It is likely that in many applications block-matching techniques for motion estimation will be further used. In this paper, a novel object-based approach for enhancement of motion fields generated by block matching is proposed. Herein, a block matching is first applied in parallel with a fast spatial image segmentation. Then, a rule-based object postprocessing strategy is used where each object is partitioned into sub-objects and each sub-object motion histogram first separately analyzed. The sub-object treatment is, in particular, useful when image segmentation errors occur. Then, using plausibility histogram tests, object motions are segregated into translational or non-translational motion. For non-translational motion, a single motion-vector per sub-object is first assigned. Then motion vectors of the sub-objects are examined according to plausibility criteria and adjusted in order to create smooth motion inside the whole object. As a result, blocking artifacts are reduced and a more accurate estimation is achieved. Another interesting result is that motion vectors are implicitly assigned to pixels of covered/exposed areas. In the paper, performance comparison of the new approach and block matching methods is given. Furthermore, a fast unsupervised image segmentation method of reduced complexity aimed at separating objects is proposed. This method is based on a binarization method and morphological edge detection. The binarization combines local and global texture-homogeneity tests based on special homogeneity masks which implicitly take possible edges into account for object separation. The paper contributes also a novel formulation of binary morphological erosion, dilation and binary edge detection. The presented segmentation uses few parameters which are automatically adjusted to the amount of noise in the image and to the local standard deviation.