METHOD FOR ANALYZING AND SEARCHING 3D MODELS

• US 20170147609A1
• Filed: 05/09/2016
• Published: 05/25/2017
• Est. Priority Date: 11/19/2015
• Status: Abandoned Application

• Current Assignee: National Chiao Tung University (Government of The Republic of China)
• Original Assignee: National Chiao Tung University (Government of The Republic of China)
• Inventors: LIN, I-Chen, LIN, Jun-Yang, SHE, Mei-Fang, TSAI, Wen-Hsiang
• Application Number:
  US15/149,182
• Publication Number:
  US 20170147609A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/5838 using colour
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06V 10/46 Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features colour feature extraction G06V10/56
• CPC Class Codes: G06V 10/467 Encoded features or binary features, e.g. local binary patterns [LBP]
• CPC Class Codes: G06V 10/478 Contour-based spectral representations or scale-space representations, e.g. by Fourier analysis, wavelet analysis or curvature scale-space [CSS]
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/752 Contour matching
• CPC Class Codes: G06V 10/761 Proximity, similarity or dissimilarity measures
• CPC Class Codes: G06V 20/647 by matching two-dimensional images to three-dimensional objects

Abstract

A method for analyzing and searching 3D models includes steps of obtaining data global features and data local features of data images by globally analyzing and locally analyzing data images of 3D models respectively; obtaining searching global features and searching local features by globally analyzing and locally analyzing searching images respectively; obtaining corresponding data global features and corresponding data local features based on the search global features and the searching local feature; and obtaining corresponding data images based on the corresponding data global features and the corresponding data local features.

Claims

1. A method for analyzing and searching images, comprising:

obtaining a plurality of data global features and a plurality of data local features of a plurality of data images by globally analyzing and locally analyzing the data images respectively;

obtaining a searching image;

obtaining a searching global feature and a searching local feature of the searching image by globally analyzing and locally analyzing the searching image respectively;

obtaining a corresponding data global feature from the data global features based on the searching global feature, and obtaining a corresponding data local feature from the data local features based on the searching local feature; and

obtaining a corresponding data image from the data images based on the corresponding data global feature and the corresponding data local feature.

2. The method of claim 1, wherein obtaining the data global features and the data local features of the data images by globally analyzing and locally analyzing the data images respectively comprises:

obtaining and analyzing a plurality of projected images of the data images in different viewpoints;

obtaining the data global features of the data images correspondingly based on the projected images of the data images;

obtaining and dividing the projected images of the data images into a plurality of local images; and

obtaining the data local features of the data images correspondingly based on the local images of the data images.

3. The method of claim 2, wherein obtaining and analyzing the projected images of the data images in different viewpoints comprises:

placing 3D models comprised by the data images at a center of a regular polyhedron; and

taking pictures of different projected images of the 3D models at a plurality of vertexes of the regular polyhedron.

4. The method of claim 3, wherein obtaining the data global features of the data images correspondingly based on the projected images of the data images comprises:

obtaining the data global features of the projected images of the data images correspondingly by extracting features from and analyzing the projected images of the data images based on Histogram of Depth Gradient (HODG) and 2D polar Fourier.

5. The method of claim 4, wherein obtaining and dividing the projected images of the data images into the local images comprises:

obtaining a main portion of each of the projected images of the data images by analyzing the projected images of the data images based on a Morphological operation; and

obtaining a branch portion of each of the projected images of the data images by removing the main portions from the projected images of the data images.

6. The method of claim 5, wherein obtaining the data local features of the data images correspondingly based on the local images of the data images comprises:

obtaining the data local features of the main portions and the branch portions of the data images correspondingly by extracting features from and analyzing the main portions and the branch portions of the projected images of the data images based on Zernike moment.

7. The method of claim 6, wherein obtaining the searching global feature and the searching local feature of the searching image by globally analyzing and locally analyzing the searching image respectively comprises:

analyzing a plurality of projected images of the searching image in different viewpoints;

obtaining the searching global features of the searching image correspondingly based on the projected images of the searching image;

obtaining and dividing the projected images of the searching image into a plurality of local images; and

obtaining the searching local features of the searching image correspondingly based on the local images of the searching image.

8. The method of claim 7, wherein analyzing the projected images of the searching image in different viewpoints comprises:

placing 3D models comprised by the searching image at a center of a regular polyhedron; and

taking pictures of different projected images of the 3D models at a plurality of vertexes of the regular polyhedron.

9. The method of claim 8, wherein obtaining the searching global features of the searching image correspondingly based on the projected images of the searching image comprises:

obtaining the searching global features of the projected images of the searching image correspondingly by extracting features from and analyzing the projected images of the searching image based on Histogram of Depth Gradient (HODG) and 2D polar Fourier.

10. The method of claim 9, wherein obtaining and dividing the projected images of the searching image into the local images comprises:

obtaining the main portion of the projected images of the searching image by analyzing the projected images of the searching image based on a Morphological operation; and

obtaining the branch portion of the projected images of the searching image by removing the main portions from the projected images of the searching image.

11. The method of claim 10, wherein obtaining the searching local features of the searching image correspondingly based on the local images of the searching image comprises:

obtaining the searching local features of the main portion and the branch portion of the searching image correspondingly by extracting features from and analyzing the main portion and the branch portion of the searching image based on Zernike moment.

12. The method of claim 11, wherein obtaining the corresponding data global feature from the data global features based on the searching global feature, and obtaining the corresponding data local feature from the data local features based on the searching local feature comprises:

obtaining the corresponding data global features whose difference with the searching global feature is the smallest by comparing the searching global features with the data global features; and

obtaining the corresponding data local features whose difference with the searching local features is the smallest by comparing the searching local features with the data local features.

13. The method of claim 12, wherein obtaining the corresponding data local features whose difference with the searching local features is the smallest data local features by comparing the searching local features with the data local features comprises:

obtaining the corresponding data local features by comparing the searching local features with the data local features based on earth mover'"'"'s distance (EMD).