Abstract: For generating a 3D geometric model (44) and/or a definition of the 3D geometric model from a single digital image of a building facade (4), a facade structure is detected from the digital image by dividing the facade (4) along horizontal lines into horizontal layers representative of floors (41), and by dividing the horizontal layers along vertical lines into tiles (42). The tiles (42) are further subdivided into a hierarchy of rectangular image regions (43). 3D architectural objects (45) corresponding to the image regions (43) are determined in an architectural element library. The 3D geometric model (44) or the definition of the 3D geometric model is generated based on the facade structure, the hierarchy and the 3D architectural objects (45). The library-based generation of the 3D geometric model makes it possible to enhance simple textured building models constructed from aerial images and/or ground-based photographs.

Abstract: A learning unit generates a function table indicating the relationship between the class number and position information of an object and the probability of appearance of the object for each small area image pattern of a code book, calculates a sharing matrix indicating the commonality of a feature amount between the classes, makes a tree diagram in which the classes with a similar feature amount are clustered, and calculates the weight of each node in the tree diagram for each small area image pattern. The recognition processing unit compares image data captured by a camera with the code book, selects the closest small area image pattern, extracts the class related to the node with the smallest weight among the nodes with a weight equal to or greater than a threshold value, and votes the position information of the small area image pattern for the class, thereby recognizing the object.

Abstract: For generating a 3D geometric model (44) and/or a definition of the 3D geometric model from a single digital image of a building facade (4), a facade structure is detected from the digital image by dividing the facade (4) along horizontal lines into horizontal layers representative of floors (41), and by dividing the horizontal layers along vertical lines into tiles (42). The tiles (42) are further subdivided into a hierarchy of rectangular image regions (43). 3D architectural objects (45) corresponding to the image regions (43) are determined in an architectural element library. The 3D geometric model (44) or the definition of the 3D geometric model is generated based on the facade structure, the hierarchy and the 3D architectural objects (45). The library-based generation of the 3D geometric model makes it possible to enhance simple textured building models constructed from aerial images and/or ground-based photographs.

Abstract: A discriminator generation device 10 includes a feature quantity extraction unit 13 which, using at least two pattern groups having patterns of different sizes for a detection object, extracts a feature quantity of patterns configuring each pattern group, and a discriminator generation unit 14 which generates a discriminator for detecting a detection object of a size corresponding to each pattern group in an image based on the feature quantity of the patterns of each pattern group.

Abstract: For generating a 3D geometric model (44) and/or a definition of the 3D geometric model from a single digital image of a building facade (4), a facade structure is detected from the digital image by dividing the facade (4) along horizontal lines into horizontal layers representative of floors (41), and by dividing the horizontal layers along vertical lines into tiles (42). The tiles (42) are further subdivided into a hierarchy of rectangular image regions (43). 3D architectural objects (45) corresponding to the image regions (43) are determined in an architectural element library. The 3D geometric model (44) or the definition of the 3D geometric model is generated based on the facade structure, the hierarchy and the 3D architectural objects (45). The library-based generation of the 3D geometric model makes it possible to enhance simple textured building models constructed from aerial images and/or ground-based photographs.

Abstract: A multiple viewpoint endoscope system having a multiple viewpoint camera setup and/or an intelligent or cognitive image control system and display device particularly adapted for localizing internal structures within a cavity or an enclosing structure, such as an animal body, for instance the abdomen of an animal or human, or for localizing a real or synthetic image of such internal structures within an overview image or on an overview 3D model.

Abstract: A learning unit 4 generates a function table indicating the relationship between the class number and position information of an object and the probability of appearance of the object for each small area image pattern of a code book, calculates a sharing matrix indicating the commonality of a feature amount between the classes, makes a tree diagram in which the classes with a similar feature amount are clustered, and calculates the weight of each node in the tree diagram for each small area image pattern. The recognition processing unit 7 compares image data captured by a camera 2 with the code book, selects the closest small area image pattern among a plurality of small area image patterns, extracts the class related to the node with the smallest weight among the nodes with a weight equal to or greater than a threshold value for the selected small area image pattern, and votes the position information of the small area image pattern for the class, thereby recognizing the object.

Abstract: A method for operating on images is described for interest point detection and/or description working under different scales and with different rotations, e.g. for scale-invariant and rotation-invariant interest point detection and/or description.

Abstract: A method for operating on images is described for interest point detection and/or description working under different scales and with different rotations, e.g. for scale-invariant and rotation-invariant interest point detection and/or description.

Abstract: A motion estimating device first detects mobile objects Oi and Oi? in continuous image frames T and T?, and acquires image areas Ri and Ri? corresponding to the mobile objects Oi and Oi?. Then, the motion estimating device removes the image areas Ri and Ri? corresponding to the mobile objects Oi and Oi? in the image frames T and T?, extracts corresponding point pairs Pj of feature points between the image frames T and T? from the image areas having removed the image areas Ri and Ri?, and carries out the motion estimation of the autonomous mobile machine between the image frames T and T? on the basis of the positional relationship of the corresponding point pairs Pj of feature points.

Abstract: Methods and apparatus for operating on images are described, in particular methods and apparatus for interest point detection and/or description working under different scales and with different rotations, e.g. for scale-invariant and rotation-invariant interest point detection and/or description. The present invention can provide improved or alternative apparatus and methods for matching interest points either in the same image or in a different image. The present invention can provide alternative or improved software for implementing any of the methods of the invention. The present invention can provide alternative or improved data structures created by multiple filtering operations to generate a plurality of filtered images as well as data structures for storing the filtered images themselves, e.g. as stored in memory or transmitted through a network. The present invention can provide alternative or improved data structures including descriptors of interest points in images, e.g.

Abstract: For generating a 3D geometric model (44) and/or a definition of the 3D geometric model from a single digital image of a building facade (4), a facade structure is detected from the digital image by dividing the facade (4) along horizontal lines into horizontal layers representative of floors (41), and by dividing the horizontal layers along vertical lines into tiles (42). The tiles (42) are further subdivided into a hierarchy of rectangular image regions (43). 3D architectural objects (45) corresponding to the image regions (43) are determined in an architectural element library. The 3D geometric model (44) or the definition of the 3D geometric model is generated based on the facade structure, the hierarchy and the 3D architectural objects (45). The library-based generation of the 3D geometric model makes it possible to enhance simple textured building models constructed from aerial images and/or ground-based photographs.

Abstract: A motion estimating device first detects mobile objects Oi and Oi? in continuous image frames T and T?, and acquires image areas Ri and Ri? corresponding to the mobile objects Oi and Oi?. Then, the motion estimating device removes the image areas Ri and Ri? corresponding to the mobile objects Oi and Oi? in the image frames T and T?, extracts corresponding point pairs Pj of feature points between the image frames T and T? from the image areas having removed the image areas Ri and Ri?, and carries out the motion estimation of the autonomous mobile machine between the image frames T and T? on the basis of the positional relationship of the corresponding point pairs Pj of feature points.

Abstract: The present invention relates to a novel endoscope or an optical large view endoscopic system with improved depth perception. In particular, a multiple viewpoint endoscope system comprising a multiple viewpoint camera setup and/or an intelligent or cognitive image control system and display device particularly adapted for localising internal structures within a cavity or an enclosing structure, such as an animal body, for instance the abdomen of an animal or human, or for localising a real or synthetic image of such internal structures within an overview image or on an overview 3D model.

Abstract: Methods and apparatus for operating on images are described, in particular methods and apparatus for interest point detection and/or description working under different scales and with different rotations, e.g. for scale-invariant and rotation-invariant interest point detection and/or description. The present invention can provide improved or alternative apparatus and methods for matching interest points either in the same image or in a different image. The present invention can provide alternative or improved software for implementing any of the methods of the invention. The present invention can provide alternative or improved data structures created by multiple filtering operations to generate a plurality of filtered images as well as data structures for storing the filtered images themselves, e.g. as stored in memory or transmitted through a network. The present invention can provide alternative or improved data structures including descriptors of interest points in images, e.g.