Detecting simple motion using cellular neural networks

The general framework of motion detection based on the discrete-time samples of the moving image is defined. Four types of motion detection problem are studied. The simplest one is a model resembling the experiment of D.H. Hubel and T.N. Wiesel (1962) with a cat´s retina for detecting the motion of an object having a given speed in a given direction. The most complicated case is the determination of the vertical and horizontal velocity components of a moving image. Various cloning template sequences are proposed for detecting different types of motion. The consecutive black and white image samples are fed to the input and to the initial state nodes of the cellular neural network, respectively. After the transients have decayed, the output gives the information necessary for detecting the presence or absence of a specific motion as well as for estimating the direction and the magnitude of the velocity vector. Conditions are analysed under which the detection is correct. The circuit realization of some motion detectors are discussed and the use of a programmable dual CNN structure is proposed