WO2012053811A3 - Tensor-voting-based color-clustering system and method - Google Patents
Tensor-voting-based color-clustering system and method Download PDFInfo
- Publication number
- WO2012053811A3 WO2012053811A3 PCT/KR2011/007765 KR2011007765W WO2012053811A3 WO 2012053811 A3 WO2012053811 A3 WO 2012053811A3 KR 2011007765 W KR2011007765 W KR 2011007765W WO 2012053811 A3 WO2012053811 A3 WO 2012053811A3
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- color
- tensor
- voting
- clustering
- unit
- Prior art date
Links
- 238000000034 method Methods 0.000 title abstract 6
- 239000003086 colorant Substances 0.000 abstract 4
- 238000007796 conventional method Methods 0.000 abstract 2
- 238000003709 image segmentation Methods 0.000 abstract 2
- 238000005266 casting Methods 0.000 abstract 1
- 238000009877 rendering Methods 0.000 abstract 1
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/46—Colour picture communication systems
- H04N1/56—Processing of colour picture signals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/143—Segmentation; Edge detection involving probabilistic approaches, e.g. Markov random field [MRF] modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Image Analysis (AREA)
Abstract
The present invention relates to tensor-voting-based color-clustering, and more particularly, to a tensor-voting-based color-clustering system and method which involve automatically estimating the number of dominant colors in tensor voting during the performance of color-clustering of an input image, and perform clustering using the data density of a color, thereby achieving effective image segmentation. To achieve the above-described technical features, provided is a tensor-voting-based color-clustering system comprising: a color feature space generating unit for classifying colors in an input image to generate color clusters for each color; a tensor voting unit for casting a tensor vote for each color cluster generated by the color feature space generating unit; and a color space analyzing and segmenting unit for analyzing and segmenting each color cluster for which a tensor vote is cast by the tensor voting unit. In addition, to achieve said technical features, provided is a tensor-voting-based color-clustering method using the tensor-voting-based color-clustering system comprising the color feature space generating unit, the tensor voting unit and the color space analyzing and segmenting unit, wherein the method comprises: a color feature space generating step in which the color feature space generating unit classifies colors in an input image to generate color clusters for each color; a tensor voting step in which the tensor voting unit casts a tensor vote for each color cluster generated through the color feature space generating step; and a color space analyzing and segmenting step in which the color space analyzing and segmenting unit analyzes and segments each of the color clusters for which a tensor vote was cast. The system and method of the present invention having the above-described objectives and functions may improve the image segmentation of an object as compared to conventional techniques by performing color-clustering. Moreover, the system and method of the present invention enable the number of primary colors to be automatically estimated unlike in conventional techniques, thus rendering the result of each color-clustering uniform and stable.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020100101495A KR101151739B1 (en) | 2010-10-18 | 2010-10-18 | System for color clustering based on tensor voting and method therefor |
| KR10-2010-0101495 | 2010-10-18 |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| WO2012053811A2 WO2012053811A2 (en) | 2012-04-26 |
| WO2012053811A3 true WO2012053811A3 (en) | 2012-06-14 |
| WO2012053811A9 WO2012053811A9 (en) | 2012-08-02 |
Family
ID=45975721
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/KR2011/007765 WO2012053811A2 (en) | 2010-10-18 | 2011-10-18 | Tensor-voting-based color-clustering system and method |
Country Status (2)
| Country | Link |
|---|---|
| KR (1) | KR101151739B1 (en) |
| WO (1) | WO2012053811A2 (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020163742A1 (en) | 2019-02-08 | 2020-08-13 | Photon-X, Inc. | Integrated spatial phase imaging |
| WO2021030454A1 (en) * | 2019-08-12 | 2021-02-18 | Photon-X, Inc. | Data management system for spatial phase imaging |
| WO2021067665A2 (en) * | 2019-10-03 | 2021-04-08 | Photon-X, Inc. | Enhancing artificial intelligence routines using 3d data |
| CN111476831B (en) * | 2020-03-20 | 2023-07-18 | 清华大学 | PCB image color migration device and method based on cluster analysis |
| CN113591981B (en) * | 2021-07-30 | 2024-02-09 | 上海建工四建集团有限公司 | Existing terrazzo information investigation method and system based on artificial intelligence |
| CN113643399B (en) * | 2021-08-17 | 2023-06-16 | 河北工业大学 | Color image self-adaptive reconstruction method based on tensor chain rank |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001039498A2 (en) * | 1999-11-24 | 2001-05-31 | Koninklijke Philips Electronics N.V. | Method and apparatus for detecting moving objects in video conferencing and other applications |
-
2010
- 2010-10-18 KR KR1020100101495A patent/KR101151739B1/en not_active Expired - Fee Related
-
2011
- 2011-10-18 WO PCT/KR2011/007765 patent/WO2012053811A2/en active Application Filing
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2001039498A2 (en) * | 1999-11-24 | 2001-05-31 | Koninklijke Philips Electronics N.V. | Method and apparatus for detecting moving objects in video conferencing and other applications |
Non-Patent Citations (2)
| Title |
|---|
| LEE, GUEE SANG ET AL.: "Extraction of Text Alignment by Tensor Voting and its Application to Text Detection", KIISE, vol. 36, no. 11, November 2009 (2009-11-01), pages 912 - 919 * |
| TOAN NGUYEN ET AL.: "Determination of Initial Parameters for K-means Clustering by Tensor Voting", KIISE, vol. 36, no. 2(A), November 2009 (2009-11-01), pages 96 - 97 * |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2012053811A9 (en) | 2012-08-02 |
| WO2012053811A2 (en) | 2012-04-26 |
| KR20120040004A (en) | 2012-04-26 |
| KR101151739B1 (en) | 2012-06-15 |
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