3D object recognition using multi-moment and neural network

To improve the performance of appearance-based three-dimensional object recognition system, we propose to extract Hupsilas moment invariants, affine moment invariants and color moments from the 2D images of 3D objects. Hupsilas and affine moment invariants have the properties of rotation, scale, translation invariance and affine transformation invariance respectively for the objects in images, and color moments are used to distinguish objects of similar shape and different color. Then these moments compose a 1-dimensional feature vector of 11 components for each 2D image of 3D objects and presented to the BP neural network for training. The trained network can be used to recognize 3D objects when provided the feature vectors of unseen views. We assessed our method based on both the original and noise corrupted COIL-100 3D objects dataset and achieved 100% correct rate of recognition when training images were presented every 10 degrees.