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State-of-the-art object categorization algorithms are designed to be heavily robust against scene variations like illumination changes, occlusions, scale changes, orientation and location 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 constraints on the scene, which in turn can reduce the enormous search space for object instances. In this position paper we explore the possibility to fixate certain of these variations according to the application specific scene constraints and investigate the influence of these adaptations on three main aspects of object categorization algorithms: the amount of training data needed, the speed of the detection and the amount of false detections. Moreover, we propose steps to simplify the training process under such scene constraints.

How to Exploit Scene Constraints to Improve Object Categorization Algorithms for Industrial Applications?