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Your search keyword '"Schomaker, Lambert R. B."' showing total 22 results
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22 results on '"Schomaker, Lambert R. B."'

1. Ferroelastic domain walls in BiFeO$_3$ as memristive networks

Author

Rieck, Jan, Cipollini, Davide, Salverda, Mart, Quinteros, Cynthia P., Schomaker, Lambert R. B., and Noheda, Beatriz

Subjects
Condensed Matter - Materials Science, Physics - Applied Physics
Abstract

Electronic conduction along individual domain walls (DWs) has been reported in BiFeO$_3$ (BFO) and other nominally insulating ferroelectrics. DWs in these materials separate regions of differently oriented electrical polarization (domains) and are just a few atoms wide, providing self-assembled nanometric conduction paths. In this work, it is shown that electronic transport is possible also from wall to wall through the dense network of as-grown DWs in BFO thin films. Electric field cycling at different points of the network, performed locally by conducting atomic force microscope (cAFM), induces resistive switching selectively at the DWs, both for vertical (single wall) and lateral (wall-to-wall) conduction. These findings are the first step towards investigating DWs as memristive networks for information processing and in-materio computing.

Published
2022

2. A fully automated end-to-end process for fluorescence microscopy images of yeast cells: From segmentation to detection and classification

Author

Haja, Asmaa and Schomaker, Lambert R. B.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In recent years, an enormous amount of fluorescence microscopy images were collected in high-throughput lab settings. Analyzing and extracting relevant information from all images in a short time is almost impossible. Detecting tiny individual cell compartments is one of many challenges faced by biologists. This paper aims at solving this problem by building an end-to-end process that employs methods from the deep learning field to automatically segment, detect and classify cell compartments of fluorescence microscopy images of yeast cells. With this intention we used Mask R-CNN to automatically segment and label a large amount of yeast cell data, and YOLOv4 to automatically detect and classify individual yeast cell compartments from these images. This fully automated end-to-end process is intended to be integrated into an interactive e-Science server in the PerICo1 project, which can be used by biologists with minimized human effort in training and operation to complete their various classification tasks. In addition, we evaluated the detection and classification performance of state-of-the-art YOLOv4 on data from the NOP1pr-GFP-SWAT yeast-cell data library. Experimental results show that by dividing original images into 4 quadrants YOLOv4 outputs good detection and classification results with an F1-score of 98% in terms of accuracy and speed, which is optimally suited for the native resolution of the microscope and current GPU memory sizes. Although the application domain is optical microscopy in yeast cells, the method is also applicable to multiple-cell images in medical applications

Published
2021

3. A Fully Automated End-to-End Process for Fluorescence Microscopy Images of Yeast Cells: From Segmentation to Detection and Classification

Author

Haja, Asmaa, Schomaker, Lambert R. B., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Su, Ruidan, editor, Zhang, Yu-Dong, editor, and Liu, Han, editor

Published
2022
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5. Deep Learning with Data Augmentation for Fruit Counting

Author

Pawara, Pornntiwa, Boshchenko, Alina, Schomaker, Lambert R. B., Wiering, Marco A., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2020
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8. Recognizing Handwritten Characters with Local Descriptors and Bags of Visual Words

Author

Surinta, Olarik, Karaaba, Mahir F., Mishra, Tusar K., Schomaker, Lambert R. B., Wiering, Marco A., Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Iliadis, Lazaros, editor, and Jayne, Chrisina, editor

Published
2015
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11. Using Local Symmetry for Landmark Selection

Author

Kootstra, Gert, de Jong, Sjoerd, Schomaker, Lambert R. B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fritz, Mario, editor, Schiele, Bernt, editor, and Piater, Justus H., editor

Published
2009
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12. Ferroelastic Domain Walls in BiFeO3 as Memristive Networks.

Author

Rieck, Jan L., Cipollini, Davide, Salverda, Mart, Quinteros, Cynthia P., Schomaker, Lambert R. B., and Noheda, Beatriz

Subjects
ATOMIC force microscopy, NANOSATELLITES, FERROELECTRIC thin films, HUMAN information processing, INFORMATION networks, THIN films, ELECTRIC fields
Abstract

Electronic conduction along individual domain walls (DWs) is reported in BiFeO3 (BFO) and other nominally insulating ferroelectrics. DWs in these materials separate regions of differently oriented electrical polarization (domains) and are just a few atoms wide, providing self‐assembled nanometric conduction paths. Herein, it is shown that electronic transport is possible also from wall‐to‐wall through the dense network of as‐grown DWs in BFO thin films. Electric field cycling at different points of the network, performed locally by conducting atomic force microscopy (cAFM), induces resistive switching selectively at the DWs, both for vertical (single wall) and lateral (wall‐to‐wall) conduction. These findings are the first step toward investigating DWs as memristive networks for information processing and in-materio computing. [ABSTRACT FROM AUTHOR]

Published
2023
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21. ONLINE HANDWRITING RECOGNITION.

Author

Plamondon, Réjean, Lopresti, Daniel P., Schomaker, Lambert R. B., and Srihari, Rohini

Subjects
WRITING, COMMUNICATION, WRITTEN communication, BUSINESS writing, SIGNATURES (Writing), IDENTIFICATION of legal documents
Abstract

This article presents information related to online handwriting recognition, that has been a research challenge for almost 40 years. Contrary to offline techniques, which deal with the automatic processing of the image of handwritten words as collected using a camera or a scanner, online approaches aim at recognizing words as they are written, using a digitizer or an instrumented pen to capture pen-tip position as a function of time. The major difference between online and offline data is that the time sequence of movements for online data is directly available from the acquired signals. This allows also the computation of other signals, like velocity signals, from the original data.

Published
1999

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