Object recognition for cell manufacturing system

The development of cell manufacturing process using object recognition has been interested in automated factory. But it is not trivial work to recognize object because features transformed from illumination and diversified field needs have caused challenge problem in object detection and recognition. The recognition reliability in real world environment can be increased by object, which preserves inherent feature and has invariance feature to scale, rotation or translation. In this paper, an illumination and rotation invariant object recognition is proposed. First, a binary image reserving clean object edges is achieved using DoG filter and local adaptive binarization. An object region from background is extracted with compensated edges that reserves geometry information of object. The object is recognized using neural network, which is trained with object classes that are categorized by object type and rotation angle. Standard shape model represented object class is used to estimate the pose of recognized object, which is handled by a robot. The simulation has been processed to evaluate feasibility of the proposed method that shows the accuracy of 99.86% and the matching speed of 0.03 seconds on ETRI database, which has 16,848 object images that has captured in various lighting environment.