Abstract:
The invention relates to a method (100) of and to a system (200) for determining a template mesh of a shape model on the basis of a plurality of instances of the shape model. The method of determining the template mesh of the shape model comprises an obtaining step (110) for obtaining the plurality of instances of the template mesh, a computing step (120) for computing a plurality of results on the basis of the plurality of instances of the shape model, a deciding step (130) for deriving a decision on the basis of the plurality of results, and a decimating step (140) for decimating the template mesh of the shape model on the basis of the decision, thereby determining the template mesh of the shape model. Thus, the template mesh of the shape model determined by the method of the invention better describes objects of interest at all locations.
Abstract:
The invention relates to a system (100) for segmentation of image data describing an object comprising a first and second object component, the system comprising: a selection unit (120) for selecting a first component model of a plurality of first component models for adapting to the image data in order to delineate the first object component and for selecting a second component model of a plurality of second component models for adapting to the image data in order to delineate the second object component, a connection unit (130) for connecting the first and second component model, an initialization unit (140) for initializing the first and second component model in the image data volume, and an adaptation unit (150) for adapting the first and second component model to the image data, thereby segmenting the image data. Thus, the system (100) of the invention offers a good coverage of object variability, allowing many object models constructed from component models, while each component model is optimized to describe the modeled object component. Consequently, selecting an optimal first and second component model and an optimal way of connecting them improves the flexibility, robustness, and accuracy of the image data segmentation.
Abstract:
The invention relates to a method (100) of adapting a geometric model to an image data comprising determining a first partial transformation for mapping a first part of the geometric model into the image data and a second partial transformation for mapping a second part of the geometric model into the image data. By determining the first partial transformation of the first part of the geometric model and the second partial transformation of the second part of the geometric model, the geometric model can assume more shapes and therefore can be more accurately adapted to an object comprised in the image data.
Abstract:
The invention describes a method for control of a device (D 1 , D 2 , D 3 , D 4 ), which method comprise aiming a pointing device (2) comprising a camera (3) at an object (B 1 , B 1 ', B 2 , B 3 , B 4 ) representing a property of the device (D 1 , D 2 , D 3 , D 4 ) to be controlled and manipulating a control input (4) on the pointing device (2) to specify a degree of change (5) for the property. An image (6) of a target area (A) aimed at by the pointing device (2) is generated by the camera (3). The target area image (6) is interpreted to identitify the chosen object (B 1 , B 1 ', B 2 , B 3 , B 4 ), and to deduce the property of the device (D 1 . D 2 , D 3 , D 4 ) to be controlled based on the chosen object (B 1 , B 1 ', B 2 , B 3 , B 4 ). A control signal (7) is then generated for the device (D 1 , D 2 , D 3 , D 4 ) to be controlled accord üng to the degree of change (5) for the deduced property. The invention also describes a pointing device (2), a device control interface (8, 8') and a system comprising such a pointing device (2) and device control interface (8, 8') suitable for applying this method.
Abstract:
The present invention relates to a method, a text segmentation system and a computer program product for clustering of text into text clusters representing a distinct semantic meaning. The text clustering method identifies text portions and assigns text portions to different clusters in such a way that each text cluster refers to one or several semantic topics. The clustering method incorporates an optimization procedure based on a re-clustering procedure evaluating a target function being indicative of the correlation between a text unit and a cluster. The text clustering method makes use of a text emission model and a cluster transition model and makes further use of various smoothing techniques.