We introduce what is believed to be a novel concept by which several sensors with automatic target recognition (ATR) capability collaborate to recognize objects. Such an approach would be suitable for netted systems in which the sensors and platforms can coordinate to optimize end-to-end performance. We use correlation filtering techniques to facilitate the development of the concept, although other ATR algorithms may be easily substituted. Essentially, a self-configuring geometry of netted platforms is proposed that positions the sensors optimally with respect to each other, and takes into account the interactions among the sensor, the recognition algorithms, and the classes of the objects to be recognized. We show how such a paradigm optimizes overall performance, and illustrate the collaborative ATR scheme for recognizing targets in synthetic aperture radar imagery by using viewing position as a sensor parameter.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/ao.45.007365 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Examining the impact of clinical features and built environment on risk of hospital onset infection.
Infect Control Hosp Epidemiol
January 2025
Department of Biostatistics and Data Science, Wake Forest University, School of Medicine, Medical Center Blvd, Winston-Salem, NC27157, USA.
Objective: Environmental features of a patient's room depend on the patient's level of acuity and their clinical manifestations upon admission and during their hospital stay. In this study, we wish to apply statistical methodology to explore the association between room features and hospital onset infections caused by (HO-CDI) while accounting for room assignment.
Method: We conducted a nested case-control study using retrospective electronic health record (EHR) data of patients hospitalized at the Ohio State University Wexner Medical Center (OSUWMC) between January 2019 and April 2021.
The Added Effect of Artificial Intelligence in CT Assessment of Abdominal Lymphadenopathy.
Lymphology
January 2025
Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt.
Lymphadenopathy is associated with lymph node abnormal size or consistency due to many causes. We employed the deep convolutional neural network ResNet-34 to detect and classify CT images from patients with abdominal lymphadenopathy and healthy controls. We created a single database containing 1400 source CT images for patients with abdominal lymphadenopathy (n = 700) and healthy controls (n = 700).
View Article and Find Full Text PDFOptimal placement of high-channel visual prostheses in human retinotopic visual cortex.
J Neural Eng
January 2025
Faculty of Psychology, University of Maastricht, P.O. Box 616, 6200 MD Maastricht, The Netherlands, Maastricht, 6211 LK, NETHERLANDS.
Recent strides in neurotechnology show potential to restore vision in individuals afflicted with blindness due to early visual pathway damage. As neuroprostheses mature and become available to a larger population, manual placement and evaluation of electrode designs becomes costly and impractical. An automatic method to optimize the implantation process of electrode arrays at large-scale is currently lacking.
View Article and Find Full Text PDFTrueSpot: A robust automated tool for quantifying signal puncta in fluorescent imaging.
bioRxiv
January 2025
Department of Molecular Physiology and Biophysics, School of Medicine, Vanderbilt University, Nashville, TN, 37232, USA.
Characterizing the movement of biomolecules in single cells quantitatively is essential to understanding fundamental biological mechanisms. RNA fluorescent in situ hybridization (RNA-FISH) is a technique for visualizing RNA in fixed cells using fluorescent probes. Automated processing of the resulting images is essential for large datasets.
View Article and Find Full Text PDFDeep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach.
Sci Rep
January 2025
Department of Electrical Electronical Engineering, Yaşar University, Bornova, İzmir, Turkey.
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!