No abstract

A moving object recognition approach is presented in this paper. The motion of an object includes the linear or nonlinear translation and rotation. For a 3 -D object, the images taken by a camera are in planar form. They are varied by different distances between camera and the object, variant angles and timing for taking pictures. However, the change rate among these images taken at different instant are logically related. The brightness level between any two neighbour string cells of machine digital scanning raster varies according to the Markovian random walk process. Thus, the direction and position of a moving object can be found by the variations of the cell random walk.The angles between a machine digital scanning raster and the edges of an object in a planar image are called pseudo -refractional angles. The variations of angles can be used as features for object recognition. Together with the Kolmogorov complexity program, the probability function of the process can be changed into a finite length of string arrays to simplify the recognition procedure. The distance between camera and the object can be measured by a radar or supersonic signal for military or industrial applications.

No abstract

A moving object recognition approach is presented in this paper. The motion of an object includes the linear or nonlinear translation and rotation. For a 3 -D object, the images taken by a camera are in planar form. They are varied by different distances between camera and the object, variant angles and timing for taking pictures. However, the change rate among these images taken at different instant are logically related. The brightness level between any two neighbour string cells of machine digital scanning raster varies according to the Markovian random walk process. Thus, the direction and position of a moving object can be found by the variations of the cell random walk.The angles between a machine digital scanning raster and the edges of an object in a planar image are called pseudo -refractional angles. The variations of angles can be used as features for object recognition. Together with the Kolmogorov complexity program, the probability function of the process can be changed into a finite length of string arrays to simplify the recognition procedure. The distance between camera and the object can be measured by a radar or supersonic signal for military or industrial applications.

In this paper An Acoustic Imaging Recognition System (AIRS) will be introduced which is installed on an Intelligent Robotic System and can recognize different type of Hand tools' by Dynamic pattern recognition. The dynamic pattern recognition is approached by look up table method in this case, the method can save a lot of calculation time and it is practicable.