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检索条件"主题词=inference algorithms"

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Contextual Identity Recognition in Personal Photo Albums

Contextual Identity Recognition in Personal Photo Albums

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Dragomir Anguelov Kuang-chih Lee Salih Burak Gokturk Baris Sumengen Riya Inc. San Mateo CA USA

We present an efficient probabilistic method for identity recognition in personal photo albums. Personal photos are usually taken under uncontrolled conditions -the captured faces exhibit significant variations in pose, expression and illumination that limit the success of traditional face recognition algorithms. We show how to improve recognition rates by incorporating additional cues present in personal photo collections, such as clothing appearance and information about when the photo was taken. This is done by constructing a Markov random field (MRF) that effectively combines all available contextual cues in a principled recognition framework. Performing inference in the MRF produces markedly improved recognition results in a challenging dataset consisting of the personal photo collections of multiple people. At the same time, the computational cost of our approach remains comparable to that of standard face recognition approaches.

关键词： Clothing Face detection Face recognition Biological system modeling Markov random fields Lighting Computational efficiency inference algorithms Hair Boosting

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Optimizing Object Detection Efficiency for Autonomous Vehicles through the Integration of YOLOv4 and EfficientDet algorithms

Optimizing Object Detection Efficiency for Autonomous Vehicl...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： V. Aravinda Rajan Sridevi Sakhamuri A Periya Nayaki Swathi Agarwal Anurag Aeron M. Lawanyashri Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankovil India Department of IoT Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Department of Information Technology Velammal College of Engineering and Technology Madurai Tamilnadu India Department of IT CVR College of Engineering Hyderabad India Department of CSE MIET Meerut Meerut India SCORE Vellore Institute of Technology Vellore India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

Advancements in autonomous vehicles are highly dependent on object detection systems that need to balance accuracy and speed. This paper addresses the comparative analysis of three object detection algorithms such as integrated YOLOv4-EfficientDet model, stand alone YOLOv4, and stand alone EfficientDet for improved efficiency in autonomous vehicles. Yet the integrated YOLOv4-EfficientDet model, which is the result of a merger of real-time capabilities of the YOLOv4 and computational efficiency of the EfficientDet, turns out to be the best one. It attains the best Mean Average Precision (mAP) of 0.85 compared with the other algorithms, which indicates an excellent object detection accuracy. With a slightly decreased inference speed of 45 FPS frames per second, the integrated model shows a good balance between accuracy and speed. However, the standalone YOLOv4, known for its speed of 55 FPS, has a lower mAP value of 0.78. Although it gains a respectable mAP score of 0.82, the Standalone EfficientDet falls behind in inference speed at 35 FPS. Our comparative analysis highlights the utility of the YOLOv4-EfficientDet model in improving the efficiency of object detection for autonomous automobiles. It offers a convincingly accurate solution with a reasonable inference speed. The selection between these algorithms should focus on the unique needs and constraints of the autonomous vehicle system. This study's findings highlight the potential of using integrated models to improve the ability of autonomous vehicles to perceive their environment, making them safe and reliable on the roadways.

关键词： YOLO Analytical models Computational modeling Object detection Real-time systems inference algorithms Computational efficiency

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Visual-based on-road vehicle detection: A transnational experiment and comparison

Visual-based on-road vehicle detection: A transnational expe...

引用

IEEE Symposium on Intelligent Vehicle

作者： Chao Wang Huijing Zhao Chunzhao Guo Seiichi Mita Hongbin Zha Key Lab of Machine Perception (MOE) Peking University Beijing China Toyota Central R&D Labs. Inc. Nagakute Japan Research Center for Smart Vehicles Toyota Technological Institute Hisakata Nagoya Japan

As a key technique in ADAS (Advanced Driving Assistant System) or autonomous driving systems, visual-based on-road vehicle detection has been studied widely, while it faces still great challenges, among which are the complexity, diversity and unpredictable changes of the real-world environments. In the authors' previous work, an algorithm was developed in a probabilistic inference framework with its focus on solving the multi-view and occlusion problems at multi-lane motor way scenes. In this research, we seek to answer the questions: how efficient is the system during a long-term operation across a large area of changed conditions? To this end, a large scale experiment is conducted, where three testing data sets are developed containing the samples of more than 30,000 on Beijing's ring roads, 800 on Nagoya's fast road, and 3,000 on Nagoya's downtown streets, and the performance of visual-based vehicle detection concerning the multi-view and occlusion problems across extensive regions and at transnational environments are studied. We present our preliminary findings in this paper, which leads to a more extensive study in future work.

关键词： Vehicles Roads Vehicle detection Probabilistic logic Testing inference algorithms Data models

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Diagnosability Enhancement of Discrete Event Systems

Diagnosability Enhancement of Discrete Event Systems

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IEEE International Conference on Systems, Man and Cybernetics

作者： YuanLin Wen ChunHsi Li MuDer Jeng Department of Electrical Engineerig National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan Ocean University Keelung Taiwan

This paper presents an iterative systematic methodology for enhancing diagnosability of discrete event systems by adding sensors. The methodology consists of the following steps. First, Petri nets are used to model the target system. Then, an algorithm of polynomial complexity is adopted to analyze a sufficient condition of diagnosability of the modeled system. Here, diagnosability is defined in the context of the discrete event systems theory, which was first introduced by Sampath [9], If the system is found to be possibly non-diagnosable, T-components of the Petri net model are computed to find a location in the system for adding a sensor. The objective is to distinguish multiple T-components with the same observable event sequences. The diagnosability-checking algorithm is used again to see if the system with the newly added sensor is diagnosable. The process is repeated until either the system is diagnosable or diagnosability of the system cannot be enhanced. Examples are given in the paper to illustrate our approach.

关键词： Discrete event systems Sensor systems Sufficient conditions Polynomials Sensor fusion Petri nets Delay Cybernetics inference algorithms Formal languages

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Simultaneous Intent Prediction and State Estimation Using an Intent-Driven Intrinsic Coordinate Model

Simultaneous Intent Prediction and State Estimation Using an...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Jiaming Liang Bashar I. Ahmad Simon Godsill Signal Processing and Communications (SigProC) Laboratory University of Cambridge Cambridge U.K.

ISBN: (数字)9781728166629

ISBN: (纸本)9781728166636

The motion of an object (e.g. ship, jet, pedestrian, bird, drone, etc.) is usually governed by premeditated actions as per an underlying intent, for instance reaching a destination. In this paper, we introduce a novel intent-driven dynamical model based on a continuous-time intrinsic coordinate model. By combining this model with particle filtering, a seamless approach for jointly predicting the destination and estimating the state of a highly manoeuvrable object is developed. We examine the proposed inference technique using real data with different measurement models to demonstrate its efficacy. In particular, we show that the introduced approach can be a flexible and competitive alternative, in terms of prediction and estimation performance, to other existing methods for various measurement models including nonlinear ones.

关键词： Mathematical model Predictive models Force Particle measurements Kinematics Atmospheric measurements inference algorithms

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AXLE: Computationally-efficient trajectory smoothing using factor graph chains

AXLE: Computationally-efficient trajectory smoothing using f...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Edwin Olson University of Michigan

Factor graph chains– the special case of a factor graph in which there are no potentials connecting non-adjacent nodes– arise naturally in many robotics problems. Importantly, they are often part of an inner loop in trajectory optimization and estimation problems, and so applications can be very sensitive to the performance of a *** course, it is well-known that factor graph chains have an O(N) solution, but an actual solution is often left as "an exercise to the reader"… with the inevitable consequence that few (if any) efficient solutions are readily *** this paper, we carefully derive the solution while keeping track of the specific block structure that arises, we work through a number of practical implementation challenges, and we highlight additional optimizations that are not at first apparent. An easy-to-use and self-contained solver is provided in C, which outperforms the AprilSAM general-purpose sparse matrix factorization library by a factor of 7.3x even without specialized block *** name AXLE reflects the names of the key matrices involved (the approach here solves the linear problem AX = E by factoring A as LL^T), while also reflecting its key application in kino-dynamic trajectory estimation of vehicles with axles.

关键词： Tracking loops Smoothing methods Simultaneous localization and mapping Estimation Libraries inference algorithms Sparse matrices

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A fire detection system based on intelligent data fusion technology

A fire detection system based on intelligent data fusion tec...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Hong Bao Jun Li Xian-Yun Zeng Jing Zhang Guangdong University of Technology Guangzhou Guangdong China

ISBN: (纸本)0780378652

In this paper, multi-sensor data fusion technology is used in fire detection, which can better solve problems existing in traditional fire detection, such as lower-grade intelligence, high mis-warning rate, delay-warning etc. According to the fire signal's inherent character, a 3-layers data fusion structure is used. In this fire detecting system, the fire experiential characteristic and the fire data-fitting characteristic of fire signal data are fused by the fuzzy inference system to get the last fire probability. After introducing the "feedback" of cybernetics, a new algorithm that extracts the fire data-fitting characteristic is presented. The algorithm may improve the accuracy and quickness of early warning with a tendency feedback signal. Finally simulation experiments are done for typical flaming fire, typical smoldering fire and the fire under typical disturbance signals in kitchen environment. And satisfactory results are obtained.

关键词： Fires Intelligent systems Intelligent sensors Smoke detectors Delay Fuzzy systems inference algorithms Data mining Fuses Sensor fusion

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Toward an Understanding of User-Defined Conditional Preferences

Toward an Understanding of User-Defined Conditional Preferen...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Zhiyong Yu Zhiwen Yu Xingshe Zhou Yuichi Nakamura School of Computer Science Northwestern Polytechnical University Xi'an China Academic Center of Computing and Media Studies Kyoto University Kyoto Japan

User-defined preferences in a natural style is useful in the pervasive computing environment, but bring a great challenge to understand. People often express conditional as well as independent preferences. We propose an ontology-based quantitative model for conditional preferences that aims to enhance the inference capacity of conditional preference statements and thus reduce users' workload. Different interpretations of the statements of our model are depicted and compared, including the inheritance property of the concept hierarchy in ontology, the connotation of sufficient and necessary conditions, and the bipolar property of preferences in human thinking. An experiment in the trip domain is conducted and shows the feasibility of our conditional preference model.

关键词： Pervasive computing Ontologies Humans Computational intelligence Context awareness inference algorithms Computer science Recommender systems Transportation Costs

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Bayesian networks to support the management of patients with ASCUS/LSIL pap tests

Bayesian networks to support the management of patients with...

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EAI International Conference on Wireless Mobile Communication and Healthcare (Mobihealth)

作者： Panagiotis Bountris Charalampos Tsirmpas Maria Haritou Abraham Pouliakis Petros Karakitsos Dimitrios Koutsouris The Biomedical Engineering Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece The Institute of Communication and Computer Systems National Technical University of Athens Athens Greece The Department of Cytopathology University of Athens Athens Greece

In the majority of cases, cervical cancer (CxCa) develops as a result of underestimated abnormalities in the Pap test. Nowadays, there are ancillary molecular biology techniques providing important information related to CxCa and the Human Papillomavirus (HPV) natural history, including HPV DNA test, HPV mRNA tests and immunocytochemistry tests. However, these techniques have their own performance, advantages and limitations, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this paper we present a risk assessment model based on a Bayesian Network which, by combining the results of Pap test and ancillary tests, may identify women at true risk of developing cervical cancer and support the management of patients with ASCUS or LSIL cytology. The model, following the paradigm of other implemented systems, can be integrated into existing platforms and be available on mobile terminals for anytime/anyplace medical consultation.

关键词： Bayes methods Educational institutions DNA inference algorithms Risk management Neoplasms Accuracy

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Speech Enhancement Using Transient Speech Components

Speech Enhancement Using Transient Speech Components

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： C. Tantibundhit J.R. Boston C.C. Li J.D. Durrant S. Shaiman K. Kovacyk A. El-Jaroudi Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA University of Pittsburgh Pittsburgh PA US Department of Communication Science and Disorders University of Pittsburgh Pittsburgh PA USA

This paper describes an algorithm to decompose speech into tonal, transient, and residual components. The algorithm uses an MDCT-based hidden Markov chain model to isolate the tonal component and a wavelet-based hidden Markov tree model to isolate the transient component. We suggest that the auditory system, like the visual system, is probably sensitive to abrupt stimulus changes and that the transient component in speech may be particularly critical to speech perception. To test this suggestion, the transient component isolated by our algorithm was selectively amplified and recombined with the original speech to generate enhanced speech, with energy adjusted to be equal to the energy of the original speech. The intelligibility of the original and enhanced speech was evaluated in eleven human subjects by the modified rhyme protocol. The word recognition rates show that the enhanced speech can provide substantial improvement in speech intelligibility at low SNR levels (8% at -15 dB, 14% at -20 dB, and 18% at -25 dB)

关键词： Speech enhancement Hidden Markov models Gaussian distribution Wavelet coefficients Wavelet transforms Auditory system Visual system inference algorithms Testing Speech analysis

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