The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a method which allows robots to infer a human's hierarchical intent from partially observed RGBD videos by imagining how the human will behave in the future. This capability is critical for creating robots which can interact socially or collaboratively with humans. We represent intent as a novel hierarchical, compositional, and probabilistic And-Or graph structure which describes...
This paper presents a method for unsupervised scene categorization. Our method aims at two objectives: (1) automatic feature selection for different scene categories. We represent images in a heterogeneous feature space to account for the large variabilities of different scene categories. Then, we use the information projection strategy to pursue features which are both informative and discriminative,...
Many vision tasks are posed as either graph partitioning (coloring) or graph matching (correspondence) problems. The former include segmentation and grouping, and the latter include wide baseline stereo, large motion, object tracking and recognition. In this paper, we present an integrated solution for both graph matching and graph partition using an effective sampling algorithm in a Bayesian framework...
In this paper, we present a compositional boosting algorithm for detecting and recognizing 17 common image structures in low-middle level vision tasks. These structures, called "graphlets", are the most frequently occurring primitives, junctions and composite junctions in natural images, and are arranged in a 3-layer And-Or graph representation. In this hierarchic model, larger graphlets...
In this paper, we present a two-level generative model for representing the images and surface depth maps of drapery and clothes. The upper level consists of a number of folds which will generate the high contrast (ridge) areas with a dictionary of shading primitives (for 2D images) and fold primitives (for 3D depth maps). These primitives are represented in parametric forms and are learned in a supervised...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.