Multiview Object and View Sequence Recognition using Hidden Markov Models

This work presents a method for multiview object recognition and the estimation of the sequence of object views. The main feature of the method is to be lightweighted, which can be crucial for future sensor technology. This goal is achieved with a hidden Markov model of the 3D object from information of a sequence of 2D images. COIL 100 dataset were used for testing the method. The tests included images affected by rotation as well as Gaussian noise. Consistent results were obtained using relatively few shots of each class for the training process.