Relevant bibliographies by topics / Hierarchical Spatio-Temporal Feature Maps

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Academic literature on the topic 'Hierarchical Spatio-Temporal Feature Maps'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Hierarchical Spatio-Temporal Feature Maps"

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Sheng, Jingwei, Li Zheng, Bingjiang Lyu, et al. "The Cortical Maps of Hierarchical Linguistic Structures during Speech Perception." Cerebral Cortex 29, no. 8 (2018): 3232–40. http://dx.doi.org/10.1093/cercor/bhy191.

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AbstractThe hierarchical nature of language requires human brain to internally parse connected-speech and incrementally construct abstract linguistic structures. Recent research revealed multiple neural processing timescales underlying grammar-based configuration of linguistic hierarchies. However, little is known about where in the whole cerebral cortex such temporally scaled neural processes occur. This study used novel magnetoencephalography source imaging techniques combined with a unique language stimulation paradigm to segregate cortical maps synchronized to 3 levels of linguistic units
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Sun, Tao, Yongjun Xu, Zhao Zhang, Lin Wu, and Fei Wang. "A Hierarchical Spatial-Temporal Embedding Method Based on Enhanced Trajectory Features for Ship Type Classification." Sensors 22, no. 3 (2022): 711. http://dx.doi.org/10.3390/s22030711.

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Ship type classification is an essential task in maritime navigation domains, contributing to shipping monitoring, analysis, and forecasting. Presently, with the development of ship positioning and monitoring systems, many ship trajectory acquisitions make it possible to classify ships according to their movement pattern. Existing methods of ship classification based on trajectory include classical sequence analysis and deep learning methods. However, the real ship trajectories are unevenly distributed in geographical space, which leads to many problems in inferring the ship movement mode on t
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Silva, Nilton Correia da, Osmar Abílio de Carvalho Júnior, Antonio Nuno de Castro Santa Rosa, Renato Fontes Guimarães, and Roberto Arnaldo Trancoso Gomes. "CHANGE DETECTION SOFTWARE USING SELF-ORGANIZING FEATURE MAPS." Revista Brasileira de Geofísica 30, no. 4 (2012): 505. http://dx.doi.org/10.22564/rbgf.v30i4.237.

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Os mapas auto-organizáveis (SOFM) consistem em um tipo de rede neural artificial que permite a conversão de dados de alta dimensão, complexos e não lineares, em simples relações geométricas com baixa dimensionalidade. Este método também pode ser utilizado para a classificação de imagens de sensoriamento remoto, pois permite a compressão de dados de alta dimensão preservando as relações topológicas dos dados primários. Este trabalho objetiva desenvolver uma metodologia eficaz para a utilização de mapas auto-organizáveis na detecção de mudanças. No presente estudo o SOFM é utilizado para a class
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Hu, Jie, Yunping Chen, Zhiwen Cai, et al. "Mapping Diverse Paddy Rice Cropping Patterns in South China Using Harmonized Landsat and Sentinel-2 Data." Remote Sensing 15, no. 4 (2023): 1034. http://dx.doi.org/10.3390/rs15041034.

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Paddy rice cropping patterns (PRCPs) play important roles in both agroecosystem modeling and food security. Although paddy rice maps have been generated over several regions using satellite observations, few studies have focused on mapping diverse smallholder PRCPs, which include crop rotation and are dominant cropping structures in South China. Here, an approach called the feature selection and hierarchical classification (FSHC) method was proposed to effectively identify paddy rice and its rotation types. Considering the cloudy and rainy weather in South China, a harmonized Landsat and Senti
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Han, Te, Yuqi Tang, Xin Yang, Zefeng Lin, Bin Zou, and Huihui Feng. "Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM)." Remote Sensing 13, no. 23 (2021): 4918. http://dx.doi.org/10.3390/rs13234918.

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To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then
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Sapienza, Davide, Davide Paganelli, Marco Prato, Marko Bertogna, and Matteo Spallanzani. "Deep learning-assisted analysis of automobiles handling performances." Communications in Applied and Industrial Mathematics 13, no. 1 (2022): 78–95. http://dx.doi.org/10.2478/caim-2022-0007.

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Abstract The luxury car market has demanding product development standards aimed at providing state-of-the-art features in the automotive domain. Handling performance is amongst the most important properties that must be assessed when developing a new car model. In this work, we analyse the problem of predicting subjective evaluations of automobiles handling performances from objective records of driving sessions. A record is a multi-dimensional time series describing the temporal evolution of the mechanical state of an automobile. A categorical variable quantifies the evaluations of handling
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Ma, Andong, and Anthony M. Filippi. "A novel spatial recurrent neural network for hyperspectral imagery classification." Abstracts of the ICA 1 (July 15, 2019): 1. http://dx.doi.org/10.5194/ica-abs-1-233-2019.

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<p><strong>Abstract.</strong> Hyperspectral images (HSIs) contain hundreds of spectral bands, providing high-resolution spectral information pertaining to the Earth’s surface. Additionally, abundant spatial contextual information can also be obtained simultaneously from a HSI. To characterize the properties of ground objects, classification is the most widely-used technology in the field of remote sensing, where each pixel in a HSI is assigned to a pre-defined class. Over the past decade, deep learning has attracted increasing attention in the machine-learning and computer-vi
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Pantho, Md Jubaer Hossain, Pankaj Bhowmik, and Christophe Bobda. "Towards an Efficient CNN Inference Architecture Enabling In-Sensor Processing." Sensors 21, no. 6 (2021): 1955. http://dx.doi.org/10.3390/s21061955.

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The astounding development of optical sensing imaging technology, coupled with the impressive improvements in machine learning algorithms, has increased our ability to understand and extract information from scenic events. In most cases, Convolution neural networks (CNNs) are largely adopted to infer knowledge due to their surprising success in automation, surveillance, and many other application domains. However, the convolution operations’ overwhelming computation demand has somewhat limited their use in remote sensing edge devices. In these platforms, real-time processing remains a challeng
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Chandrasekaran, V., M. Palaniswami, and T. M. Caelli. "Spatio-temporal feature maps using gated neuronal architecture." IEEE Transactions on Neural Networks 6, no. 5 (1995): 1119–31. http://dx.doi.org/10.1109/72.410356.

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Hussain, I., J. Pilz, and G. Spoeck. "Hierarchical Bayesian space-time interpolation versus spatio-temporal BME approach." Advances in Geosciences 25 (March 30, 2010): 97–102. http://dx.doi.org/10.5194/adgeo-25-97-2010.

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Abstract. The restrictions of the analysis of natural processes which are observed at any point in space or time to a purely spatial or purely temporal domain may cause loss of information and larger prediction errors. Moreover, the arbitrary combinations of purely spatial and purely temporal models may not yield valid models for the space-time domain. For such processes the variation can be characterized by sophisticated spatio-temporal modeling. In the present study the composite spatio-temporal Bayesian maximum entropy (BME) method and transformed hierarchical Bayesian space-time interpolat
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Dissertations / Theses on the topic "Hierarchical Spatio-Temporal Feature Maps"

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Low, Choy Samantha Jane. "Hierarchical models for 2D presence/absence data having ambiguous zeroes: With a biogeographical case study on dingo behaviour." Thesis, Queensland University of Technology, 2001. https://eprints.qut.edu.au/37098/12/Samantha%20Low%20Choy%20Thesis.pdf.

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This dissertation is primarily an applied statistical modelling investigation, motivated by a case study comprising real data and real questions. Theoretical questions on modelling and computation of normalization constants arose from pursuit of these data analytic questions. The essence of the thesis can be described as follows. Consider binary data observed on a two-dimensional lattice. A common problem with such data is the ambiguity of zeroes recorded. These may represent zero response given some threshold (presence) or that the threshold has not been triggered (absence). Suppose tha
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Machireddy, Amrutha. "Learning Non-linear Mappings from Data with Applications to Priority-based Clustering, Prediction, and Detection." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5670.

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With the volume of data generated in today's internet-of-things, learning algorithms to extract and understand the underlying relations between the various attributes of data have gained momentum. This thesis is focused on learning algorithms to extract meaningful relations from the data using both unsupervised and supervised learning algorithms. Vector quantization techniques are popularly used for applications in contextual data clustering, data visualization and high-dimensional data exploration. Existing vector quantization techniques, such as, the K-means and its variants and those der
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Conference papers on the topic "Hierarchical Spatio-Temporal Feature Maps"

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Machireddy, Amrutha, Prayag Gowgi, and Shayan Srinivasa Garani. "Extracting Temporal Correlations Using Hierarchical Spatio-Temporal Feature Maps." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534337.

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Lee, Ho-Keun, Sun-Kyu Kwon, Hee-Soo Kim, and Yeong-Ho Ha. "3D modeling using hierarchical feature point and spatio-temporal relationship." In Photonics West 2001 - Electronic Imaging, edited by Bernd Girod, Charles A. Bouman, and Eckehard G. Steinbach. SPIE, 2000. http://dx.doi.org/10.1117/12.411860.

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Papadopoulos, Konstantinos, Enjie Ghorbel, Djamila Aouada, and Bjorn Ottersten. "Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatio-Temporal Graph Convolutional Network for Action Recognition." In 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021. http://dx.doi.org/10.1109/icpr48806.2021.9413189.

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Fan, Wentao, Nizar Bouguila, and Xin Liu. "A hierarchical Dirichlet process mixture of GID Distributions with feature selection for spatio-temporal video modeling and segmentation." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952661.

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Jiang, P., I. Bychkov, J. Liu, and A. Hmelnov. "Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks." In 1st International Workshop on Advanced Information and Computation Technologies and Systems 2020. Crossref, 2021. http://dx.doi.org/10.47350/aicts.2020.09.

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Forecasting of air pollutant concentration, which is influenced by air pollution accumulation, traffic flow and industrial emissions, has attracted extensive attention for decades. In this paper, we propose a spatio-temporal attention convolutional long short term memory neural networks (Attention-CNN-LSTM) for air pollutant concentration forecasting. Firstly, we analyze the Granger causalities between different stations and establish a hyperparametric Gaussian vector weight function to determine spatial autocorrelation variables, which is used as part of the input feature. Secondly, convoluti
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Yao, Changqing, Hongquan Chen, Akhil Datta-Gupta, Sanjay Mawalkar, Srikanta Mishra, and Ashwin Pasumarti. "Robust CO2 Plume Imaging Using Joint Tomographic Inversion Of Distributed Pressure And Temperature Measurements." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206249-ms.

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Abstract Geologic CO2 sequestration and CO2 enhanced oil recovery (EOR) have received significant attention from the scientific community as a response to climate change from greenhouse gases. Safe and efficient management of a CO2 injection site requires spatio-temporal tracking of the CO2 plume in the reservoir during geologic sequestration. The goal of this paper is to develop robust modeling and monitoring technologies for imaging and visualization of the CO2 plume using routine pressure/temperature measurements. The streamline-based technology has proven to be effective and efficient for
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