Browsing by Subject "Video object segmentation"

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Open Access
Semi-automatic video object segmentation
(2000) Esen, Ersin
Content-based iunetionalities form the core of the future multimedia applications. The new multimedia standard MPEG-4 provides a new form of interactivity with coded audio-visual data. The emerging standard MPEG-7 specifies a common description of various types of multimedia information to index the data for storage and retrieval. However, none of these standards specifies how to extract the content of the multimedia data. Video object segmentation addresses this task and tries to extract semantic objects from a scene. Two tyj)es of video object segmentation can be identified: unsupervised and supervised. In unsupervised méthods the user is not involved in any step of the process. In supervised methods the user is requested to supply additional information to increase the quality of the segmentation. The proposed weakly supervised still image segmentation asks the user to draw a scribble over what he defines as an object. These scribbles inititate the iterative method. .A.t each iteration the most similar regions are merged until the desired numljer of regions is reached. The proposed .segmentation method is inserted into the unsupervised COST211ter .A-ualysis Model (.A.M) for video object segmentation. The AM is modified to handh' the sujiervision. The new semi-automatic AM requires the user intei actimi for onl>· first frame of the video, then segmentation and object tracking is doin' automatically. The results indicate that the new semi-automatic AM constituK's a good tool for video oliject segmentation.
Open Access
Utilization of improved recursive-shortest-spanning-tree method for video object segmentation
(1997) Tuncel, Ertem
Emerging standards MPEG-4 and MPEG-7 do not standardize the video object segmentation tools, although their performance depends on them. There are a lot of still image segmentation algorithms in the literature, like clustering, split-and-merge, region merging, etc. One of these methods, namely the recursive shortest spanning tree (RSST) method, is improved so that a still image is approximated as a piecewise planar function, and well-approximated areas on the image are extracted cis regions. A novel video object segmentation algorithm, which takes the previously estimated 2-D dense motion vector field as input, and uses this improved RSST method to approximate each component of the motion vector field as a piecewise planar function, is proposed. The algorithm is successful in locating 3-D planar objects in the scene correctly, with acceptable accuracy at the boundaries. Unlike the existing algorithms in the literature, the proposed algorithm is fast, parameter-free and requires no initial guess about the segmentation result. Moreover, it is a hierarchical scheme which gives finest to coarsest segmentation results. The proposed algorithm is inserted into the current version of the emerging “Analysis Model (AM)” of the Europan COST21U'’’ project, and it is observed that the current AM is outperformed.
Open Access
Video object segmentation for interactive multimedia
(1998) Ekmekçi, Tolga
Recently, trends in video processing research have shifted from video compression to video analysis, due to the emerging standards MPEG-4 and MPEG-7. These standards will enable the users to interact with the objects in the audiovisual scene generated at the user’s end. However, neither of them prescribes how to obtain the objects. Many methods have been proposed for segmentation of video objects. One of the approaches is the “Analysis Model” (AM) of European COST-211 project. It is a modular approach to video object segmentation problem. Although AM performs acceptably in some cases, the results in many other cases are not good enough to be considered as semantic objects. In this thesis, a new tool is integrated and some modules are replaced by improved versions. One of the tools uses a block-based motion estimation technique to analyze the motion content within a scene, computes a motion activity parameter, and skips frames accordingly. Also introduced is a powerful motion estimation method which uses maximum a posteriori probability (MAP) criterion and Gibbs energies to obtain more reliable motion vectors and to calculate temporally unpredictable areas. To handle more complex motion in the scene, the 2-D affine motion model is added to the motion segmentation module, which employs only the translational model. The observed results indicate that the AM performance is improved substantially. The objects in the scene and their boundaries are detected more accurately, compared to the previous results.