Free Form Object Recognition Module using A-KAZE and GCS

This paper presents an object recognition module development. This module uses a local feature approach to identify keypoints in free form objects and an unsupervised artificial neural network (ANN) to associate the nearest ones and get clusters of each object learned. The module uses A-KAZE feature descriptor and Growing Cell Structure (GCS) ANN. The module is validated using an own data base, with twenty real objects and twenty different images each one. Here is presented a variety of experiments using from five to fifteen trainning images per object and the rest of them for evaluation. This method gets good results with 100% of discrimination between objects and up to 80% of correct classification.