Structural Synthesis based on PCA: Methodology and Evaluation

Sriniwas Chowdhary Maddukuri, Wolfgang Heidl, Christian Eitzinger, Andreas Pichler


In recent surface inspections systems, interactive training of fault classification is becoming state of the art. While being most informative for both training and explanation, fault samples at the decision boundary are rare in production datasets. Therefore, augmenting the dataset with synthesized samples at the decision boundary could greatly accelerate the training procedure. Traditionally, synthesis methods had proven to be useful for computer graphics applications and have only been applied for generating samples with stochastic and regular texture patterns. Presently, the state of the art synthesis methods assume that the test sample is available and are feature independent. In the context of surface inspection systems, incoming samples are often classified to several defect classes after the feature extraction stage. Therefore, the goal in this research work is to perform the synthesis for a new feature vector such that the resulting synthesized image visualizes the decision boundary. This paper presents a methodology for structural synthesis based on principal components analysis. The methodology expects the samples of the training set as an input. It renders the synthesized form of the input samples of training set through eigenimages and its computed coefficients by solving a linear regression problem. The methodology has been evaluated on an industrial dataset to validate its performance.


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Paper Citation

in Harvard Style

Maddukuri S., Heidl W., Eitzinger C. and Pichler A. (2016). Structural Synthesis based on PCA: Methodology and Evaluation . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 348-355. DOI: 10.5220/0005721403480355

in Bibtex Style

author={Sriniwas Chowdhary Maddukuri and Wolfgang Heidl and Christian Eitzinger and Andreas Pichler},
title={Structural Synthesis based on PCA: Methodology and Evaluation},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},

in EndNote Style

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Structural Synthesis based on PCA: Methodology and Evaluation
SN - 978-989-758-175-5
AU - Maddukuri S.
AU - Heidl W.
AU - Eitzinger C.
AU - Pichler A.
PY - 2016
SP - 348
EP - 355
DO - 10.5220/0005721403480355