Image-Data-Driven Dynamically-Reconfigurable Multiprocessor System In Automated Histopathology

1986 ◽  
Author(s):  
R. L. Shoemaker ◽  
P. H. Bartels ◽  
H. Bartels ◽  
W. G. Griswold ◽  
D. Hillman ◽  
...  
Keyword(s):  
Image Data ◽  
Data Driven ◽  
Multiprocessor System ◽  
Dynamically Reconfigurable

Dynamically Reconfigurable Multiprocessor System For Scene Segmentation In Histopathology

1989 ◽  
Author(s):  
R. L. Shoemaker ◽  
O. Stucky ◽  
R. Manner ◽  
D. B. Thompson ◽  
W. G. Griswold ◽  
...  
Keyword(s):  
Multiprocessor System ◽  
Scene Segmentation ◽  
Dynamically Reconfigurable

scholarly journals Outfit Browser – An Image-data-driven User Interface for Self-service Systems in Fashion Stores

2015 ◽  
Vol 3 ◽  
pp. 3521-3528 ◽  
Author(s):  
Alexander Piazza ◽  
Christian Zagel ◽  
Sebastian Huber ◽  
Matthias Hille ◽  
Freimut Bodendorf
Keyword(s):  
User Interface ◽  
Image Data ◽  
Service Systems ◽  
Data Driven ◽  
Self Service

scholarly journals Thermal-Aware Task Mapping on Dynamically Reconfigurable Network-on-Chip Based Multiprocessor System-on-Chip

2018 ◽  
Vol 67 (12) ◽  
pp. 1818-1834 ◽  
Author(s):  
Weichen Liu ◽  
Lei Yang ◽  
Weiwen Jiang ◽  
Liang Feng ◽  
Nan Guan ◽  
...  
Keyword(s):  
Network On Chip ◽  
System On Chip ◽  
Multiprocessor System ◽  
Task Mapping ◽  
Dynamically Reconfigurable ◽  
On Chip

scholarly journals Toward Image Data-Driven Predictive Modeling for Guiding Thermal Ablative Therapy

2020 ◽  
Vol 67 (6) ◽  
pp. 1548-1557
Author(s):  
Jarrod A. Collins ◽  
Jon S. Heiselman ◽  
Logan W. Clements ◽  
Jared A. Weis ◽  
Daniel B. Brown ◽  
...  
Keyword(s):  
Predictive Modeling ◽  
Image Data ◽  
Data Driven ◽  
Ablative Therapy

MODEL-BASED OBJECT RECOGNITION USING PROBABILISTIC LOGIC AND MAXIMUM ENTROPY

1991 ◽  
Vol 05 (03) ◽  
pp. 425-437 ◽  
Author(s):  
THOMAS B. KANE ◽  
PATRICK McANDREW ◽  
ANDREW M. WALLACE
Keyword(s):  
Maximum Entropy ◽  
Image Data ◽  
Data Driven ◽  
Probabilistic Logic ◽  
Simulated Image ◽  
Two Dimensional Image ◽  
Reasoning System ◽  
Scene Description ◽  
Iconic Image ◽  
Local Edge

In the visual context, a reasoning system should he capable of inferring a scene description using evidence derived from data-driven processing of the iconic image data. This evidence may consist of a set of curvilinear boundaries, which are obtained by grouping local edge data into extended features. Using linear primitives, a framework is described which represents the information contained in pre-formed models of possible objects in the scene, and in the segmented scenes themselves. A method based on maximum entropy is developed which assigns measures of likelihood for the presence of objects in the two-dimensional image. This method is applied to and evaluated on real and simulated image data, and the effectiveness of the approach is discussed.

scholarly journals Data-driven image restoration with option-driven learning for big and small astronomical image data sets

2020 ◽  
Vol 501 (1) ◽  
pp. 291-301
Author(s):  
Peng Jia ◽  
Runyu Ning ◽  
Ruiqi Sun ◽  
Xiaoshan Yang ◽  
Dongmei Cai
Keyword(s):  
Image Restoration ◽  
Learning Strategies ◽  
Simulated Data ◽  
Image Data ◽  
Data Driven ◽  
Generative Adversarial Networks ◽  
Supervised Learning Algorithms ◽  
Reference Images ◽  
Astronomical Images ◽  
Restoration Method

ABSTRACT Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data-driven image restoration methods. However, most of these methods belong to supervised learning algorithms, which require paired images either from real observations or simulated data as training set. For some applications, it is hard to get enough paired images from real observations and simulated images are quite different from real observed ones. In this paper, we propose a new data-driven image restoration method based on generative adversarial networks with option-driven learning. Our method uses several high-resolution images as references and applies different learning strategies when the number of reference images is different. For sky surveys with variable observation conditions, our method can obtain very stable image restoration results, regardless of the number of reference images.

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