Peking University

Mingmin Zhen

Wen Gao

Ge Li

Ronggang Wang

Zhenyu Wang

Ying Li

Wenmin Wang

Shengfu Dong

An image representation method and processing device based on local PCA whitening. A first mapping module maps words and characteristics to a high-dimension space. A principal component analysis module conducts principal component analysis in each corresponding word space, to obtain a projection matrix. A VLAD computation module computes a VLAD image representation vector; a second mapping module maps the VLAD image representation vector to the high-dimension space. A projection transformation module conducts projection transformation on the VLAD image representation vector obtained by means of projection. A normalization processing module conducts normalization on characteristics obtained by means of projection transformation, to obtain a final image representation vector. An obtained image representation vector is projected to a high-dimension space first, then projection transformation is conducted on a projection matrix computed in advance and vectors corresponding to words, to obtain a low-dimension vector; and in this way, the vectors corresponding to the words are consistent. The disclosed method and the processing device can obtain better robustness and higher performance.

Image representation method and processing device based on local PCA whitening

A method and a device for extracting local features of a 3D point cloud are disclosed. Angle information and the concavo-convex information about a feature point to be extracted and a point of an adjacent body element are calculated based on a local reference system corresponding to the points of each body element. The feature relation between the two points can be calculated accurately. The property of invariance in translation and rotation is possessed. Since concavo-convex information about a local point cloud is contained during extraction, the inaccurate extraction caused by ignoring concavo-convex ambiguity in previous 3D local feature description is resolved. During normalization processing, exponential normalization processing and second-normal-form normalization are adopted, which solves the problem of inaccurate similarity calculation caused by a circumstance that a few elements in a vector are too large or too small during feature extraction, thus improving accuracy of extracted three-dimensional local features.

Method and a device for extracting local features of a three-dimensional point cloud

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Shenzhen, China

Mingmin Zhen