Canon Kabushiki Kaisha

Other

Barry James Drake

Alan Valev Tonisson

Scott Alexander Rudkin

Ernest Yiu Cheong Wan

Nagita Mehrseresht

Andrew Docherty

Andrew Peter Downing

Laurence Crew

Rob Sangster

David John Maunder

Eileen Oi-Yan Mak

Jeonren Vendrig

Mark W Peters

A method of generating hash codes from a query vector. An ordering of dimensions is determined for a lattice hole in an A* lattice, the lattice hole being located nearest to the query vector and the ordering of dimensions represents a mapping applied to transform a canonical hole of the A* lattice to the lattice hole. An initial hash code is determined for a first point in the A* lattice, based on the query vector, using a plurality of predetermined constants. A subsequent hash code is generated for a second point in the A* lattice, from the initial hash code by modifying the initial hash code by a multiple of a constant. The constant is selected from the plurality of predetermined constants using the determined ordering of dimensions.

Method, system and apparatus for generating hash codes

A method and associated imaging system for classifying at least one concept type in a video segment is disclosed. The method associates an object concept type in the video segment with a spatio-temporal segment of the video segment. The method then associates a plurality of action concept types with the spatio-temporal segment, where each action concept type of the plurality of action concept types is associated with a subset of the spatio-temporal segment associated with the object concept type. The method then classifies the action concept types and the object concept types associated with the video segment using a conditional Markov random field (CRF) model where the CRF model is structured with the plurality of action concept types being independent and indirectly linked via a global concept type assigned to the video segment, and the object concept type is linked to the global concept type.

Imaging system and method for classifying a concept type in video

A system and method for linking a hash code to a portion of an image. A plurality of lattice points is selected in a multidimensional lattice to form a smallest enclosing region about a feature vector representing the portion of the image and a lattice point is determined from the selected plurality of lattice points according to a distribution criteria. The determined lattice point is common to the smallest enclosing region and a region of the lattice adjacent to the smallest enclosing region located within a query radius distance of the feature vector. When the feature vector is located within the query radius of a query vector the feature vector is considered a match. The method assigns the feature vector to the determined lattice point and stores a link between a hash code associated with the determined lattice point and the portion of the image.

Image retrieval method

An image processing system for identifying content within image data. The image processing system comprises a processor that is arranged to: arrange image data in a Markov random field (MRF); and calculate state upper bound values of pairs of variables in the image data that are associated with an edge. The calculation of the state upper bound values is performed by the processor assigning the maximum state values of all of the states of a first variable of the pair of variables to the states of a second variable of the pair of variables, identifying the first and second variables from the pair of variables based on a number of states within each of the first and second variables, and determining a single state solution for identifying content in the image data based on the calculation of the state upper bound values.

Image processing system and method for identifying content within image data using markov random fields

A computer-implementable method of analyzing tissues of a retina. A polarization-sensitive (PS-OCT) image data set of the retina is received from a polarization-sensitive device, each element in the polarization-sensitive image data set being associated with an intensity value ({I, . . . , I}) and a polarization direction ({P, . . . , P}). A likelihood score is determined for each element of the polarization-sensitive image data set based on the intensity value of the element and a degree of divergence (&#x3bc;*P or D) of the polarization direction associated with the element from a reference polarization direction (&#x3bc;*) associated with the polarization sensitive image data set, wherein the likelihood score indicates whether said element is drawn from a directionally polarized distribution or a depolarizing distribution, Elements of the polarization-sensitive image data set are classified using the determined likelihood scores to analyze tissues of the retina.

Depolarizing region identification in the retina

A method of generating a feature vector for an image is disclosed. Values are determined from a plurality of points in a region of the image, each of the values being determined using at least two of the plurality of points. A periodic sequence of the determined values is determined based on an order of the plurality of points. The periodic sequence is phase variant to a starting point of the ordered plurality of points, the order of the plurality of points being determined according to a predetermined rule. The feature vector for one of the points is generated from a frequency domain representation of the periodic sequence, the feature vector being invariant to rotation with respect to the plurality of points.

Method, apparatus and system for generating a feature vector

A method of determining a hash code representing a portion of an image, is disclosed. A Delaunay region (e.g.,) enclosing an image feature point (e.g.,) representing at least the portion of the image is determined. The Delaunay region is determined from A* lattice points. A mapping transforming the Delaunay region to a predetermined canonical form is determined A point of the Delaunay region is received. The received point defines a plane containing the A* lattice points of the Delaunay region excluding the received point. A normal of the plane is determined by setting at least two co-ordinates of the normal to predetermined non-zero values, the two co-ordinates being selected according to the determined mapping. The hash code representing a portion of the image is determined according to a distance determined using the normal.

Method, system and apparatus for determining a hash code representing a portion of an image

Disclosed is a method of determining one or more event subsets within a plurality of images. Each image is associated with time and location data specifying the time and location of capture of the image. The method determines a time variable for each adjacent pair of images in a capture time ordered list of the plurality of images. A distance variable for each adjacent pair of images in the ordered list of images is then determined. The method determines speed data of the image capture device at the time and location of capture of each image. The ordered list of images is then partitioned into one or more event subsets on the basis of a cost function, the cost function being determined in accordance with a normalization of the time variable and distance variable, wherein the time variable and the distance variable are weighted relative to the speed data.

Image clustering method

An assistance method for selecting a program using a display device () is disclosed. The method () selects one or more attributes associated with a first program, the one or more attributes being selected dynamically by a processor associated with the display device () according to one or more predetermined criteria. The method searches for a second program associated with one or more of the selected attributes. The second program and the one or more attributes associated therewith are displayed on the display device, according to the search for the second program.

Program selecting apparatus and method of controlling program selecting apparatus

A method and apparatus for constructing spatial representations of referents is provided. The method and apparatus have application in any area where a spatial representation of a referent is required. Examples are image processing and machine vision. In image processing the 'referents' are signal sources which are being viewed by an image processing sensor, eg CCD array. The image sources will be points in a scene, for example. Currently available image systems must be aware of the relative positions of the image sensors. In prior art image systems, this information is usually provided a priori. In the present invention, no a priori information on the arrangement of the image sensors is required. Instead, a geometrical representation of the referents is produced by obtaining signals from the referents and utilizing the signals in order to provide the geometrical representation. The geometrical representation is built up purely from the signals provided by the sensors themselves. The invention assumes that behavioral differences between signals for any two referents imply a distance between the referents, and this assumption is utilized in order to build up the geometrical representation of the referents.

Growing community of inventors

Thornleigh, Australia

Barry James Drake