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331 results about "GiST" patented technology

In computing, GiST or Generalized Search Tree, is a data structure and API that can be used to build a variety of disk-based search trees. GiST is a generalization of the B+ tree, providing a concurrent and recoverable height-balanced search tree infrastructure without making any assumptions about the type of data being stored, or the queries being serviced. GiST can be used to easily implement a range of well-known indexes, including B+ trees, R-trees, hB-trees, RD-trees, and many others; it also allows for easy development of specialized indexes for new data types. It cannot be used directly to implement non-height-balanced trees such as quad trees or prefix trees (tries), though like prefix trees it does support compression, including lossy compression. GiST can be used for any data type that can be naturally ordered into a hierarchy of supersets. Not only is it extensible in terms of data type support and tree layout, it allows the extension writer to support any query predicates that they choose.

Rotary cutting tool, such as a drill, comprising an exchangeable cutting insert, and an exchangeable cutting insert

A rotary cutting tool that is composed of a tool shank with at least one chucking groove or chip flute and one exchangeable cutting insert. At the tip of the shank a recess for accommodating the cutting insert is provided. The tool shank, on its circumference, has a limb with a bearing surface that is inclined relative to the tool axis and that corresponds to a likewise inclined bearing surface on a wing of the cutting insert. The inclination of the bearing surfaces produces an axial force that retains the cutting insert on the tool shank. The abstract of the disclosure is submitted herewith as required by 37 C.F.R. §1.72(b). As stated in 37 C.F.R. §1.72(b): A brief abstract of the technical disclosure in the specification must commence on a separate sheet, preferably following the claims, under the heading “Abstract of the Disclosure.” The purpose of the abstract is to enable the Patent and Trademark Office and the public generally to determine quickly from a cursory inspection the nature and gist of the technical disclosure. The abstract shall not be used for interpreting the scope of the claims. Therefore, any statements made relating to the abstract are not intended to limit the claims in any manner and should not be interpreted as limiting the claims in any manner.
Owner:KENNAMETAL INC

Image retrieval method based on minimum projection errors of multiple hash tables

An image retrieval method based on minimum projection errors of multiple hash tables belongs to the technical field of image retrieval, and is characterized in that the gist features of an image to be retrieved, a training image and a query image are respectively extracted; the principal component direction of training features is calculated and optimized through the iterative quantization method, and features to be retrieved and query features are projected on the optimized principal component direction to acquire the corresponding hash codes; the training features go through energy reduction to get new training features, and the process is repeated until the Num groups of hash codes are acquired; and the Hamming distance between the Num group of hash codes of the query image and the Num group of hash codes of the image to be retrieved is calculated, so that the similarity between the image to be retrieved and the query image can be measured according to the distance. The invention has the effects and benefits that the image retrieval method overcomes the shortcoming that the Hamming spherical radius of a single harsh table is large in case of a high recalling rate, as well as the problem that random projection hashing needs too many hash tables in case of a high recalling rate.
Owner:DALIAN UNIV OF TECH

Method for detecting complex sea-surface remote sensing image ships based on Gist characteristic study

The invention discloses a method for detecting complex sea-surface remote sensing image ships based on Gist characteristic study, comprising the following steps: step 1, collecting the remote sensing image data of complex sea-surfaces which have different time phases, different sensors and different sizes; step 2, implementing the block preprocess for the complex sea-surface remote sensing image to obtain a sample image section and a detection image section; step 3, drawing the distinguishing features and the Gist features of the sample image section and the detection image section; step 4, training the sample image section according to the distinguishing features and the Gist features obtained in the step 3 to obtain a training mode; step 5, adopting an SVM (Support Vector Machine) classifier to judge whether the detection image section has ships according to the training mode obtained in the step 4; and step 6, finding the single ship of the detection image section based on an improved itti visual attention mode. The method for detecting the complex sea-surface remote sensing image ships based on Gist characteristic study ensures that the ships do not fail to examine, reduces the false alarm rate, effectively treats the complex sea-surface remote sensing images under the disturbed conditions of sea clutters, cloud and mist, and has low computation complexity and strong pertinence.
Owner:WUHAN UNIV
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