scholarly journals On the recognition problem for virtually special cube complexes

2018 ◽  
Author(s):  
Martin R. Bridson ◽  
Henry Wilton
Keyword(s):  
Recognition Problem ◽  
Cube Complexes

Fusion of biometric algorithms in the recognition problem

2005 ◽  
Vol 26 (5) ◽  
pp. 679-684 ◽  
Author(s):  
Andrew L. Rukhin ◽  
Igor Malioutov
Keyword(s):  
Recognition Problem

scholarly journals Property A and CAT(0) cube complexes

2009 ◽  
Vol 256 (5) ◽  
pp. 1408-1431 ◽  
Author(s):  
J. Brodzki ◽  
S.J. Campbell ◽  
E. Guentner ◽  
G.A. Niblo ◽  
N.J. Wright
Keyword(s):  
Property A ◽  
Cube Complexes

IMAGE RECOGNITION BY INTEGRATION OF CONNECTIONIST AND SYMBOLIC APPROACHES

1995 ◽  
Vol 09 (03) ◽  
pp. 485-515 ◽  
Author(s):  
F. ROLI ◽  
S. B. SERPICO ◽  
G. VERNAZZA
Keyword(s):  
Image Recognition ◽  
Magnetic Resonance Image ◽  
Contextual Information ◽  
Recognition System ◽  
Recognition Problem ◽  
Training Process ◽  
Neural Architecture ◽  
Recognition Phase ◽  
Abstraction Levels

This paper presents a methodology for integrating connectionist and symbolic approaches to 2D image recognition. The proposed integration paradigm exploits the synergy of the two approaches for both the training and the recognition phases of an image recognition system. In the training phase, a symbolic module provides an approximate solution to a given image-recognition problem in terms of symbolic models. Such models are hierarchically organized into different abstraction levels, and include contextual descriptions. After mapping such models into a complex neural architecture, a neural training process is carried out to optimize the solution of the recognition problem. The so-obtained neural networks are used during the recognition phase for pattern classification. In this phase, the role of symbolic modules consists of managing complex aspects of information processing: abstraction levels, contextual information, and global recognition hypotheses. A hybrid system implementing the proposed integration paradigm is presented, and its advantages over single approaches are assessed. Results on Magnetic Resonance image recognition are reported, and comparisons with some well-known classifiers are made.

On a development of sparse PCA method for face recognition problem

2021 ◽  
Author(s):  
Loc Tran ◽  
Bich Ngo ◽  
Tuan Tran ◽  
Lam Pham ◽  
An Mai
Keyword(s):  
Face Recognition ◽  
Recognition Problem ◽  
Sparse Pca ◽  
Pca Method

scholarly journals Ping Pong on CAT(0) cube complexes

2016 ◽  
Vol 91 (3) ◽  
pp. 543-561 ◽  
Author(s):  
Aditi Kar ◽  
Michah Sageev
Keyword(s):  
Ping Pong ◽  
Cube Complexes
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