Recognition Algorithm Design and Complex Analysis for Languages of S-Nets

2010 ◽  
Vol 10 (1) ◽  
pp. 106-112 ◽  
Author(s):  
Tingting Cui ◽  
Qingtian Zeng ◽  
Dongli Zhang
Keyword(s):  
Complex Analysis ◽  
Algorithm Design ◽  
Recognition Algorithm

Automated Design of Image Recognition Process for Picking System

2016 ◽  
Vol 10 (5) ◽  
pp. 737-752 ◽  
Author(s):  
Taiki Ogata ◽  
◽  
Kazuaki Tsujimoto ◽  
Taigo Yukisawa ◽  
Yanjiang Huang ◽  
...  
Keyword(s):  
Image Recognition ◽  
Optimization Problem ◽  
Algorithm Design ◽  
Optimization Method ◽  
Automated Design ◽  
Recognition Algorithm ◽  
Design System ◽  
Patterned Surfaces ◽  
Recognition Method ◽  
F Measure

In this study, we propose an automated design system for an image recognition algorithm applicable to picking work in general and actual factory environments. Considering that an image recognition algorithm design consists of frameworks for selecting a rough recognition method from any of the three basic procedures of pre-processing of contained images, feature-extraction, and discrimination, we formulate it as an optimization problem and propose a random multi-start optimization method by which to derive solutions. We have conducted four types of evaluation experiments for the following four combinations: large or small degrees of similarity in the shape of objects to be recognized and whether they have patterned surfaces. The evaluation experiments show that the proposed design system succeeds in selecting a framework that fits the features of the objects to be recognized and that the designed basic processes have an F measure of 0.9 or more.

scholarly journals A New Evolutionary-Incremental Framework for Feature Selection

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Mohamad-Hoseyn Sigari ◽  
Muhammad-Reza Pourshahabi ◽  
Hamid-Reza Pourreza
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Algorithm Design ◽  
Feature Selection Method ◽  
Recognition Algorithm ◽  
Comparison Result ◽  
Open Problems ◽  
Invasive Weed ◽  
Np Hard Problem ◽  
Robust Face Recognition

Feature selection is an NP-hard problem from the viewpoint of algorithm design and it is one of the main open problems in pattern recognition. In this paper, we propose a new evolutionary-incremental framework for feature selection. The proposed framework can be applied on an ordinary evolutionary algorithm (EA) such as genetic algorithm (GA) or invasive weed optimization (IWO). This framework proposes some generic modifications on ordinary EAs to be compatible with the variable length of solutions. In this framework, the solutions related to the primary generations have short length. Then, the length of solutions may be increased through generations gradually. In addition, our evolutionary-incremental framework deploys two new operators called addition and deletion operators which change the length of solutions randomly. For evaluation of the proposed framework, we use that for feature selection in the application of face recognition. In this regard, we applied our feature selection method on a robust face recognition algorithm which is based on the extraction of Gabor coefficients. Experimental results show that our proposed evolutionary-incremental framework can select a few number of features from existing thousands features efficiently. Comparison result of the proposed methods with the previous methods shows that our framework is comprehensive, robust, and well-defined to apply on many EAs for feature selection.

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