This master’s thesis deals with the fundamentals of machine vision application and its practical implementation. The research part is focused on the basic possibilities of image acquisition and image processing in different dimensions. The practical part describes the design of the dimension control algorithm using a 3D camera. This algorithm is then implemented in a single-purpose machine for optical dimension control.

Master’s thesis deals with object pose estimation using monocular camera. As an object is considered every rigid, shape fixed entity with strong edges, ideally textureless. Object position in this work is represented by transformation matrix, which describes object translation and rotation towards world coordinate system. First chapter is dedicated to explanation of theory of geometric transformations and intrinsic and extrinsic parameters of camera. This chapter also describes detection algorithm Chamfer Matching, which is used in this work. Second chapter describes all development tools used in this work. Third, fourth and fifth chapter are dedicated to practical realization of this works goal and achieved results. Last chapter describes created application, that realizes known object pose estimation in scene.

Dot peen markings are used by Scania Ferruform to maintain a traceability of their products throughout the manufacturing. Quality inspection of the markings are performed to ensure that they are added correctly and readable. This is, however, done manually by workers, which they are looking to change. Machine vision, in combination with machine learning, could prove helpful in automating this process, which is where this thesis comes in. Images of two types of dot peen markings were gathered using different experimental setups and equipment. Amazon Rekognition and MVTec Halcon were both used to predict the characters of the images, in order to determine if the two systems could be used to demonstrate that the quality inspection can be automated. To improve the result, the images were also processed with varied techniques. The pretrained version of Amazon Rekognition and MVTec Halcon, with unprocessed image, performed the best. They both predicted all the characters correctly, and showed a high confidence in their predictions, with an average confidence of 96.41% and 99.87% respectively. When processing the images before predicting the confidence of the systems decreased and predictions were also made incorrectly. Custom training a model also showed a poor result, with the best combination of average precision and overall recall being at 0.733 and 0.561 respectively.