Create a variation model for image comparison.

create_variation_model creates a variation model that can be used for image comparison. The handle for the variation model is returned in ModelID.

Typically, the variation model is used to discriminate correctly manufactured objects (``good objects'') from incorrectly manufactured objects (``bad objects''). It is assumed that the discrimination can be done solely based on the gray values of the object.

The variation model consists of an ideal image of the object to which the images of the objects to be tested are compared later on with compare_variation_model and an image that represents the amount of gray value variation at every point of the object. The size of the images with which the object model is trained and with which the model is compared later on is passed in Width and Height, respectively. The image type of the images used for training and comparison is passed in Type.

The variation model is trained using multiple images of good objects. Therefore, it is essential that the training images show the objects in the same position and rotation. If this cannot be guarateed by external means, the pose of the object can, for example, be determined by using matching (see find_shape_model). The image can then be transformed to a reference pose with affine_trans_image.

The parameter Mode is used to determine how the image of the ideal object and the corresponding variation image are computed. For Mode='standard', the ideal image of the object is computed as the mean of all training images at the respective image positions. The corresponding variation image is computed as the standard deviation of the training images at the respective image positions. This mode has the advantage that the variation model can be trained iteratively, i.e., as soon as an image of a good object becomes available, it can be trained with train_variation_model. The disadvantage of this mode is that great care must be taken to ensure that only images of good objects are trained, because the mean and standard deviation are not robust against outliers, i.e., if an image of a bad object is trained inadvertently, the accuracy of the ideal object image and that of the variation image might be degraded.

If it cannot be avoided that the variation model is trained with some images of objects that can contain errors, Mode can be set to 'robust'. In this mode, the image of the ideal object is computed as the median of all training images at the respective image positions. The corresponding variation image is computed as a suitably scaled median absolute deviation of the training images and the median image at the respective image positions. This mode has the advantage that it is robust against outliers. It has the disadvantage that it cannot be trained iteratively, i.e., all training images must be accumulated using concat_obj and be trained with train_variation_model in a single call.

create_variation_model returns 2 (H_MSG_TRUE) if all parameters are correct.

create_variation_model is processed completely exclusively without parallelization.

train_variation_model

clear_variation_model, find_shape_model, affine_trans_image

Matching