Novel object detection and recognition system based on points of interest selection and SVM classification

2018 ◽  
Vol 52 ◽  
pp. 985-994 ◽  
Author(s):  
R. Bhuvaneswari ◽  
Ravi Subban
Keyword(s):  
Object Detection ◽  
Recognition System ◽  
Novel Object ◽  
Svm Classification ◽  
Points Of Interest ◽  
Detection And Recognition

scholarly journals GPGPU-based real-time object detection and recognition system

2010 ◽  
Vol 10 (7) ◽  
pp. 997-997 ◽  
Author(s):  
D. Parks ◽  
A. Jain ◽  
J. McInerney ◽  
L. Itti
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Recognition System ◽  
Detection And Recognition

An object detection and recognition system for weld bead extraction from digital radiographs

2006 ◽  
Vol 102 (3) ◽  
pp. 238-249 ◽  
Author(s):  
Marcelo Kleber Felisberto ◽  
Heitor Silvério Lopes ◽  
Tania Mezzadri Centeno ◽  
Lúcia Valéria Ramos de Arruda
Keyword(s):  
Object Detection ◽  
Recognition System ◽  
Weld Bead ◽  
Digital Radiographs ◽  
Detection And Recognition

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.

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