of 1 cm is estimated for muon interactions at the center region of the detector module, using a simple centroid positioning algorithm (Anger logic)

    Face Detection in Intelligent Ambiences with Colored Illumination

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    Human face detection is an essential step in the creation of intelligent lighting ambiences, but the constantly changing multi-color illumination makes reliable face detection more challenging. Therefore, we introduce a new face detection and localization algorithm, which retains a high performance under various indoor illumination conditions. The method is based on the creation of a robust skin mask, using general color constancy techniques, and the application of the Viola-Jones face detector on the candidate face areas. Extensive experiments, using a challenging state-of-the-art database and a new one with a wider variation in colored illumination and cluttered background, show a significantly better performance for the newly proposed algorithm than for the most widely used face detection algorithms

    Multi-view Face Detection Using Deep Convolutional Neural Networks

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    In this paper we consider the problem of multi-view face detection. While there has been significant research on this problem, current state-of-the-art approaches for this task require annotation of facial landmarks, e.g. TSM [25], or annotation of face poses [28, 22]. They also require training dozens of models to fully capture faces in all orientations, e.g. 22 models in HeadHunter method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks. The proposed method has minimal complexity; unlike other recent deep learning object detection methods [9], it does not require additional components such as segmentation, bounding-box regression, or SVM classifiers. Furthermore, we analyzed scores of the proposed face detector for faces in different orientations and found that 1) the proposed method is able to detect faces from different angles and can handle occlusion to some extent, 2) there seems to be a correlation between dis- tribution of positive examples in the training set and scores of the proposed face detector. The latter suggests that the proposed methods performance can be further improved by using better sampling strategies and more sophisticated data augmentation techniques. Evaluations on popular face detection benchmark datasets show that our single-model face detector algorithm has similar or better performance compared to the previous methods, which are more complex and require annotations of either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR

    Robust multi-clue face tracking system

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    In this paper we present a multi-clue face tracking system, based on the combination of a face detector and two independent trackers. The detector, a variant of the Viola-Jones algorithm, is set to generate very low false positive error rate. It initiates the tracking system and updates its state. The trackers, based on 3DRS and optical flow respectively, have been chosen to complement each other in different conditions. The main focus of this work is the integration of the two trackers and the design of a closed loop detector-tracker system, aiming at achieving superior robustness at real-time operation on a PC platform. Tests were carried out to assess the actual performance of the system. With an average of about 95% correct face location rate and no significant false positives, the proposed approach appears to be particularly robust to complex backgrounds, ambient light variation, face orientation and scale changes, partial occlusions, different\ud facial expressions and presence of other unwanted faces
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