Exploiting scene constraints to improve object detection algorithms for industrial applications KU Leuven
State-of-the-art object detection algorithms are designed to be heavily robust against scene and object variations like illumination changes, occlusions, scale changes, orientation differences, background clutter and object intra-class variability. However, in industrial machine vision applications, where objects with variable appearance have to be detected, many of these variations are in fact constant and can be seen as scene specific ...