Name
Abstract | ||
|---|---|---|
Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the quality of agricultural products is often associated with their color. Most existing automatic color grading systems determine color quality either by directly comparing product color against a predefined and fixed set of reference colors or by using a set of color separating parameters, often in three-dimensional color spaces. Using these methods, it is not convenient for the user to adjust color preferences or grading parameters. In this paper, we present an effective and user-friendly color mapping concept for automated color grading that is well suited for commercial production. User friendliness is often viewed by the industry as a very important factor to the acceptance and success of automation equipment. This color mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to the application. In contrast with more complex color grading techniques, the proposed method makes it easy for a human operator to specify and adjust color-preference settings Tomato and date maturity evaluation and date surface defect detection are used to demonstrate the performance of this novel color mapping concept. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TASE.2010.2087325 | IEEE T. Automation Science and Engineering |
Keywords | Field | DocType |
Image color analysis,Color,Indexes,Machine vision,Gray-scale,Automation,Calibration | Computer vision,Color space,Color mapping,Computer science,RGB color space,Color preferences,Color balance,Artificial intelligence,Color normalization,Color quantization,ICC profile | Journal |
Volume | Issue | ISSN |
8 | 2 | 1545-5955 |
Citations | PageRank | References |
9 | 0.76 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dah-Jye Lee | 1 | 422 | 42.05 |
| James K. Archibald | 2 | 632 | 161.01 |
| Guangming Xiong | 3 | 78 | 12.80 |