Break-Segment Detection and Recognition in Broadcasting Video/Audio Based on C/S Architecture

2009 ◽  
pp. 45-51 ◽  
Author(s):  
Xiangdong Wang ◽  
Xinhui Li ◽  
Yuelian Qian ◽  
Ying Yang ◽  
Shouxun Lin
Keyword(s):  
Detection And Recognition

Detection and recognition.

1978 ◽  
Vol 85 (3) ◽  
pp. 192-206 ◽  
Author(s):  
David M. Green ◽  
Theodore G. Birdsall
Keyword(s):  
Detection And Recognition

Processing of remote sensing data for detection and recognition of object classes

2004 ◽  
Vol 10 (5-6) ◽  
pp. 175-177
Author(s):  
S.L. Kravtsov ◽  
◽  
L.V. Oreshkina ◽  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Object Classes ◽  
Detection And Recognition

PECULIARITIES OF SMALL UNMANNED AERIAL VEHICLES DETECTION AND RECOGNITION

2019 ◽  
Vol 78 (9) ◽  
pp. 771-781 ◽  
Author(s):  
V. M. Kartashov ◽  
V. N. Oleynikov ◽  
S. A. Sheyko ◽  
S. I. Babkin ◽  
I. V. Korytsev ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Detection And Recognition

CNN-based Rain Reduction in Street View Images

2020 ◽  
Vol 2020 (1) ◽  
pp. 78-81
Author(s):  
Simone Zini ◽  
Simone Bianco ◽  
Raimondo Schettini
Keyword(s):  
Character Recognition ◽  
Optical Character Recognition ◽  
State Of The Art ◽  
Weather Conditions ◽  
Specific Interest ◽  
Optical Character ◽  
Street View ◽  
In The Wild ◽  
Bad Weather ◽  
Detection And Recognition

Rain removal from pictures taken under bad weather conditions is a challenging task that aims to improve the overall quality and visibility of a scene. The enhanced images usually constitute the input for subsequent Computer Vision tasks such as detection and classification. In this paper, we present a Convolutional Neural Network, based on the Pix2Pix model, for rain streaks removal from images, with specific interest in evaluating the results of the processing operation with respect to the Optical Character Recognition (OCR) task. In particular, we present a way to generate a rainy version of the Street View Text Dataset (R-SVTD) for "text detection and recognition" evaluation in bad weather conditions. Experimental results on this dataset show that our model is able to outperform the state of the art in terms of two commonly used image quality metrics, and that it is capable to improve the performances of an OCR model to detect and recognise text in the wild.

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