Patents
Literature
Patsnap Copilot is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Patsnap Copilot

2914 results about "Centroid" patented technology

In mathematics and physics, the centroid or geometric center of a plane figure is the arithmetic mean position of all the points in the figure. Informally, it is the point at which a cutout of the shape could be perfectly balanced on the tip of a pin.

Business directory search engine

A system and method for efficiently searching directory listing information to obtain more relevant results is provided. In a computer system running a computing application, it is advantageous to provide search capabilities, in the form of a search engine, to operators to assist them in their effort of retrieving desired data. The search engine may cooperate with a data store having directory listing information to provide listings data to an operator. In an illustrative implementation, this search engine may be deployed on an Internet Web site that offers business listing information. The search system may comprise a user interface to enter search query information, a data store that houses a variety of directory listing information according to a predefined data taxonomy, and a means for displaying the search results. In operation, the search engine offers a variety of search options, such as, search by business name, by business categories levels, by geographic position of the user or the business, or a combination thereof. Depending on the search query entered, the search engine will perform either a bounded search (i.e. a search bounded to a specific geographic area), a proximity search (i.e. a search proximate to a computed centroid), or a combination of the two to find the most relevant directory listings. Using the inputted search qualifiers, the search engine polls the data store according to a predefined set of rules and instructions for the relevant directory listing information. These rules are directly related to the taxonomy of the data store.
Owner:MICROSOFT TECH LICENSING LLC

Optimizations for live event, real-time, 3D object tracking

InactiveUS20090046152A1Maximized manufacturingMaximized installation costTelevision system detailsImage analysisView cameraIce hockey
A system 1000, and its alternates 1002 through 1022, for automatically tracking and videoing the movements of multiple participants and objects during an event. System 1000 and its alternates comprise a scalable area tracking matrix of overhead tracking cameras 120c, whose individual field-of-views 120v combine to form a contiguous view 504m of the performance area where the event is being held. As participants 110, and all other necessary objects such as 103 (e.g. a puck in ice-hockey) and 104 (e.g. a stick in ice-hockey) move about during the event, computer 160 analyzes the images from contiguous view 504m to create a real-time tracking database at least including participant and object centroid locations respective to the performance area, and preferably including their identities matched to these ongoing locations. System 1000 and its alternates then employ the real-time database to automatically direct, without operator intervention, one or more side-view cameras such as 140-a, 140-b, 140-c and 140-d, to maintain optimal viewing of the event. The participants and objects may additionally be marked with encoded or non-encoded, visible or non-visible markers, either denoting centroid locations visible from their upper surfaces, and/or non-centroid locations visible from perspective views. The encoded markers are preferably placed on upper surfaces and detectable by computer 160 as it analyzes the images from contiguous overhead field-of-view 504m, thus providing participant and object identities to correspond with ongoing locations, further enhancing algorithms for subsequently controlling side-view cameras 140-a through 140-d. The non-encoded markers are preferably placed on multiple non-centroid locations at least on the participants that are then adjustably viewable by system 1000 and its alternates as the system uses the determined locations of each participant from the overhead view to automatically adjust one or more side-view cameras to tightly follow the participant. The resulting images from side-view cameras 140-a through 140-d may then be subsequently processed to determine the non-encoded marker locations, thus forming a three dimensional model of each participant and objects movements.
Owner:MAXX HLDG

Methods and apparatus related to pruning for concatenative text-to-speech synthesis

The present invention provides, among other things, automatic identification of near-redundant units in a large TTS voice table, identifying which units are distinctive enough to keep and which units are sufficiently redundant to discard. According to an aspect of the invention, pruning is treated as a clustering problem in a suitable feature space. All instances of a given unit (e.g. word or characters expressed as Unicode strings) are mapped onto the feature space, and cluster units in that space using a suitable similarity measure. Since all units in a given cluster are, by construction, closely related from the point of view of the measure used, they are suitably redundant and can be replaced by a single instance. The disclosed method can detect near-redundancy in TTS units in a completely unsupervised manner, based on an original feature extraction and clustering strategy. Each unit can be processed in parallel, and the algorithm is totally scalable, with a pruning factor determinable by a user through the near-redundancy criterion. In an exemplary implementation, a matrix-style modal analysis via Singular Value Decomposition (SVD) is performed on the matrix of the observed instances for the given word unit, resulting in each row of the matrix associated with a feature vector, which can then be clustered using an appropriate closeness measure. Pruning results by mapping each instance to the centroid of its cluster.
Owner:APPLE INC

Perception-based image retrieval

A content-based image retrieval (CBIR) system has a front-end that includes a pipeline of one or more dynamically-constructed filters for measuring perceptual similarities between a query image and one or more candidate images retrieved from a back-end comprised of a knowledge base accessed by an inference engine. The images include at least one color set having a set of properties including a number of pixels each having at least one color, a culture color associated with the color set, a mean and variance of the color set, a moment invariant, and a centroid. The filters analyze and compare the set of properties of the query image to the set of properties of the candidate images. Various filters are used, including: a Color Mask filter that identifies identical culture colors in the images, a Color Histogram filter that identifies a distribution of colors in the images, a Color Average filter that performs a similarity comparison on the average of the color sets of the images, a Color Variance filter that performs a similarity comparison on the variances of the color sets of the images, a Spread filter that identifies a spatial concentration of a color in the images, an Elongation filter that identifies a shape of a color in the images, and a Spatial Relationship filter that identifies a spatial relationship between the color sets in the images.
Owner:RGT UNIV OF CALIFORNIA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products