Object identification method based on p-SIFT (Scale Invariant Feature Transform) characteristic

An object recognition and eigenvalue technology, applied in the field of computer vision, can solve problems such as decline, affect the recognition accuracy, and feature space incompleteness discrimination, and achieve the effect of improving computing efficiency.

Active Publication Date: 2013-02-13
TSINGHUA UNIV
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AI Technical Summary

Problems solved by technology

Although this algorithm can effectively reduce the amount of calculation, there are two problems. One is that the incompleteness of its feature space makes the

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  • Object identification method based on p-SIFT (Scale Invariant Feature Transform) characteristic
  • Object identification method based on p-SIFT (Scale Invariant Feature Transform) characteristic
  • Object identification method based on p-SIFT (Scale Invariant Feature Transform) characteristic

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Embodiment Construction

[0028] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0029] The present invention provides a kind of object recognition method based on p-SIFT feature, comprises template library training stage and test picture matching stage, wherein, template library training stage comprises the following steps:

[0030] S1: respectively calculate the SIFT feature points of each of the M training pictures in the template library to obtain M feature matrices, where M is a positive integer;

[0031] S2: Calculate the covariance matrix of each feature matrix, and obtain the p-SIFT feature descriptor of each...

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Abstract

The invention provides an object identification method based on a p-SIFT (Scale Invariant Feature Transform) characteristic. The object identification method comprises a template library training phase and a test picture matching phase. The template library training phase comprises the following steps of: respectively calculating an SIFT characteristic point of each training picture in a template library to obtain a characteristic matrix; and calculating a covariance matrix of the characteristic matrix to obtain a p-SIFT characteristic descriptor. The test picture matching phase comprises the following steps of: calculating an SIFT characteristic matrix of a test picture; and calculating the similarity of the test picture and the characteristic matrix of a template library training picture. According to the object identification method disclosed by the invention, the characteristic points described by the p-SIFT characteristic are the region relativity and the direction relativity, so that the characteristic points are changed into a relative direction and a relative direction from an absolute position and an absolute direction, and the identification accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a p-SIFT feature-based object recognition method suitable for real-time 2D or 3D object recognition application scenarios that require a balance between recognition accuracy and computational complexity. Background technique [0002] At present, rigid object recognition in 2D or 3D pictures is more and more widely used in various computer vision algorithms and many related application systems have been developed, such as defective product detection in industrial production, license plate recognition at traffic intersections and Internet image retrieval etc. When measuring the pros and cons of object recognition algorithms, recognition accuracy and computational complexity are two important indicators. Usually, the improvement of recognition accuracy is achieved by introducing more features, which will also increase computational complexity. , that is, recognition accurac...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 尹首一张杰男欧阳鹏刘雷波魏少军
Owner TSINGHUA UNIV
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