In order to achieve an optimal appearance, it is necessary to account for these shifts and anticipate the deformation of the thread pattern within the CNC sewing program. Up to now, this is addressed by human experts in an iterative way. Using the current state of the CNC program, a specimen is created. Subsequently, the human expert inspects the specimen and modifies the program accordingly. The program modification is performed more or less intuitively and requires a lot of experience. This procedure is repeated as long as necessary and may take up to two days. Next to the time commitment, this trial and error approach comes along with a high material cost, since a sufficient amount of test samples is needed to optimize the CNC program.
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1) Rigid registration: A rigid registration consists of a rotation and translation. Scaling is determined and applied as part of the deformation registration later on. The transformation is the result of an optimization over all model features and their associated targets, such that a non-linear equation system needs to be solved. An optimal solution is found using the conjugate gradient method [7]. 2) Deformable registration: New positions for the model points are the result of an optimization procedure that minimizes the total energy depending on the new positions. A linear equation system with the coordinates of all model points needs to be optimized. The relative weighting between external and internal energy determines how near a model point is attracted to its target with the model steepness as the constraint. A large weight for the internal energy means a very steep model that is robust but fails to model each local detail of the thread deformation. A small weight means a very flexible but not very robust model. Hence, the improvement of the quality of the found targets with each iteration allows a dynamic adaptation of the weights, resulting in a model adapting better and better to local deformations recorded in the thread image.
Given arbitrary complex thread patterns, the system automatically detects the real world thread pattern and compares it with the pattern intended by the manufacturer. A deformation vector can be calculated for every individual stitch with an accuracy of up to 50µm. The system may serve as a stand-alone for quality assurance. However, it also lays the foundation for the automated correction of stitch positions in textiles distorted by the elasticity of the material. Based on the computed deformation vector field, an automated correction of the CNC program to create the desired pattern is possible. An obvious limitation of the proposed system is the requirement for the thread to appear visually different than the background tissue. However, this is not the case for a variety of products, for which the thread color is wanted to be identical to the background color. A typical example is a black car seat sewed using a black thread. We are currently working on exchanging the RGB-camera for a multi-spectral image acquisition in order to distinguish originally metamer, i.e. identical appearing, objects.
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A camera based inspection system was introduced to automate the quality inspection process within the area of CNC sewing. Created thread patterns are automatically detected and compared against the intended pattern model. A deformation vector is computed for every individual stitch position. For both the thread detection as well as the model-based registration and adaptation, dedicated image processing pipelines were proposed.
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