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Hand activity is a critical monitoring component in understanding a driver's behavior within the car. Current vision-based hand detection algorithms perform poorly in naturalistic settings, due to various challenges such as global illumination changes and constant hand deformation and occlusion. To achieve a more accurate and robust hand detection system, this paper presents a hierarchical context-aware...
This paper presents an agile approach to facilitate the rapid development of traffic sign classification algorithms in heavy vehicles under a wide range of visibility conditions. A vision-based traffic sign recognition system makes a significant contribution to improving the transportation safety by enhancing the driver's awareness on important road signs in an automotive cockpit environment. It has...
Human detection in digital videos is challenging since the human appearance may widely vary. Several algorithms to detect humans in digital images have been recently developed, such as the Aggregated Chanel Features (ACF). Most of them are based on features related to the shape. These algorithms give the best results regarding accuracy but generate many false alarms. In this paper, we propose to use...
Initialization and feature selection are crucial in supervised landmark detection. Mismatching caused by detector error and discrepant initialization is very common in these existing methods. To solve this problem, we proposed a new method, which includes a new initialize model and multitask feature learning, for the robust facial landmark localization. In our new method, firstly, a fast detection...
In object recognition techniques, specially feature-based methods, a fundamental step is to extract keypoints which are distinct and considerably interesting in the image. There are many different keypoint detectors already available, each with its own specific use and results vary enormously. It is widely agreed that evaluation of feature detectors is important. To our knowledge there is no comparative...
In this paper, we introduce the application of generic multi-level Convolutional Neural Networks (CNN) approach into the scene understanding or image parsing task. Given an input image, first, a set of similar images from the training set are retrieved based on global-level CNN feature matching similarities. Then, the input test image and the similar images are overseg-mented into superpixels. Next,...
We propose a novel approach for multi-view object detection in 3D scenes reconstructed from RGB-D sensor. We utilize shape based representation using local shape context descriptors along with the voting strategy which is supported by unsupervised object proposals generated from 3D point cloud data. Our algorithm starts with a single-view object detection where object proposals generated in 3D space...
The need for simultaneous neutron and gamma detection continues to motivate the development of dual mode radiation detectors. One promising candidate is the elpasolite inorganic scintillator Cs2LiYCl6:Ce (CLYC). Previous studies, with other dual neutron–gamma detectors (EJ-299/EJ-309), have shown that the choice of digital data acquisition (DAQ) system can significantly affect pulse shape discrimination...
We are developing a PET detector capable of measuring both time-of-fight (TOF) and depth-of-interaction (DOI) with a goal to improve resulting image quality and accuracy. Phoswich designs have been realized in PET detectors to measure DOI for more than a decade. However, PET detectors based on phoswich designs put great demand on the readout circuit, which has to differentiate the pulse shape produced...
Photon counting arrays with energy resolving capabilities are recently desired for the next-generation X-ray imaging systems. In this work, we present the performance of a 2 mm thick CZT pixel detector, with pixel pitches of 500 μm and 250 μm, coupled to a fast and low noise ASIC (PIXIE ASIC), characterized by only the preamplifier stage. A 16-channel digital readout electronics was used to continuously...
We report on the design and tests results of a fully custom amplifier-comparator readout chip designed to be coupled to silicon detectors with internal charge multiplication for precise timing applications. The ASIC has been developed in UMC 110nm CMOS technology and aims to fulfill the CMS-TOTEM Precision Proton Spectrometer (CT-PPS) time resolution requirements (∼30 ps per detector plane). Both...
The boron-coated straw (BCS) neutron detection technology has been proposed as an attractive 3He replacement solution in a series of applications in homeland security, neutron science, and safeguards. In order to improve the neutron detection efficiency of the BCS, while maintaining manufacturability, Proportional Technologies, Inc. is introducing a modified BCS with significant performance benefits...
The Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) is currently operating with proton-proton collisions at 13 TeV center-of-mass energy and at a bunch spacing of 25 ns. Achieving and maintaining excellent energy resolution for electrons and photons is of primary importance for the CMS physics program for Standard Model measurements and beyond Standard Model searches. In...
In the frame of the INFN experiment REDSOX/REDSOX2 and in collaboration with Fondazione Bruno Kessler (FBK), Trento, we are developing trapezoidal-shaped monolithic arrays of Silicon Drift Detector (SDD) cells for low energy X-ray fluorescence (XRF) experiments. The shape of the individual SDD cell must be carefully selected in order to find the optimal compromise between spectroscopic performance...
A technique is presented to algorithmically evaluate prompt gamma neutron activation spectra, which were produced through excitation of specific material samples. The excitation is done with a neutron generator that provides a switchable, artificial form of neutron radiation. To evaluate the spectra, a extension to prior peak based analysis methods is proposed that dynamically incorporates the detector...
Detection of human beings in a complex background environment is a great challenge in the area of computer vision. For such a difficult task, most of the time no single feature algorithm is rich enough to capture all the relevant information available in the image. To improve the detection accuracy we propose a new descriptor that fuses the local phase information, image gradient, and texture features...
This paper considers the Korean character detection problem. Unlike English where an alphabet constitutes a character, the Korean character is composed of more than two Korean alphabets, where they could be either connected or separated, relying on the Korean character font. Also, the Korean has two character structures which constitute a nested structure. These properties make the Korean character...
Detecting and localizing insulator plays a vital role in any power line monitoring system. In this work, we present a novel method for rotation invariant insulator detection. Rotation invariance is achieved by an efficient approach for estimating rotation angle of all insulator of an image. Sliding window based local directional pattern (LDP) feature is extracted from the image and support vector...
Automatic detection of human in a video sequence is a canonical instance of object detection. It's considered as a nonrigid object; it has many appearances at different perspectives. Different approaches are used by several methods to combine what is specific to the pedestrian detection, and what is common to the object recognition. A robust solution to this problem would have numerous applications...
This paper proposes an extended Constrained Local Model (CLM) formulation for aligning faces using depth information. The CLMs are popular methods that were initially designed to locate facial features in regular intensity images. Briefly, they combine a set of local detectors, one for each landmark, whose locations are regularized by a linear shape model. Fitting a CLM is usually framed as a two...
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