A Randomized Heuristic for Scene Recognition by Graph Matching

2004 ◽  
pp. 100-113 ◽  
Author(s):  
Maria C. Boeres ◽  
Celso C. Ribeiro ◽  
Isabelle Bloch
Keyword(s):  
Graph Matching ◽  
Scene Recognition

Scene Recognition in Pigeons

2000 ◽  
Author(s):  
Jennifer E. Sutton ◽  
William A. Roberts
Keyword(s):  
Scene Recognition

Scene Recognition with Infrared, Low-Light, and Sensor-Fused Imagery

1999 ◽  
Author(s):  
Michael J. Sinai ◽  
Jason S. McCarley ◽  
William K. Krebs
Keyword(s):  
Scene Recognition ◽  
Low Light

User Manual and Installation Guide for the Graph Matching Toolkit (GMT) Version 1.0

2014 ◽  
Author(s):  
Heather Roy ◽  
Kirk Ogaard ◽  
Sue Kase
Keyword(s):  
Graph Matching

Structural information and (hyper)graph matching for MRI piglet brain extraction

2019 ◽  
Author(s):  
A. Durandeau ◽  
J.-B. Fasquel ◽  
I. Bloch ◽  
E. Mazerand ◽  
P. Menei ◽  
...  
Keyword(s):  
Graph Matching ◽  
Structural Information ◽  
Brain Extraction

scholarly journals Design of Desktop Audiovisual Entertainment System with Deep Learning and Haptic Sensations

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1718
Author(s):  
Chien-Hsing Chou ◽  
Yu-Sheng Su ◽  
Che-Ju Hsu ◽  
Kong-Chang Lee ◽  
Ping-Hsuan Han
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
User Experience ◽  
Recognition System ◽  
Scene Recognition ◽  
Single Shot ◽  
Auditory Signals ◽  
Hot Weather ◽  
Viewing Experience ◽  
At Home

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.

scholarly journals Wireless Channel Scene Recognition Method Based on an Autocorrelation Function and Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226324-226336
Author(s):  
Shuguang Ning ◽  
Yigang He ◽  
Lifen Yuan ◽  
Yuan Huang ◽  
Shudong Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autocorrelation Function ◽  
Wireless Channel ◽  
Scene Recognition ◽  
Recognition Method
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