- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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3D Shape Analysis (Fundamentals, Theory, and Applications) || 3D Face Recognition
摘要： The automatic recognition of human faces has many potential applications in various fields including security and human–computer interaction. An accurate and robust face recognition system needs to discriminate between the faces of different people under variable conditions. The main challenge is that faces, from a general perspective, look similar and their differences can be very subtle. They all have the same structure and are composed of similar components (e.g. nose, eyes, and mouth). On the other hand, the appearance of the same face can considerably change under variable extrinsic factors, e.g. the camera position and the intensity and direction of light, and intrinsic factors such as the head position and orientation, facial expressions, age, skin color, and gender. On that basis, face recognition can be considered to be more challenging than the general object recognition problem discussed in Chapter 11. Pioneer researchers initially focused on 2D face recognition, i.e. how to recognize faces from data captured using monocular cameras. They reported promising recognition results, particularly in controlled environments. With the recent popularity of cost-effective 3D acquisition systems, face recognition systems are starting to benefit from the availability, advantages, and widespread use of 3D data. In this chapter, we review some of the recent advances in 3D face recognition. We will first present, in Section 10.2, the various 3D facial datasets and benchmarks that are currently available to researchers and then discuss the challenges and evaluation criteria. Section 10.3 will review the key 3D face recognition methods. Section 10.4 provides a summary and discussions around this chapter.
关键词： local feature-based matching,face identification,face verification,holistic approaches,challenges,3D face recognition,datasets
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Recent Advances and Opportunities in Scene Classification of Aerial Images with Deep Models
摘要： Scene classification is a fundamental task in interpretation of remote sensing images, and has become an active research topic in remote sensing community due to its important role in a wide range of applications. Over the past years, tremendous efforts have been made for developing powerful approaches for scene classification of remote sensing images, evolving from the traditional bag-of-visual-words model to the new generation deep convolutional neural networks (CNNs). The deep CNN based methods have exhibited remarkable break-through on performance, dramatically outperforming previous methods which strongly rely on hand-crafted features. However, performance with deep CNNs has gradually plateaued on existing public scene datasets, due to the notable drawbacks of these datasets, such as the small scale and low-diversity of training samples. Therefore, to promote the development of new methods and move the scene classification task a step further, we deeply discuss the existing problems in scene classification task, and accordingly present three open directions. We believe these potential directions will be instructive for the researchers in this field.
关键词： Scene classification,deep models,domain adaptation,datasets,scene caption
更新于2025-09-23 15:22:29
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Hour-ahead photovoltaic power forecast using a hybrid GRA-LSTM model based on multivariate meteorological factors and historical power datasets
摘要： Owing to the clean, inexhaustible and pollution-free, solar energy has become a powerful means to solve energy and environmental problems. However, photovoltaic (PV) power generation varies randomly and intermittently with respect to the weather, which bring the challenge to the dispatching of PV electrical power. Thus, power forecasting for PV power generation has become one of the key basic technologies to overcome this challenge. The paper presents a grey relational analysis (GRA) and long short-term memory recurrent neural network (LSTM RNN) (GRA-LSTM) model-based power short-term forecasting of PV power plants approach. The GRA algorithm is adopted to select the similar hours from history dataset, and then the LSTM NN maps the nonlinear relationship between the multivariate meteorological factors and power data. The proposed model is verified by using the dataset of the PV systems from the Desert Knowledge Australia Solar Center (DKASC). The prediction results of the method are contrasted with those obtained by LSTM, grey relational analysis-back propagation neural network (GRA-BPNN), grey relational analysis-radial basis function neural network (GRA-RBFNN) and grey relational analysis-Elman neural network (GRA-Elman), respectively. Results show an acceptable and robust performance of the proposed model.
关键词： photovoltaic power forecast,GRA-LSTM model,historical power datasets,multivariate meteorological factors
更新于2025-09-16 10:30:52
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Assessment of Artificial Neural Networks Learning Algorithms and Training Datasets for Solar Photovoltaic Power Production Prediction
摘要： The capability of accurately predicting the Solar Photovoltaic (PV) power productions is crucial to effectively control and manage the electrical grid. In this regard, the objective of this work is to propose an efficient Artificial Neural Network (ANN) model in which 10 different learning algorithms (i.e., different in the way in which the adjustment on the ANN internal parameters is formulated to effectively map the inputs to the outputs) and 23 different training datasets (i.e., different combinations of the real-time weather variables and the PV power production data) are investigated for accurate 1 day-ahead power production predictions with short computational time. In particular, the correlations between different combinations of the historical wind speed, ambient temperature, global solar radiation, PV power productions, and the time stamp of the year are examined for developing an efficient solar PV power production prediction model. The investigation is carried out on a 231 kWac grid-connected solar PV system located in Jordan. An ANN that receives in input the whole historical weather variables and PV power productions, and the time stamp of the year accompanied with Levenberg-Marquardt (LM) learning algorithm is found to provide the most accurate predictions with less computational efforts. Specifically, an enhancement reaches up to 15, 1, and 5% for the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2) performance metrics, respectively, compared to the Persistence prediction model of literature.
关键词： learning algorithms,training datasets,solar photovoltaic,persistence,Artificial Neural Networks,power prediction
更新于2025-09-12 10:27:22
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CUDA-based Volume Rendering and Inspection for Time-varying Ultrasonic Testing Datasets
摘要： We present a framework for time-varying 3D UT datasets based on volume real-time CUDA-based rendering with high quality. In addition, we design a novel trapezoid-shaped transfer functions (TF) with color gradient. Furthermore, we propose an interactive 3D inspection method via clipping plane with enhanced ability of the cross-section data.
关键词： Clipping Plane,Volume Rendering,Ultrasonic Testing,Transfer Function,CUDA,Time-varying Datasets
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Surface Deformation of Kangding Airport, Qinghai-Tibet Plateau, China Using Insar Techniques and Multi-Temporal Sentinel-1 Datasets
摘要： Kangding Airport, Sichuan, China located on the eastern margin of the Qinghai-Tibet Plateau has been developed in rugged terrain. The airport environment is characterized by high filling of earth materials, high frequencies and intensities of seismic activities, and high altitude. The understanding of potential risk of the 3-high airport assessed by the surface deformation is of vital importance. In this study, the Small Baseline Subset (SBAS) and Quasi Persistent Scatterer (QPS) techniques were used and proven to be effective to quantify the long-term surface deformation of the airport. The deformation was highly correlated with the local geological settings and annual climate cycle.
关键词： SBAS and QPS InSAR techniques,Airport surface deformation,high-filling with earth materials,Sentinel-1 datasets
更新于2025-09-10 09:29:36
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[Studies in Computational Intelligence] Recent Advances in Computer Vision Volume 804 (Theories and Applications) || Hyperspectral Image: Fundamentals and Advances
摘要： Hyperspectral remote sensing has received considerable interest in recent years for a variety of industrial applications including urban mapping, precision agriculture, environmental monitoring, and military surveillance as well as computer vision applications. It can capture hyperspectral image (HSI) with a lager number of land-cover information. With the increasing industrial demand in using HSI, there is a must for more ef?cient and effective methods and data analysis techniques that can deal with the vast data volume of hyperspectral imagery. The main goal of this chapter is to provide the overview of fundamentals and advances in hyperspectral images. The hyperspectral image enhancement, denoising and restoration, classical classi?cation techniques and the most recently popular classi?cation algorithm are discussed with more details. Besides, the standard hyperspectral datasets used for the research purposes are covered in this chapter.
关键词： image enhancement,restoration,Hyperspectral imaging,classification,remote sensing,denoising,datasets
更新于2025-09-04 15:30:14