A Comparison of Local Detectors and Descriptors for Multi-Object Applications

2015 ◽  
Author(s):  
Faten A. Khalifa ◽  
Noura A. Semary ◽  
Hatem M. El-Sayed ◽  
Mohiy M. Hadhoud
Keyword(s):  
Local Detectors

scholarly journals Local Detectors and Descriptors for Object Class Recognition

2015 ◽  
Vol 7 (10) ◽  
pp. 12-18 ◽  
Author(s):  
Faten A. Khalifa ◽  
◽  
Noura A. Semary ◽  
Hatem M. El-Sayed ◽  
Mohiy M. Hadhoud
Keyword(s):  
Object Class ◽  
Object Class Recognition ◽  
Local Detectors

scholarly journals Live nephron imaging by MRI

2014 ◽  
Vol 307 (10) ◽  
pp. F1162-F1168 ◽  
Author(s):  
Chunqi Qian ◽  
Xin Yu ◽  
Nikorn Pothayee ◽  
Stephen Dodd ◽  
Nadia Bouraoud ◽  
...  
Keyword(s):  
Simultaneous Detection ◽  
Negative Contrast ◽  
Positive Contrast ◽  
Gadopentetate Dimeglumine ◽  
Live Imaging ◽  
Renal Tubules ◽  
Local Sensitivity ◽  
Local Detectors ◽  
Integrated Detector

The local sensitivity of MRI can be improved with small MR detectors placed close to regions of interest. However, to maintain such sensitivity advantage, local detectors normally need to communicate with the external amplifier through cable connections, which prevent the use of local detectors as implantable devices. Recently, an integrated wireless amplifier was developed that can efficiently amplify and broadcast locally detected signals, so that the local sensitivity was enhanced without the need for cable connections. This integrated detector enabled the live imaging of individual glomeruli using negative contrast introduced by cationized ferritin, and the live imaging of renal tubules using positive contrast introduced by gadopentetate dimeglumine. Here, we utilized the high blood flow to image individual glomeruli as hyperintense regions without any contrast agent. These hyperintense regions were identified for pixels with signal intensities higher than the local average. Addition of Mn2+ allowed the simultaneous detection of both glomeruli and renal tubules: Mn2+ was primarily reabsorbed by renal tubules, which would be distinguished from glomeruli due to higher enhancement in T1-weighted MRI. Dynamic studies of Mn2+ absorption confirmed the differential absorption affinity of glomeruli and renal tubules, potentially enabling the in vivo observation of nephron function.

scholarly journals Two Types of Distributed CFAR Detection Based on Weighting Functions in Fusion Center for Weibull Clutter

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Amir Zaimbashi
Keyword(s):  
Fusion Center ◽  
Test Statistics ◽  
Constant False Alarm Rate ◽  
Robust Performance ◽  
Global Test ◽  
Weighting Functions ◽  
Cfar Detection ◽  
Local Detectors ◽  
Detection Schemes ◽  
Maximum Minimum

Two types of distributed constant false alarm rate (CFAR) detection using binary and fuzzy weighting functions in fusion center are developed. In the two types of distributed detectors, it was assumed that the clutter parameters at the local sensors are unknown and each local detector performs CFAR processing based on ML and OS CFAR processors before transmitting data to the fusion center. At the fusion center, received data is weighted either by a binary or a fuzzy weighting functions and combined according to deterministic rules, constructing global test statistics. Moreover, for the Weibull clutter, the expression of the weighting functions, based on ML and OS CFAR processors in local detectors, is obtained. In the binary type, we analyzed various distributed detection schemes based on maximum, minimum, and summation rules in fusion center. In the fuzzy type, we consider the various distributed detectors based on algebraic product, algebraic sum, probabilistic OR, and Lukasiewicz t-conorm fuzzy rules in fusion center. The performance of the two types of distributed detectors is analyzed and compared in the homogenous and nonhomogenous situations, multiple targets, or clutter edge. The simulation results indicate the superiority and robust performance of fuzzy type in homogenous and non homogenous situations.

Performance of passive decoy-state quantum key distribution with mismatched local detectors

2021 ◽  
Author(s):  
Kai Yu ◽  
Chun Hui Zhang ◽  
Xing Yu Zhou ◽  
Qin Wang
Keyword(s):  
Quantum Key Distribution ◽  
Detection Efficiency ◽  
Finite Size ◽  
Key Distribution ◽  
Finite Size Effect ◽  
Dark Count ◽  
Decoy State ◽  
State Method ◽  
Local Detectors ◽  
Decoy State Method

Abstract In quantum key distribution (QKD), passive decoy-state method can simplify the intensity modulation and reduce some of side-channel information leakage and modulation errors. It is usually implemented with a heralded single-photon source. In [Physical Review A 96, 032312 (2016)], a novel passive decoy-state method is proposed by Wang et al., which uses two local detectors to generate more detection events for tightly estimating channel parameters. However, in original scheme, the two local detectors are assumed to be identical, including same detection efficiency and dark count rate, which is often not satisfied in realistic experiment. Therefore, in this paper, we construct a model for this passive decoy-state QKD scheme with two mismatched detectors and explore the effect on QKD performance with certain parameter. We also take the finite-size effect into consideration, showing the performance with statistical fluctuations. The results show that the efficiencies of local detectors affect the key rate more obviously than dark count rates. Our work provides a reference value for realistic QKD system.

scholarly journals Deep Insights into Convolutional Networks for Video Recognition

2019 ◽  
Vol 128 (2) ◽  
pp. 420-437 ◽  
Author(s):  
Christoph Feichtenhofer ◽  
Axel Pinz ◽  
Richard P. Wildes ◽  
Andrew Zisserman
Keyword(s):  
Computer Vision ◽  
Internal Representation ◽  
Training Data ◽  
Motion Patterns ◽  
Convolutional Networks ◽  
Video Recognition ◽  
Motion Features ◽  
Local Detectors ◽  
Local Representations ◽  
Shed Light

Abstract As the success of deep models has led to their deployment in all areas of computer vision, it is increasingly important to understand how these representations work and what they are capturing. In this paper, we shed light on deep spatiotemporal representations by visualizing the internal representation of models that have been trained to recognize actions in video. We visualize multiple two-stream architectures to show that local detectors for appearance and motion objects arise to form distributed representations for recognizing human actions. Key observations include the following. First, cross-stream fusion enables the learning of true spatiotemporal features rather than simply separate appearance and motion features. Second, the networks can learn local representations that are highly class specific, but also generic representations that can serve a range of classes. Third, throughout the hierarchy of the network, features become more abstract and show increasing invariance to aspects of the data that are unimportant to desired distinctions (e.g. motion patterns across various speeds). Fourth, visualizations can be used not only to shed light on learned representations, but also to reveal idiosyncrasies of training data and to explain failure cases of the system.

Sign in / Sign up

Export Citation Format

Share Document