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Experimental and Theoretical Scrutiny of the Geometric Derivation of the Fundamental Matrix 20

Experimental and Theoretical Scrutiny of the Geometric Deriv...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Basta, Tayeb Al Ghurair Univ Coll Engn & Comp Dubai U Arab Emirates

ISBN: (纸本)9781450375511

In this paper, we prove mathematically that the geometric derivation of the fundamental matrix.. of the two-view reconstruction problem is flawed. Although the fundamental matrix approach is quite classic, it is still taught in universities around the world. Thus, analyzing the derivation of F now is a non-trivial subject. The geometric derivation of E is based on the cross product of vectors in R-3. The cross product (or vector product) of two vectors is x x y where.. = < x(1), x(2), x(3)> and y = < y(1), y(2), y(3)> in R-3. The relationship between the skew-matrix of a vector.. in R-3 and the cross product is [t](x) y = t x y for any vector t in R3. In the derivation of the essential matrix we have E = [t](x).. which is the result of replacing t x R by [t](x) R, the cross product of a vector t and a 3x3 matrix R. This is an undefined operation and therefore the essential matrix derivation is flawed. The derivation of F, is based on the assertion that the set of all points in the first image and their corresponding points in the second image are protectively equivalent and therefore there exists a homography H-pi between the two images. An assertion that does not hold for 3D non-planar scenes.

关键词： Cross product skew-matrix essential matrix fundamental matrix epipolar geometry stereo vision 3D reconstruction

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A Facial Expression Recongnition Method Based on Dlib, RI-LBP and ResNet 3

A Facial Expression Recongnition Method Based on Dlib, RI-LB...

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3rd international conference on Computer Information Science and Application Technology (CISAT)

作者： Yang, Jianxing Adu, Jianhua Chen, Hongguang Zhang, Jie Tang, Jianfeng Chengdu Univ Informat Technol Sch Software Engn Chengdu 611230 Sichuan Peoples R China

A method of facial expression recognition using a composite feature is proposed. The method combines the expanded Dlib facial feature detector, the rotation-invariant local binary pattern (RI-LBP) and the 50-layer ResNet neural network model (ResNet_50). First, the expanded Dlib was used to locate 83 feature points on the face, obtainting the Dlib feature after preprocessing and dimentionality reduction (PCA). Then, the rotation-invariant LBP feature was extracted from 8 important regions after tilt correction. Furthermore, a 50-layer ResNet neural network was used to extract the low level features from the images. Finally, the three features were combined and extreme learning machine (ELM) was used to classify the composite facial features. The experimental results on Jaffe and CK+ datasets showed that the proposed method performs better compared with other methods.

关键词： Local binary pattern

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Local Binary pattern Histogram for Face recognition in Student Attendance System 3

Local Binary Pattern Histogram for Face Recognition in Stude...

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3rd international conference on Computer and Informatics Engineering, IC2IE 2020

作者： Alexander, Allan D Salkiawati, Ratna Lubis, Hendarman Rahman, Fathur Herlawati, Herlawati Handayanto, Rahmadya Trias Universitas Bhayangkara Jakarta Raya Bekasi Dept. of Informatics Engineering Bekasi Indonesia Universitas Islam 45 Bekasi Dept. of Computer Engineering Bekasi Indonesia

ISBN: (纸本)9781728182476

Student attendance record has an important role in the educational process. Universitas Bhayangkara Jakarta Raya, as a case study, uses attendance record as the factor for final grade calculation. Many attendance recording systems were developed using biometrics, e.g. face recognition, iris recognition, and fingerprint recognition. In this study, face recognition was proposed since the face cannot be duplicated and can eliminate fraud committed by students. In addition, this contactless method could minimize the risk of COVID-19 spread with some additional treatments. The local binary pattern (LBP) was proposed in this study. This method has the ability to describe the texture and shape of an image by dividing the image into small portions of feature extraction. The result showed that the proposed system can identify students with 86% accuracy. © 2020 IEEE.

关键词： Face recognition

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Improving Single Shot Detector for Industrial Cracks by Feature Resolution Analysis 20

Improving Single Shot Detector for Industrial Cracks by Feat...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Qi, Shengxiang Dong, Yaming Mao, Qing Shanghai Elect Grp Co Ltd Cent Acad 960 Zhongxing Rd Shanghai Peoples R China

ISBN: (纸本)9781450375511

Although the single shot detector (SSD) is effective for object detection in natural images, it is not suitable for special tasks such as the industrial crack detection. The difficulty lies in the wide diversity of crack sizes and shapes that is usually unpredictable. To solve this problem, we improve the SSD model by feature resolution analysis. The classical SSD network extracts several convolutional feature layers with degressive scales, and then classifies and locates targets by a series of prior boxes with default sizes and aspect ratios regarding to each scale. Therefore, the key is whether the design of these prior boxes is consistent with the real target characteristics. In this paper, we improve the architecture of SSD network via statistically analyzing the distribution of sizes and shapes from our collected crack samples. According to the resolution analysis of the targets at each feature scale, only a fewer number of valid feature layers are carefully extracted, and some more accurate prior boxes are designed relative to each scale. Finally, experimental results demonstrate that the proposed method could not only achieve significantly better prediction accuracy, but also acquire higher computational efficiency, which outperform the state-of-the-art methods.

关键词： Object detection Computer vision Deep learning Industrial application

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2020 3rd international conference on Intelligent Autonomous Systems, ICoIAS 2020

2020 3rd International Conference on Intelligent Autonomous ...

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3rd international conference on Intelligent Autonomous Systems, ICoIAS 2020

ISBN: (纸本)9781728160788

The proceedings contain 25 papers. The topics discussed include: complete scene parsing for autonomous navigation in unstructured environments;semi-supervised meta-learning via self-training;value iteration solver networks;research on pulsar classification based on machine learning;bias and raising threshold algorithm using learning agents for the best proportion-searching problem;a study on layers of deep neural networks;motion-blur-free high-frame-rate vision system with frame-by-frame visual-feedback control for a resonant mirror;complete scene parsing for autonomous navigation in unstructured environments;pattern recognition of dynamic social network;and a comparative study on HSV-based and deep learning-based object detection algorithms for pedestrian traffic light signal recognition.

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Staged Reinforcement Learning for Complex Tasks Through Decomposed Environments 3rd

Staged Reinforcement Learning for Complex Tasks Through Dec...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Pina, Rafael Artaud, Corentin Liu, Xiaolan De Silva, Varuna Institute for Digital Technologies Loughborough University London 3 Lesney Avenue E20 3BS London United Kingdom

ISBN: (纸本)9783031463372

Reinforcement Learning (RL) is an area of growing interest in the field of artificial intelligence due to its many notable applications in diverse fields. Particularly within the context of intelligent vehicle control, RL has made impressive progress. However, currently it is still in simulated controlled environments where RL can achieve its full super-human potential. Although how to apply simulation experience in real scenarios has been studied, how to approximate simulated problems to the real dynamic problems is still a challenge. In this paper, we discuss two methods that approximate RL problems to real problems. In the context of traffic junction simulations, we demonstrate that, if we can decompose a complex task into multiple sub-tasks, solving these tasks first can be advantageous to help minimising possible occurrences of catastrophic events in the complex task. From a multi-agent perspective, we introduce a training structuring mechanism that exploits the use of experience learned under the popular paradigm called Centralised Training Decentralised Execution (CTDE). This experience can then be leveraged in fully decentralised settings that are conceptually closer to real settings, where agents often do not have access to a central oracle and must be treated as isolated independent units. The results show that the proposed approaches improve agents performance in complex tasks related to traffic junctions, minimizing potential safety-critical problems that might happen in these scenarios. Although still in simulation, the investigated situations are conceptually closer to real scenarios and thus, with these results, we intend to motivate further research in the subject. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Policy Generation from Latent Embeddings for Reinforcement Learning 3rd

Policy Generation from Latent Embeddings for Reinforcement...

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3rd international conference on Intelligent Systems and pattern recognition, ISPR 2023

作者： Artaud, Corentin Pina, Rafael Shi, Xiyu De-Silva, Varuna Institute for Digital Technologies Loughborough University London The Broadcast Centre Here East 3 Lesney Ave E20 3BS London United Kingdom

ISBN: (纸本)9783031463372

The human brain endows us with extraordinary capabilities that enable us to create, imagine, and generate anything we desire. Specifically, we have fascinating imaginative skills allowing us to generate fundamental knowledge from abstract concepts. Motivated by these traits, numerous areas of machine learning, notably unsupervised learning and reinforcement learning, have started using such ideas at their core. Nevertheless, these methods do not come without fault. A fundamental issue with reinforcement learning especially now when used with neural networks as function approximators is their limited achievable optimality compared to its uses from tabula rasa. Due to the nature of learning with neural networks, the behaviours achievable for each task are inconsistent and providing a unified approach that enables such optimal policies to exist within a parameter space would facilitate both the learning procedure and the behaviour outcomes. Consequently, we are interested in discovering whether reinforcement learning can be facilitated with unsupervised learning methods in a manner to alleviate this downfall. This work aims to provide an analysis of the feasibility of using generative models to extract learnt reinforcement learning policies (i.e. model parameters) with the intention of conditionally sampling the learnt policy-latent space to generate new policies. We demonstrate that under the current proposed architecture, these models are able to recreate policies on simple tasks whereas fail on more complex ones. We therefore provide a critical analysis of these failures and discuss further improvements which would aid the proliferation of this work. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Research on Pollution Gas Position Based on Multilayer Perceptron Algorithm 3

Research on Pollution Gas Position Based on Multilayer Perce...

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3rd international conference on Artificial Intelligence and Advanced Manufacture, AIAM 2021

作者： Lin, Hongyang Fuzhou Sunshine International School Fuzhou350015 China

ISBN: (纸本)9781450385046

At present, the national treatment and prevention measures for industrial exhaust gas pollution have transitioned from industrial upgrading, capacity reduction, and energy structure regulation to the stage of precise positioning and precise treatment due to the phenomenon of stealthy and excessive discharge of exhaust gas in industrial parks, which has brought serious threats to the safety of the atmospheric environment. In addition, the exhaust gas emission of industrial parks is characterized by randomness, abruptness, complex gas composition and large gas emission. Therefore, traditional pollution location methods such as emission inventory method and grid method are not effective. Based on the above problems, this paper proposes an air pollution gas location strategy based on neural network multi-layer perceptron algorithm. Through pattern recognition, fuzzy linear discriminant function is applied, and the feature space is segmented by hyperplane discriminant boundary and the fuzzy area is retained. Determine the orientation of the fuzzy discriminant surface by measuring the weight vector of a specific neuron and formulate a method to initialize the initial weight of the network on the hypersphere. Determine the weight initialization hypersphere by measuring the distance from the origin to the discriminant surface and the offset and then to further determine the specific location information of the polluted gas. After proposing the control strategy, this paper performed a specific simulation verification on the MATLAB platform. The verification results show that the algorithm strategy can greatly reduce the learning time of the neural network, improve the convergence performance of the network, and significantly improve the accuracy of polluting gas positioning. © 2021 ACM.

关键词： Multilayer neural networks

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The Lagrangian Relaxation Approach for Home Health Care Problems 12th

The Lagrangian Relaxation Approach for Home Health Care Prob...

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12th international conference on Soft Computing and pattern recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Dastgoshade, Sohaib Abraham, Ajith Fozooni, Nazanin Department of Industrial Engineering Yazd University Yazd Iran Scientific Network for Innovation and Research Excellence 11 3rd Street NW P.O. Box 2259 Auburn WA98071 United States Department of Industrial Engineering K. N. Toosi University of Technology Tehran Iran

ISBN: (纸本)9783030736880

Today, life expectancy and demand for caring for the elderly have been increased in developing countries. However, hospitals and nursing homes have not grown at the same rate. Thus, the home health care problem has been addressed by health academics and practitioners. The home care industry is a unique part of health care with growing demand, which has not been adequately addressed in the past. Based on the assumptions of this context and considering time windows and coordination constraints, a Lagrangian relaxation-based mixed-integer programming model is developed in this paper. To solve this model, since it is NP-hard, two metaheuristic algorithms, including simulated annealing and social engineering optimization, were applied. The results showed that the simulated annealing algorithm outperformed the other algorithm. Besides, a complete sensitivity analysis was performed to investigate the effect of the important parameters on the model outputs. As well as some perspectives on future works will be suggested. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sensitivity analysis

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Compartmental spiking neuron model for pattern classification 3

Compartmental spiking neuron model for pattern classificatio...

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3rd international Scientific conference on Applied Physics, Information Technologies and Engineering, APITECH-III 2021

作者： Astapova, L.A. Korsakov, A.M. Bakhshiev, A.V. Eremenko, E.A. Smirnova, E.Y. Saint-Petersburg Russia Institute of Machinery Mate-rials and Transport Higher School of Automation and Robotics Saint-Petersburg Russia

One of the directions of development within the framework of the neuromorphic approach is the development of anatomically similar models of brain networks, taking into account the structurally complex structure of neurons and the adaptation of connections between them, as well as the development of learning algorithms for such models. In this work, we use the previously presented compartmental spike model of a neuron, which describes the structure (dendritic tree, soma, synapses) and behaviour (temporal and spatial signal summation, generation of action potential, stimulation and suppression of electrical activity) of a biological neuron. An algorithm for the structural organization of neuron models into a spike neural network is proposed for recognizing an arbitrary impulse pattern by introducing inhibitory synapses between trained neuron models. The dynamically adapting neuron models used are trained according to a previously proposed algorithm that automatically selects parameters such as soma size, dendrite length, and the number of synapses on each of the dendrites in order to induce a temporal response at the output depending on the input pattern encoded using a time window and temporal delays in the vector of single spikes arriving at a separate dendrite of a neuron. The developed algorithms are evaluated on the Iris dataset classification problem with four training examples from each class. As a result of the classification, separate disjoint clusters are formed, which demonstrates the applicability of the proposed spike neural network with a dynamically changing structure of elements in the problem of pattern recognition and classification. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Neurons

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