Results 51 to 60 of about 2,450,419 (369)

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector [PDF]

, 2017
The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology.
Acciarri, R., Adams, C., An, R., Anthony, J., Asaadi, J., Auger, M., Bagby, L., Balasubramanian, S., Baller, B., Barnes, C., Barr, G., Bass, M., Bay, F., Bishai, M., Blake, A., Bolton, T., Caicedo, D. A. Martinez, Camilleri, L., Caratelli, D., Carls, B., Cavanna, F., Chen, H., Church, E., Cianci, D., Cohen, E., Collin, G. H., Conrad, J. M., Convery, M., Crespo-Anadon, J. I., de Vries, J. Jan, Del Tutto, M., Devitt, D., Dytman, S., Eberly, B., Ereditato, A., Esquivel, J., Fadeeva, A. A., Fernandez, R. Castillo, Fleming, B. T., Foreman, W., Furmanski, A. P., Garcia-Gomez, D., Garvey, G. T., Genty, V., Goeldi, D., Gollapinni, S., Graf, N., Gramellini, E., Greenlee, H., Grosso, R., Guenette, R., Hackenburg, A., Hamilton, P., Hen, O., Hewes, J., Hill, C., Ho, J., Horton-Smith, G., Hourlier, A., Huang, E. -C., James, C., Jen, C. -M., Jiang, L., John, J. St., Johnson, R. A., Joshi, J., Jostlein, H., Kaleko, D., Karagiorgi, G., Ketchum, W., Kirby, B., Kirby, M., Kobilarcik, T., Kreslo, I., Laube, A., Li, Y., Lister, A., Littlejohn, B. R., Lockwitz, S., Lorca, D., Louis, W. C., Luethi, M., Lundberg, B., Luo, X., Marchionni, A., Mariani, C., Marshall, J., Meddage, V., Miceli, T., MicroBooNE collaboration, Mills, G. B., Moon, J., Mooney, M., Moore, C. D., Mousseau, J., Murrells, R., Naples, D., Nienaber, P., Nowak, J., Palamara, O., Paolone, V., Papavassiliou, V., Pate, S. F., Pavlovic, Z., Piasetzky, E., Porzio, D., Pulliam, G., Qian, X., Raaf, J. L., Rafique, A., Rochester, L., Russell, B., Sanchez, L. Escudero, Schmitz, D. W., Schukraft, A., Seligman, W., Shaevitz, M. H., Sinclair, J., Smith, A., Snider, E. L., Soderberg, M., Soldner-Rembold, S., Soleti, S. R., Spentzouris, P., Spitz, J., Strauss, T., Szelc, A. M., Tagg, N., Terao, K., Thomson, M., Toups, M., Tsai, Y. -T., Tufanli, S., Usher, T., Van De Pontseele, W., Van de Water, R. G., Viren, B., von Rohr, C. Rudolf, Weber, M., Wickremasinghe, D. A., Wolbers, S., Wongjirad, T., Woodruff, K., Yang, T., Yates, L., Zeller, G. P., Zennamo, J., Zhang, C.   +147 more
core   +2 more sources

Image Search with Text Feedback by Visiolinguistic Attention Learning [PDF]

, 2020
Image search with text feedback has promising impacts in various real-world applications, such as e-commerce and internet search. Given a reference image and text feedback from user, the goal is to retrieve images that not only resemble the input image ...
Bazzani, L, Chen, Y, Gong, S, IEEE Conference on Computer Vision and Pattern Recognition   +3 more
core   +1 more source

The unpredictably eruptive dynamics of spruce budworm populations in eastern Canada

Population Ecology, EarlyView.
We examine historical population data for spruce budworm from several locations through the period 1930–1997, and use density‐dependent recruitment curves to test whether the pattern of population growth over time is more consistent with Royama's (1984; Ecological Monographs 54:429–462) linear R(t) model of harmonic oscillation at Green River New ...
Barry J. Cooke, Jacques Régnière
wiley   +1 more source

A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

Journal of Electrical and Computer Engineering, 2016
Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy.
Johannes Jordan, Elli Angelopoulou, Andreas Maier   +2 more
doaj   +1 more source

Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors

Sensors, 2018
Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches—in particular deep-learning based—have been proposed to extract an effective ...
Frédéric Li, Kimiaki Shirahama, Muhammad Adeel Nisar, Lukas Köping, Marcin Grzegorzek   +4 more
doaj   +1 more source

Contour-Aware Polyp Segmentation in Colonoscopy Images Using Detailed Upsampling Encoder-Decoder Networks

IEEE Access, 2020
Colorectal cancer has become one of the most common cause of cancer mortality worldwide, with a five-year survival rate of over 50%. Additionally, the potential of some common polyp types to progress to colorectal cancer is considered high.
Ngoc-Quang Nguyen, Duc My Vo, Sang-Woong Lee   +2 more
doaj   +1 more source

Quantum Face Recognition Protocol with Ghost Imaging [PDF]

arXiv, 2021
Face recognition is one of the most ubiquitous examples of pattern recognition in machine learning, with numerous applications in security, access control, and law enforcement, among many others. Pattern recognition with classical algorithms requires significant computational resources, especially when dealing with high-resolution images in an ...
arxiv  

Network topology drives population temporal variability in experimental habitat networks

Population Ecology, EarlyView.
Habitat patches connected by dispersal pathways form habitat networks. We explored how network topology affects population outcomes in laboratory experiments using a model species (Daphnia carinata). Central habitat nodes in complex lattice networks exhibited lower temporal variability in population sizes, suggesting they support more stable ...
Yiwen Xu, J. Angus Webb, William D. Bovill, Elise L. King   +3 more
wiley   +1 more source

Pattern Recognition and Reconstruction [PDF]

, 2011
This document is part of Part 1 'Principles and Methods' of Subvolume B 'Detectors for Particles and Radiation' of Volume 21 'Elementary Particles' of Landolt-Bornstein - Group I 'Elementary Particles, Nuclei and Atoms'. It contains the Section '4.3 Pattern Recognition and Reconstruction' of Chapter '4 Data Treatment and Analysis Methods' with the ...
Frühwirth, Rudolf, Brondolin, E, Strandlie, Are   +2 more
openaire   +4 more sources

Pattern recognition in bioinformatics [PDF]

Briefings in Bioinformatics, 2013
Pattern recognition is concerned with the development of systems that learn to solve a given problem using a set of example instances, each represented by a number of features. These problems include clustering, the grouping of similar instances; classification, the task of assigning a discrete label to a given instance; and dimensionality reduction ...
Dick de Ridder, Jeroen de Ridder, Marcel J. T. Reinders   +2 more
openaire   +2 more sources