Building a visual database for example-based graphics generation

2003 ◽  
Author(s):  
M.X. Zhou ◽  
Min Chen ◽  
Ying Feng
Keyword(s):  
Visual Database

PAVID-CV s: Persian Audio-Visual Database of CV syllables

2021 ◽  
Author(s):  
Mahsa Hedayatipour ◽  
Yasser Shekofteh ◽  
Mohsen Ebrahimi Moghaddam
Keyword(s):  
Visual Database

Audio-Visual Database for Spanish-Based Speech Recognition Systems

2019 ◽  
pp. 452-460
Author(s):  
Diana-Margarita Córdova-Esparza ◽  
Juan Terven ◽  
Alejandro Romero ◽  
Ana Marcela Herrera-Navarro
Keyword(s):  
Speech Recognition ◽  
Visual Database ◽  
Recognition Systems

scholarly journals Image Retrieval Based on Fractal Dictionary Parameters

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Yuanyuan Sun ◽  
Rudan Xu ◽  
Lina Chen ◽  
Xiaopeng Hu
Keyword(s):  
Computer Vision ◽  
Image Compression ◽  
Image Retrieval ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Efficient Management ◽  
Retrieval Rate ◽  
Statistical Indices ◽  
Visual Database

Content-based image retrieval is a branch of computer vision. It is important for efficient management of a visual database. In most cases, image retrieval is based on image compression. In this paper, we use a fractal dictionary to encode images. Based on this technique, we propose a set of statistical indices for efficient image retrieval. Experimental results on a database of 416 texture images indicate that the proposed method provides a competitive retrieval rate, compared to the existing methods.

CHEAVD: a Chinese natural emotional audio–visual database

2016 ◽  
Vol 8 (6) ◽  
pp. 913-924 ◽  
Author(s):  
Ya Li ◽  
Jianhua Tao ◽  
Linlin Chao ◽  
Wei Bao ◽  
Yazhu Liu
Keyword(s):  
Visual Database

Co-Evolution Genetic Algorithm for UAV Distributed Tracking in Urban Environments

2008 ◽  
Author(s):  
Vitaly Shaferman ◽  
Tal Shima
Keyword(s):  
Genetic Algorithm ◽  
Population Based ◽  
Urban Environments ◽  
Distributed Search ◽  
Test Bed ◽  
Team Members ◽  
Solution Approach ◽  
Distributed Approach ◽  
Visual Database ◽  
Cooperative Tracking

A distributed approach is proposed for planning a cooperative tracking task for a team of unmanned aerial vehicles (UAVs). In the scenario of interest UAVs are required to autonomously track, using their onboard sensors, a moving target in a known urban environment. The solution methodology involves finding visibility regions, from which a UAV can maintain a line of sight to the target during the scenario; and restricted regions, in which a UAV can not fly, due to the presence of buildings or other airspace limitations. A co-evolution genetic algorithm is derived for searching, in realtime, monotonically improving solutions. In the proposed distributed search method every UAV iteratively manipulates its own population of chromosomes, each encoding its control inputs in the calculated horizon. Team performance is attained by assigning fitness to each solution in the population based on the cooperative performance when using it together with preceding iteration tracking information obtained from teammates. Important attributes of the proposed solution approach are its scalability and robustness; and consequently it can be applied to large sized problems and adapt to the loss of UAV team members. The distributed nature of the algorithm also reduces the computation and communication loads. The performance of the algorithm is studied using a high fidelity simulation test-bed incorporating a visual database of the city of Tel-Aviv, Israel.

Sign in / Sign up

Export Citation Format

Share Document