| Operators |
train_texture_inspection_model — Train a texture inspection model.
train_texture_inspection_model( : : TextureInspectionModel : )
train_texture_inspection_model trains a texture inspection model with all training images which were added by add_texture_inspection_model_image.
The complete texture inspection process works with image pyramids. The depth of the pyramid is determined by the parameter 'num_levels' (or ValRef{'levels'} if the levels have been set explicitly). In the first step of the training, texture features are extracted and added to the set of training samples for each pyramid level. For each pyramid level a Gaussian Mixture Model (GMM) classifier is determined with all training samples of the corresponding pyramid level. In a third step, the training samples are used to determine a novelty threshold for each pyramid level. In the following, the three training steps are explained in detail:
Feature extraction extracts a feature for each pixel within the training images. The texture features are calculated according to the settings of the 'patch_*' parameters, which can be manipulated with set_texture_inspection_model_param. Each texture feature is added to the training data of the GMM.
During the training of the GMMs, the optimal 'gmm_*' parameters are calculated from the training data. The dimension of the single GMMs is determined by the size of the feature patches. Please refer to set_texture_inspection_model_param for details. For large patch sizes, it can be extremely difficult to obtain a good approximation of the optimal GMM parameters. Also, the run time and memory usage rises significantly. Therefore we recommend to alter the default patch size with care.
More information on GMM classifiers can be found in the description of the operator create_class_gmm.
The calculation of the novelty thresholds is required to be able to distinguish between defective and non-defective texture. First, the novelty score of each training sample is determined with the GMMs determined in step 2. Then, based on the resulting novelty scores, novelty thresholds are determined for each pyramid level. The automatic determination of the novelty thresholds can be influenced with the parameter 'sensitivity' that can be set with set_texture_inspection_model_param.
After the training was successful it is possible to classify images with apply_texture_inspection_model. Each pixel is assigned a 'novelty score' that is compared to the novelty threshold, which was determined within the third step of the training. For optimal results it is possible to adapt the novelty threshold of the single pyramid levels with set_texture_inspection_model_param.
In general, the manipulation of all parameters, with the exception of 'gen_result_handle' and 'sensitivity', requires a retraining of the texture inspection model. Depending on which part of the training is affected by the corresponding parameter, either the whole training or only single steps have to be recalculated. This can lead to strong variations in the time required for retraining a texture inspection model. A precise description which parameters influence which parts of the algorithm are illustrated in set_texture_inspection_model_param.
For an explanation of the concept of the texture inspection see the introduction of chapter Inspection / Texture Inspection.
This operator modifies the state of the following input parameter:
The value of this parameter may not be shared across multiple threads without external synchronization.Handle of the texture inspection model.
* Create texture inspection model
create_texture_inspection_model ('basic', TextureInspectionModel)
* Make this short example fast:
set_texture_inspection_model_param (TextureInspectionModel, \
'gmm_em_max_iter', 1)
* Read and add training images
read_image (TrainImage, 'carpet/carpet_01')
add_texture_inspection_model_image (TrainImage, TextureInspectionModel, \
Indices)
* Train the model
train_texture_inspection_model (TextureInspectionModel)
* Read and apply a test image
read_image (TestImage, 'carpet/carpet_02')
apply_texture_inspection_model (TestImage, DefectCandidates, \
TextureInspectionModel, \
TextureInspectionResultID)
* Clean up
clear_texture_inspection_model (TextureInspectionModel)
The operator train_texture_inspection_model returns the value 2 (H_MSG_TRUE) if the given parameters are valid and within acceptable range. Otherwise, an exception will be raised.
add_texture_inspection_model_image, set_texture_inspection_model_param
apply_texture_inspection_model, clear_texture_inspection_model, remove_texture_inspection_model_image, write_texture_inspection_model, serialize_texture_inspection_model
X. Xianghua, M. Mirmehdi: “TEXEMS: Texture Exemplars for Defect Detection
on Random Textured Surfaces”; IEEE Transactions on Pattern Analysis and
Machine Intelligence, Vol. 29, No. 8; August 2007.
T. Boettger, M. Ulrich: “Real-time Texture Detection on Textured
Surfaces with Compressed Sensing”; Pattern Recognition and Image
Analysis, Vol. 26, No. 1, pp. 88-94; January 2016.
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| Operators |