Abstract: The present invention includes in a system and method for estimating and tracking an orientation of a user's face by combining head tracking and face detection techniques. The orientation of the face, or facial pose, can be expressed in terms of pitch, roll and yaw of the user's head. Facial pose information can be used, for example, to ascertain in which direction the user is looking. In general, the facial pose estimation method obtains a position of the head and a position of the face and compares the two to obtain the facial pose. In particular, a camera is used to obtain an image containing a user's head. Any movement of the user's head is tracked and the head position is determined. A face then is detected on the head and the face position is determined. The head and face positions then are compared.

Abstract: A location history is a collection of locations over time for an object. A stay is a single instance of an object spending some time in one place, and a destination is any place where one or more objects have experienced a stay. Location histories are parsed using stays and destinations. In a described implementation, each location of a location history is recorded as a spatial position and a corresponding time at which the spatial position is acquired. Stays are extracted from a location history by analyzing locations thereof with regard to a temporal threshold and a spatial threshold. Specifically, two or more locations are considered a stay if they exceed a minimum stay duration and are within a maximum roaming distance. Each stay includes a location, a starting time, and an ending time. Destinations are produced from the extracted stays using a clustering operation and a predetermined scaling factor.

Abstract: The present invention involves a new system and method for probabilistic exemplar-based tracking of patterns or objects. Tracking is accomplished by first extracting a set of exemplars from training data. The exemplars are then clustered using conventional statistical techniques. Such clustering techniques include k-medoids clustering which is based on a distance function for determining the distance or similarity between the exemplars. A dimensionality for each exemplar cluster is then estimated and used for generating a probabilistic likelihood function for each exemplar cluster. Any of a number of conventional tracking algorithms is then used in combination with the exemplars and the probabilistic likelihood functions for tracking patterns or objects in a sequence of images, or in a space, or frequency domain.

Abstract: The present invention involves a new system and method for probabilistic exemplar-based tracking of patterns or objects. Tracking is accomplished by first extracting a set of exemplars from training data. The exemplars are then clustered using conventional statistical techniques. Such clustering techniques include k-medoids clustering which is based on a distance function for determining the distance or similarity between the exemplars. A dimensionality for each exemplar cluster is then estimated and used for generating a probabilistic likelihood function for each exemplar cluster. Any of a number of conventional tracking algorithms is then used in combination with the exemplars and the probabilistic likelihood functions for tracking patterns or objects in a sequence of images, or in a space, or frequency domain.

Abstract: A location history is a collection of locations over time for an object. By applying a recurring time period to a location history, it can be converted into a stochastic model of the location history. For example, a location history can be reorganized based on intervals that subside a recurring cycle. In a described implementation, training a location history model involves traversing each interval of multiple cycles of a target location history. After each object location at each interval is entered into a training matrix, the intervals can be normalized to determine relative probabilities per location for each interval of a designated cycle. The training and resulting location history model can be Markovian or non-Markovian. Applications include probabilistic location estimation, fusion of location estimates, location-history simulation, optimal scheduling, transition analysis, clique analysis, and so forth.

Abstract: An image background maintenance system and method for maintaining a background model of an image sequence by processing on multiple spatial scales. These multiple spatial scales include a pixel scale, a regional scale and a frame scale. The image sequence undergoes pixel processing that determines a current background model and provides an initial pixel assignment as either a background or a foreground pixel. Region processing further refines the initial pixel assignments by considering relationships between pixels and possibly reassigning pixels. Frame processing further refines the current background model by determining whether a substantial change has occurred in the actual background and, if so, providing a more accurate background model.

Abstract: An image background maintenance system and method for maintaining a background model of an image sequence by processing on multiple spatial scales. These multiple spatial scales include a pixel scale, a regional scale and a frame scale. The image sequence undergoes pixel processing that determines a current background model and provides an initial pixel assignment as either a background or a foreground pixel. Region processing further refines the initial pixel assignments by considering relationships between pixels and possibly reassigning pixels. Frame processing further refines the current background model by determining whether a substantial change has occurred in the actual background and, if so, providing a more accurate background model.

Abstract: An image background maintenance system and method for maintaining a background model of an image sequence by processing on multiple spatial scales. These multiple spatial scales include a pixel scale, a regional scale and a frame scale. The image sequence undergoes pixel processing that determines a current background model and provides an initial pixel assignment as either a background or a foreground pixel. Region processing further refines the initial pixel assignments by considering relationships between pixels and possibly reassigning pixels. Frame processing further refines the current background model by determining whether a substantial change has occurred in the actual background and, if so, providing a more accurate background model.

Abstract: An example-based filling system identifies appropriate filling material to replace a destination region in an image and fills the destination region using this material, thereby alleviating or minimizing the amount of manual editing required to fill a destination region in image. Tiles of image data are borrowed from the proximity of the destination region or some other source to generate new image data to fill in the region. Destination regions may be designated by user input (e.g., selection of an image region by a user) or by other means (e.g., specification of a color or feature to be replaced). In addition, the order in which the destination region is filled by example tiles may be configured to emphasize the continuity of linear structures and composite textures using a type of isophote-driven image-sampling process.

Abstract: An automated or semi-automated authoring tool and method for incorporating maps and other geographical data in combination with text, photographs, and other multimedia data associated with a trip to produce a travelogue of the trip. The geographical travelogue authoring tool and method includes a number of geographically-oriented features that provide automation and are useful when blending the geographical data into the geographic travelogue. The geographic travelogue authoring method includes obtaining content items that are associated with a trip and geographically coding these content items. This serves to associate a geographic location from the trip with the content item. A map then is selected based on the trip, and the map and the geo-coded content items are automatically integrated with other multimedia data to generate the geographic travelogue.

Abstract: A system and process for controlling common information displays, referred to as shared displays, is presented. The system and process allows multiple modes of input using a set of modules that accept and display data from a variety of sources. Input modules are able to understand data from a single mode of communication and to be able to generate messages as output accordingly. An optional translation module takes discrete message units and converts them into commands or requests that can be processed by a logic module. The logic module includes any application that is running on the shared display. A layout module lays out the information output by the logic module and a display module takes the layout data and converts the information to a form that can be readily displayed on a display device.

Abstract: The present invention includes in a system and method for estimating and tracking an orientation of a user's face by combining head tracking and face detection techniques. The orientation of the face, or facial pose, can be expressed in terms of pitch, roll and yaw of the user's head. Facial pose information can be used, for example, to ascertain in which direction the user is looking. In general, the facial pose estimation method obtains a position of the head and a position of the face and compares the two to obtain the facial pose. In particular, a camera is used to obtain an image containing a user's head. Any movement of the user's head is tracked and the head position is determined. A face then is detected on the head and the face position is determined. The head and face positions then are compared.

Abstract: A system and process for automatically learning a reliable color-based tracking system is presented. The tracking system is learned by using information produced by an initial object model in combination with an initial tracking function to probabilistically determine the configuration of one or more target objects in a temporal sequence of images, and a data acquisition function for gathering observations relating to color in each image. The observations gathered by the data acquisition function include information that is relevant to parameters desired for a final color-based object model. A learning function then uses probabilistic methods to determine conditional probabilistic relationships between the observations and probabilistic target configuration information to learn a color-based object model automatically tailored to specific target objects.

Abstract: A system and method for providing a peer-to-peer photo-sharing environment. The system includes: manual and automatic photo annotation at the client; periodic client-server synchronization; an index of client photos on a central server or a photo database that is resident on the central server, which is updated by the client-server synchronization function; end-user search functionality to search the centralized index or photo database; and transmission of the relevant photos to the client via an on-line image cache. In one embodiment, one client's new photos are automatically displayed on another client's computer (e.g., via screen saver or another mechanism).

Abstract: A browsing system and method for browsing allows multiple users to access and view hypertext documents on a shared display. A browsing system includes a hypertext document converter configured to convert a component in a hypertext document to include alternate component activation tags. A hypertext display controller controls a display module to display the converted component in the hypertext document. The input processor receives and processes an input signal related to the alternate component activation tag from at least one of the plurality of input devices. The browsing system activates the converted component of the hypertext document upon receiving the input signal.

Abstract: The present invention is embodied in a system and process for automatically learning a reliable color-based tracking system. The tracking system is learned by using information produced by an initial object model in combination with an initial tracking function to probabilistically determine the configuration of one or more target objects in a temporal sequence of images, and a data acquisition function for gathering observations relating to color in each image. The observations gathered by the data acquisition function include information that is relevant to parameters desired for a final color-based object model. A learning function then uses probabilistic methods to determine conditional probabilistic relationships between the observations and probabilistic target configuration information to learn a color-based object model automatically tailored to specific target objects.

Abstract: The present invention includes in a system and method for estimating and tracking an orientation of a user's face by combining head tracking and face detection techniques. The orientation of the face, or facial pose, can be expressed in terms of pitch, roll and yaw of the user's head. Facial pose information can be used, for example, to ascertain in which direction the user is looking. In general, the facial pose estimation method obtains a position of the head and a position of the face and compares the two to obtain the facial pose. In particular, a camera is used to obtain an image containing a user's head. Any movement of the user's head is tracked and the head position is determined. A face then is detected on the head and the face position is determined. The head and face positions then are compared.

Abstract: The present invention includes in a system and method for estimating and tracking an orientation of a user's face by combining head tracking and face detection techniques. The orientation of the face, or facial pose, can be expressed in terms of pitch, roll and yaw of the user's head. Facial pose information can be used, for example, to ascertain in which direction the user is looking. In general, the facial pose estimation method obtains a position of the head and a position of the face and compares the two to obtain the facial pose. In particular, a camera is used to obtain an image containing a user's head. Any movement of the user's head is tracked and the head position is determined. A face then is detected on the head and the face position is determined. The head and face positions then are compared.

Abstract: The present invention includes in a system and method for estimating and tracking an orientation of a user's face by combining head tracking and face detection techniques. The orientation of the face, or facial pose, can be expressed in terms of pitch, roll and yaw of the user's head. Facial pose information can be used, for example, to ascertain in which direction the user is looking. In general, the facial pose estimation method obtains a position of the head and a position of the face and compares the two to obtain the facial pose. In particular, a camera is used to obtain an image containing a user's head. Any movement of the user's head is tracked and the head position is determined. A face then is detected on the head and the face position is determined. The head and face positions then are compared.

Abstract: An example-based filling system identifies appropriate filling material to replace a destination region in an image and fills the destination region using this material, thereby alleviating or minimizing the amount of manual editing required to fill a destination region in image. Tiles of image data are borrowed from the proximity of the destination region or some other source to generate new image data to fill in the region. Destination regions may be designated by user input (e.g., selection of an image region by a user) or by other means (e.g., specification of a color or feature to be replaced). In addition, the order in which the destination region is filled by example tiles may be configured to emphasize the continuity of linear structures and composite textures using a type of isophote-driven image-sampling process.