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检索条件"主题词=Detection algorithms"

共 7359 条记录，以下是241-250 订阅

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Comparative Study on Foreground detection algorithms for Human detection

Comparative Study on Foreground Detection Algorithms for Hum...

引用

International Conference on Image and Graphics (ICIG)

作者： Y. Benezeth B. Emile C. Rosenberger Laboratoire Vision et Robotique-UPRES EA 2078 ENSI de Bourges-Université d''Orléans Bourges France

The objective of this paper is to develop an approach to objectively evaluate foreground detection algorithms. The purpose of the desired application is human detection in images sequences. This paper presents in a first part the state of the art on foreground detection algorithms. In a second part, we use realistic synthetic videos to perform our evaluation. This kind of videos is very interesting for the evaluation study because it permits to evaluate the influences of some artifacts (changing light, video quality, shadow). We use a common metric to quantify the efficiency of the 11 selected algorithms. Time computation is also considered.

关键词： detection algorithms Humans Videos Pixel Image sequences Intelligent robots Protocols Image databases Graphics Robot vision systems

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Soft data detection algorithms for an iterative turbo coded MIMO OFDM systems

Soft data detection algorithms for an iterative turbo coded ...

引用

Asilomar Conference on Signals, Systems & Computers

作者： K.J. Kim T. Reid R.A. Iltis Communications Laboratory Nokia Research Center Irving TX USA Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

The use of multiple antennas at both transmitter and receiver can significantly increase the channel capacity. By using orthogonal frequency division multiplexing (OFDM) transmission techniques, a MIMO-OFDM system can achieve high spectral efficiency, which makes it an attractive candidate for high-data-rate wireless applications. In this paper, we propose an iterative soft data detection algorithm, called the soft-QRD-M structure for the MIMO OFDM system, which can incorporate a kind of coding. Iteratively communicating the soft informations between the proposed MIMO data detector and the Turbo decoder, we can improve the BER performance significantly.

关键词： detection algorithms Turbo codes MIMO OFDM Receiving antennas Transmitting antennas Transmitters Channel capacity Detectors Iterative decoding

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DECONSTRUCTING OPTIMAL detection algorithms

DECONSTRUCTING OPTIMAL DETECTION ALGORITHMS

引用

Workshop on Hyperspectral Image and Signal Processing

作者： Alan Schaum US Naval Research Laboratory

Composite hypothesis testing problems admit a class of solutions that are as optimal as possible, absent a cost function. These indomitable detectors comprise a superset of all optimal solutions. However, evaluating them with finite precision arithmetic can generate unstable answers. Here we devise a general method for deriving indomitables, using an extended fusion technique. Then we show how to reverse the process to generate arbitrarily accurate approximations to indomitables that are also stable.

关键词： Detectors Light rail systems detection algorithms Testing Bayes methods Fuses Mathematical model

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On Out-of-Distribution detection algorithms with Deep Neural Skin Cancer Classifiers

On Out-of-Distribution Detection Algorithms with Deep Neural...

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Andre G. C. Pacheco Chandramouli S. Sastry Thomas Trappenberg Sageev Oore Renato A. Krohling Federal University of Espirito Santo Vitória Brazil Dalhousie University Halifax Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Computer-aided skin cancer detection systems built with deep neural networks yield overconfident predictions on out-of-distribution examples. Motivated by the importance of out-of-distribution detection in these systems and the lack of relevant benchmarks targeted for skin cancer classification, we introduce a rich collection of out-of-distribution datasets - designed to comprehensively evaluate state-of-the-art out-of-distribution algorithms with skin cancer classifiers. In addition, we propose an adaptation in the Gram-Matrix algorithm for out-of-distribution detection that generally performs better and faster than the original algorithm for the considered skin cancer classification task. We also include a detailed discussion comparing the various state-of-the-art out-of-distribution detection algorithms and identify avenues for future research.

关键词： Training Melanoma Neural networks detection algorithms Dogs Skin

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A survey on community detection algorithms in large scale real world networks

A survey on community detection algorithms in large scale re...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： S Rao Chintalapudi M H M Krishna Prasad Department of CSE University College of Engineering Kakinada (A) JNTUK Kakinada AP INDIA

ISBN: (纸本)9781479968329

Community Structure is one of the most relevant features of real world networks. Detecting such structures in large scale networks is a challenging task in scientific world. These are similar to clusters in which intra cluster density is more than the inter cluster density. This paper reviews the prominent community detection algorithms that detect both disjoint and overlapped communities. These algorithms are experimented on benchmark dataset Zachary's karate club. Obtained number of communities is compared with the ground truth. The quality measures namely modularity and Normalized Mutual Information (NMI) are computed for all disjoint community detection algorithms. As a result of voluminous research done in this area the overlapped communities are come into the picture. Overlapped community means that a node in the network may be affiliated to more than one community. To test these algorithms Omega index is also included in this survey. After reviewing all these algorithms, this survey concludes that quality and scalability are the major issues in this area and also the measure used for detecting communities needs more computational power. So, one need to use either high performance computing framework with Graphical Processing Units (GPU) or Hadoop framework for distributed computing. Hence, this will balance the trade-off between running time and quality.

关键词： Communities detection algorithms Image edge detection Algorithm design and analysis Benchmark testing Graphics processing units Social network services

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Asynchronous interpolated servo detection algorithms

Asynchronous interpolated servo detection algorithms

引用

International Conference on Magnetics (INTERMAG)

作者： V. Annampedu P.M. Aziz Agere Systems Allentown PA USA Agere Systems Dallas TX USA

Recording systems for disk drives read and detect data from tracks consisting of "read" data sectors and "servo" sectors between read sectors. In this work, we (1) present a new asynchronous servo detection algorithm based on interpolation (2) quantitatively demonstrate improvements in performance of the asynchronous algorithm over the prior synchronous algorithm in the presence of moderate to high RI. Lastly, we propose to use an asynchronous algorithm, which dispenses with the baud rate timing loop and uses an asynchronously sampled ADC output for servo detection.

关键词： Servomechanisms detection algorithms Phase detection Detectors Timing Maximum likelihood detection Frequency synchronization Filters Disk drives Viterbi algorithm

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Parallel Implementation of Target and Anomaly detection algorithms for Hyperspectral Imagery

Parallel Implementation of Target and Anomaly Detection Algo...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Abel Paz Antonio Plaza Soraya Blazquez Department of Technology of Computers and Communications University of Extremadura Caceres Spain

This paper develops several parallel algorithms for target detection in hyperspectral imagery, considered to be a crucial goal in many remote sensing applications. In order to illustrate parallel performance of the proposed parallel algorithms, we consider a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center. Experimental results, collected by the AVIRIS sensor over the World Trade Center, just five days after the terrorist attacks, indicate that commodity cluster computers can be used as a viable tool to increase computational performance of hyperspectral target detection applications.

关键词： detection algorithms Hyperspectral imaging Partitioning algorithms Hyperspectral sensors Object detection Iterative algorithms Concurrent computing Parallel algorithms Terrorism Parallel processing

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Analysis of Community detection algorithms

Analysis of Community Detection Algorithms

引用

International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： R Jaya Krishna Yash Chaudhry Devi Prasad Sharma Manipal University Jaipur Rajasthan India

Community detection is starting to become an inevitable task for data science community and like-minded analysts to understand the hidden insights and trends in large complex networks. Through this paper we would like to understand and comparatively analyze the prevalent and recent community detection techniques like Minimum Cut Method, Hierarchical Clustering, Girvan Newman Algorithm, Modularity Maximization, Statistical Inference, Clique Percolation & Maximum Permanence Method based on their network relevance, adaptability and robustness to changes and finally their capacity of size and speed. Finally, we will review the realworld application of these algorithms.

关键词： Clustering algorithms Inference algorithms Conferences detection algorithms Computational modeling Facebook

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A Parallel LFR-like Benchmark for Evaluating Community detection algorithms

A Parallel LFR-like Benchmark for Evaluating Community Detec...

引用

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： George M. Slota Jack Garbus Rensselaer Polytechnic Institute Troy NY

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Community detection is one of the most widely-used graph analytics. As recent community detection algorithms have been targeting large-scale networks, an emerging problem is how best to evaluate the output of these algorithms. Common measures such as modularity have several well-known issues, so comparisons against a notion of a “ground truth” community structure, such as in the Lancicinetti-Fortunato-Radicchi (LFR) benchmark, is preferred. This current work targets the parallel generation of graphs matching the specifications of the LFR benchmark. We are able to generate such graphs at the billion-edge scale in seconds, giving orders-of-magnitude speedup relative to prior work.

关键词： Benchmark testing Generators Instruction sets Twitter Complexity theory Probabilistic logic detection algorithms

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Misregistration errors in change detection algorithms and how to avoid them

Misregistration errors in change detection algorithms and ho...

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IEEE International Conference on Image Processing

作者： D. Farin P.H.N. de With University슠of슠Technology Eindhoven Eindhoven Netherlands

Background subtraction is a popular algorithm for video object segmentation. It identifies foreground objects by comparing the input images with a pure background image. In camera-motion compensated sequences, small errors in the motion estimation can lead to large image differences along sharp edges. Consequently, the errors in the image registration finally lead to segmentation errors. This paper proposes a computationally efficient approach to detect image areas having a high risk of showing misregistration errors. Furthermore, we describe how existing change detection algorithms can be modified to avoid segmentation errors in these areas. Experiments show that our algorithm can improve the segmentation quality. The algorithm is memory efficient and suitable for real-time processing.

关键词： detection algorithms Cameras Change detection algorithms Object segmentation Image edge detection Image segmentation Motion estimation Partitioning algorithms Object detection Sprites (computer)

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