A Novel Video Object Spatial Segmenting Strategy Based on Morphological Filtering
Video object extraction has been a most discussed topic in the field of modern information processing. In order to improve the performance of the common temporal-spatial segmenting algorithms and to obtain video objects with ideal boundaries, a novel video object spatial watershed segmenting strategy is proposed in this paper. This strategy combines the merits of alternating sequential filtering by reconstruction and adaptive threshold. The reduction of the number of the segmented regions without complicated area merging is guaranteed by the iteration of morphological filtering of opening and closing by reconstruction with gradually expanding structural elements. Besides, the iteration makes it easier to distinguish the gradient of the object boundary pixels and that of the pixels inside the foreground and background plateaus. This process facilitates the application of the adaptive threshold algorithms to remove the small regions of local gradient extreme. Several comparative temporal-spatial segmenting experiments have been implemented on some standard testing sequences. The experimental results indicate that the proposed strategy has achieved satisfactory spatial segmentation and enhanced the performance of the temporal-spatial segmenting algorithm for the subject and the object. Additionally, appropriate structural element size of the morphological filter and the non-linear threshold values can be automatically generated. This ensures fairly good versatility and usability of the algorithm.