This paper describes a new algorithm for finding the contours of a moving object in an image sequence. A distinctive feature of this algorithm is its complete bottom-up strategy from image data to a consistent contour description. In our algorithm, an input image sequence is immediately converted to a complete set of quasi logical spatio-temporal measures on each pixel, which provide constraints on varying brightness. Then, candidate regions in which to localize the contour are bounded based on consistent grouping among neighboring measures. This reduces drastically the ambiguity of contour location. Finally, Some mid-level constraints on spatial and temporal smoothness of moving boundaries are invoked, and they are combined with these low-level measures in the candidate regions. This is performed efficiently by the regularization over the restricted trajectory of the moving boundary in the candidate regions. Since any quantity is dimensionless, the results are not affected by varying conditions of camera and objects. We examine the efficiency of this algorithm through several experiments on real NTSC motion pictures with dynamic background and natulal textures.