In classical machine vision, the relevant features are defined by the machine vision engineer. Based on these rules, the software detects edges, shapes, colors, or textures. The approach is transparent, deterministic, and precise—especially for repeatable processes under stable conditions.
Deep learning is based on artificial neural networks that learn features from sample data automatically. The system analyzes many training images and identifies which structures, textures, or shapes are relevant for a given class. This makes deep learning well suited for scenarios with high variability, non-specific defects, or irregular patterns.
COMPARISON
Classical machine vision remains essential when maximum precision, traceability, and performance are required. Deep learning complements these methods as soon as objects become variable, complex, or not clearly definable. MVTec offers both approaches integrated in one software environment – for efficient, future-proof machine vision solutions.
