Abstract: A method for minimizing image blur in an image projected onto a display surface by a projector, the image blur being caused by out-of-focus regions, the method comprising: estimating (10) a spatially varying point-spread-functions (PSF) profile for a test image projected by the projector; and pre-conditioning (11) the image using a predetermined pre-processing algorithm based on the estimated PSF profile; wherein the pre-conditioned image is projected (17) by the projector to minimise image blur.

Abstract: Media slides are often employed in conference sessions, meetings, lectures, and other interactive forums. The proliferation of laptops and handheld computers allows a speaker to present directly from the laptop by connecting to the projector at the conference site. Physically connecting and disconnecting each presenter's laptop to the projection apparatus, however, can be a clumsy and disruptive process, particularly since the presenters may be seated at various locations around the room. A wireless interface between a presentation server and a laptop in a multi-user multi-projector presentation system allows a media sequence from each media source to be displayed on a common display via the presentation server and the wireless interface. Presenters need not run or swap cables or other physical connections to switch media sources to the common display. The interface requires no software modification to the media source laptops and maintains independence between media sources and the server for security.

Abstract: An object model has a plurality of features and is described by a model state. An unregistered feature of the object model, and an available frame from a sequence of images are selected to minimize a cost function of a subsequent search for a match of the selected model feature to the image in the selected frame. Upon a match, the feature is registered in that frame. The model state is then updated for each available frame. The steps of selecting, searching and updating are repeated. A video storage module may contain only one frame corresponding to a single time instance, in which case the framework used is based on integrated sequential feature selection. Alternatively, the video store may contain the entire video sequence, in which case feature selection is performed across all video frames for maximum tracking efficiency.

Abstract: An object model, having a plurality of features and described by a model state, is registered in an image. Unregistered features of the object model are dynamically selected such that the cost function of each feature search is minimized. A search is performed for a match of the selected model feature to the image, or to features within the image, to register the feature, and the model state is updated accordingly. These steps are repeated until all features have been registered. The search is performed in a region of high probability of a match. The cost function for a feature is based on the feature's basin of attraction, and in particular can be based on the complexity of the search process at each basin of attraction. A search region is based on a projected state probability distribution. In particular, the cost function is based on the “matching ambiguity,” or the number of search operations required to find a true match with some specified minimum probability.

Abstract: A plurality of object models, where each object model comprises a plurality of features and is described by a model state, are registered in at least one image a subset of the object models is selected. Different object models have different sets of features, which may or may not overlap. A feature of each selected object model is registered in one of the images, and the model state for each selected object model is updated accordingly. The model states of some or all of the object models are then updated according to a set of constraints. These steps are repeated until one or more object models are registered. At the beginning of each registration cycle, a cost function of a subsequent search is determined for each unregistered feature of each object model. An unregistered feature of each object model is then selected such that the cost function is minimized.

Abstract: Media slides are often employed in conference sessions, meetings, lectures, and other interactive forums. The proliferation of laptops and handheld computers allows a speaker to present directly from the laptop by connecting to the projector at the conference site. Physically connecting and disconnecting each presenter's laptop to the projection apparatus, however, can be a clumsy and disruptive process, particularly since the presenters may be seated at various locations around the room. A wireless interface between a presentation server and a laptop in a multi-user multi-projector presentation system allows a media sequence from each media source to be displayed on a common display via the presentation server and the wireless interface. Presenters need not run or swap cables or other physical connections to switch media sources to the common display. The interface requires no software modification to the media source laptops and maintains independence between media sources and the server for security.

Abstract: The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, sequential data such as are collected from detection of moving objects in three dimensional space are placed into data frames. Also, a single frame of data may require analysis by a sequence of analysis operations. Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data. In particular this information obtained purely from data observations can also be expressed as a probability density function, known as the likelihood function.

Abstract: The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, sequential data such as are collected from detection of moving objects in three dimensional space are placed into data frames. Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data. In particular this information obtained purely from data observations can also be expressed as a probability density function, known as the likelihood function.

Abstract: The invention recognizes that a probability density function for fitting a model to a complex set of data often has multiple modes, each mode representing a reasonably probable state of the model when compared with the data. Particularly, an image may require a complex sequence of analyses in order for a pattern embedded in the image to be ascertained. Computation of the probability density function of the model state involves two main stages: (1) state prediction, in which the prior probability distribution is generated from information known prior to the availability of the data, and (2) state update, in which the posterior probability distribution is formed by updating the prior distribution with information obtained from observing the data. In particular this information obtained purely from data observations can also be expressed as a probability density function, known as the likelihood function.