目標物影像之顯示，以及移動物體其特質之雜亂變動皆有無複性存在，但其邊綠之成長以及其特徵之新亂步行作用(Random walk)等，亦有其各自之邏輯性及系統性，一般言之，過往在計算機科學應用以及人工智慧設計上常將之目標影像以其整體之形態（如邊綠撿定，特微找出，平面形態等等），加以分析處理之。如若使用大型計算機處理常沒有困難發生，但以小型機器言之，其負荷能量實不夠使川。本研究之首要目標是在可行條件下發展以簡易表達方法做各種之辨認作用。
由於高水準智慧型科技機器之需要，以及其進步創新之必然性將來各行各業之運用及處理控制皆應在即時情形下按秩序而處理之，其具有高水準資訊資料亦皆要在卽時下做呈低水準數據(Low-level Coding)，使之做運算、控制、操作或檔案工作等。
柯氏複性規劃法(Kolmogorov Complexity programming)使之機率複性變之爲定值長度數據，並且使用具有決策法則之杜林機加以運轉，當可使之作用時間迅速，更能增大其正確率，使用串結法(String Array method)，可直接表達出Kolmogorov Complexity Program之應用。
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 plannar 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 plannar 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.
Index Items: Markovian chain, Random walk process, Kolmogorov complexity, Chapman-Kolmogorov process, String array approach, Poisson process, Optical flow technique, 3-D object analysis, Nonlinear motion, Translation and rotation.
