New features, improved technologies and enhanced development – all good reasons for machine vision users to now upgrade to HALCON 13!
With version 13, MVTec HALCON offers significantly improved identification technologies. For the first time, the latest release offers optical character recognition (OCR) functions based on deep learning technology. A series of pre-trained fonts help to make the recognition process faster than with all previous classification methods. In addition, the automatic text recognition function is faster and now works with dot print fonts. HALCON 13 also reliably reads defective or occluded bar codes. Moreover, the QR code reader is even more robust when it comes to reading unclear or distorted codes.
An additional handy new feature for developers is the newly added option to debug HDevEngine applications directly within HDevelop. This debugging enables the developer to inspect call stack and variable values while executing procedures step by step, making error tracking a lot easier. You can even connect HDevelop to an HDevEngine application running on a different computer for remote debugging.
Furthermore, HALCON 13 offers an easy-to-use texture inspection, in which potential texture defects can be automatically identified by using an image-based comparison with flawless materials. This new feature allows the classification of a wide range of different textures with only a few parameters, making surface inspections of corresponding textures much easier.
HALCON 13 also provides a further improved surface-based 3D matching. By analyzing the edges in the model and the 3D point cloud, recognition especially of flat surfaces is now more robust, which allows the position of objects to be determined more precisely. This new method is particularly suitable for finding the exact position of boxes in the 3D space.
Moreover, MVTec HALCON now includes a new technology for the multi-view reconstruction of 3D objects with surface fusion. Using the image information from multiple cameras produces more robust results than provided by pairwise reconstruction methods. During this process, HALCON transfers available color information of the input images to the reconstructed objects, which improves visualization and further processing (e.g., with a 3D printer).