A hybrid texture analysis system based on non-linear & oriented kernels, particle swarm optimization, and kNN vs. support vector machines

Title:

A hybrid texture analysis system based on non-linear & oriented kernels, particle swarm optimization, and kNN vs. support vector machines

Creator:

Peters, Stefanie and Koenig, Andreas

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:12bce73a-9c43-40cd-845e-536fbda08260
uuid:12bce73a-9c43-40cd-845e-536fbda08260

Subject:

Texture analysis, design automation, auto-configuration, PSO, and SVM

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The presented work reports on the progress of our methodology and framework for automated image processing and analysis systém design for industrial vision application. We focus on the important task of automated texture analysis, which is an essential component of automated quality-control systems. In this context, the portfolio of texture operators and assessment methods has been enlarged. Optimized operator parameterization is investigated using particle swarm optimization (PSO). A particular goal of this work is the investigation of support vector machines (SVM) as alternative assessment method for the operator parameter optimization, incorporating the efficient inclusion of SVM parameter settings in this optimization. Methods of the enhanced portfolio were applied employing benchmark textures, real application data from leather inspection, and synthetic textures including defects, specially designed to industrial needs. The key results of our work are that SVM is a highly esteemed and powerful assessment and classification method and parameter optimization, based, e.g., on SVM/PSO of standard and proprietary texture operators boosted performance in all cases. However, the appropriateness of a certain operator proved to be highly data-dependent, which advocates our methodology even more. Thus, the operator selection has been included and investigated for the synthetic textures. Summarising, our work provides a generic texture analysis system, even for unskilled users, that is automatically configured to the application. The method portfolio will be enlarged in future work.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2007 Volume:17 | Number:6

Harvested from:

CDK

Metadata only:

false