Object-oriented image analysis using the CNN universal machine: new analogic CNN algorithms for motion compensation, image synthesis, and consistency observation

Image-analysis algorithms are of great interest in the context of object-oriented coding schemes. With reference to the utilization of the cellular neural network (CNN) universal machine for object-oriented image analysis, this paper presents new analogic CNN algorithms for obtaining motion compensation, image synthesis, and consistency observation. Along with the already developed segmentation and object labeling technique, the proposed method represents a framework for implementing CNN-based real-time image analysis. Simulation results, carried out for different video sequences, confirm the validity of the approach developed herein.