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In this paper, we present an interactive object segmentation system form video, such as TV products and films, for converting 2D to 3D contents. It is focused on reducing the processing time for the object segmentation, increasing the usability. The proposed system is consist of three steps which are trimap generation based on polygon and object segmentation using Graph Cut algorithm and refinement by a user interfaces (UI) based on rectangle and local features. It makes it easy to get object segmentation rapidly. It is also helpful to create 3D contents. [ABSTRACT FROM AUTHOR]

Video inpainting is often used as a tool to assist user in removing objects or repairing damaged areas in a video. To deal with different kinds of video, several techniques such as object segmentation and temporal continuity maintenance are commonly adopted. In this paper, we extend the concept of exemplar-based inpainting to propose a new video inpainting algorithm which can realize object removal both in modern digital video and digitized aged films. Furthermore, a new patch searching strategy and a new patch adjustment mechanism are intorduced to maintain the temporal continuity of video and thus improve the results of video inpainting. Experiments demonstrate that the proposed algorithm can be effectively applied to different types of video. [ABSTRACT FROM AUTHOR]

This paper describes the method to know objects for autonomous robot navigation in an unknown outdoor environment. The method segments the objects from an image taken by moving robot on outdoor environment. In the beginning object segmentation, this uses multiple features to obtain the objects of segmented region. Multiple features are color, context information, line segments, edge, Hue Co-occurrence Matrix (HCM), Principal Components (PCs) and Vanishing Points (VPs). The model of the objects for outdoor environment defines their characteristics individually. We segment the region as mixture using the proposed features and methods. Next the stage classifies the object into natural and artificial ones. We detect sky and trees of natural object and detect building of artificial object using the combination of appearance and context information. Then we estimate the dimensions of building. Extensive experiments with the object segmentation and analysis on outdoor environment confirm the validity of the approach. [ABSTRACT FROM AUTHOR]