The presentation considers computer vision and machine learning, especially from a point of view of applications. Digital image processing and analysis with machine learning methods could enable efficient solutions for various areas of useful data-centric engineering applications. Challenges with imbalance of datasets, domain adaptation, active learning, open set classification, and metric learning of similarities are considered, and related examples are given in automated plankton image recognition, re-identification of individual animals, log measurement systems for the sawmill industry, traffic sign condition analysis, and medical image analysis of diabetic retinopathy, based on the research of LUT Computer Vision and Pattern Recognition Laboratory (CVPRL).