Representative Color Reference Region Extraction and Color Classification of Vehicles Using the Delaunay Triangulation

2017 ◽  
Vol 42 (12) ◽  
pp. 2425-2432
Author(s):  
Ho-yean Ahn ◽  
Kwang-ju Kim ◽  
Kil-houm Park
Keyword(s):  
Delaunay Triangulation ◽  
Reference Region ◽  
Color Classification ◽  
Region Extraction

An Application and Research of Gray-Relation for Color Classification in Dyeing Textile

2013 ◽  
Vol 694-697 ◽  
pp. 2881-2885
Author(s):  
Hai Yan Wang ◽  
Jian Xin Zhang
Keyword(s):  
System Analysis ◽  
High Efficiency ◽  
Information Management System ◽  
Space Distribution ◽  
Color Classification ◽  
Uniform Sampling ◽  
Forecast Precision ◽  
Internal Instability ◽  
The Stability

Dyeing textile’s information management system is the basis of accurate classification of color， machine studying methods have became a popular area of research for application in color classification. Traditional classification methods have high efficiency and are very simple ， but they are dependent on the distribution of sample spaces. If the sample data properties are not independent， forecast precision will been affected badly and internal instability will appear. An application of Gray-Relation for dyeing textile color classification has been designed， which offsets the discount in mathematical statistics method for system analysis. It is applicable regardless of variant in sample size， while quantizing structure is in agreement with qualitative analysis. On the basis of theoretical analysis， Dyeing textile color classification was conducted in the conditions of random sampling、 uniform sampling and stratified sampling. The experimental results proofs that by using Gray-Relation， dyeing textile color classification does not need to be dependent on sample space distribution， and increases the stability of classification.

scholarly journals Comparison of color indexes for tomato ripening

2004 ◽  
Vol 22 (3) ◽  
pp. 534-537 ◽  
Author(s):  
Andrés F. López Camelo ◽  
Perla A. Gómez
Keyword(s):  
Color Index ◽  
Human Perception ◽  
Color Difference ◽  
Color Classification ◽  
Color Changes ◽  
Color Categories ◽  
Visual Color ◽  
Postharvest Life ◽  
Range Of Values

Color in tomato is the most important external characteristic to assess ripeness and postharvest life, and is a major factor in the consumer's purchase decision. Degree of ripening is usually estimated by color charts. Colorimeters, on the other hand, express colors in numerical terms along the L*, a* and b* axes (from white to black, green to red and blue to yellow, respectively) within the CIELAB color sphere which are usually mathematically combined to calculate the color indexes. Color indexes and their relationship to the visual color classification of tomato fruits vine ripened were compared. L*, a* and b* data (175 observations from eleven cultivars) from visually classified fruits at harvest in six ripening stages according to the USDA were used to calculate hue, chroma, color index, color difference with pure red, a*/b* and (a*/b*)². ANOVA analysis were performed and means compared by Duncan's MRT. Color changes throughout tomato ripening were the result of significant changes in the values of L*, a* and b*. Under the conditions of this study, hue, color index, color difference and a*/b* expressed essentially the same, and the color categories were significantly different in terms of human perception, with hue showing higher range of values. Chroma was not a good parameter to express tomato ripeness, but could be used as a good indicator of consumer acceptance when tomatoes are fully ripened. The (a*/b*)² relationship had the same limitations as chroma. For vine ripened fruits, hue, color index, color difference and a*/b* could be used as objective ripening indexes. It would be interesting to find out what the best index would be if ripening took place under inadequate conditions of temperature and ilumination.

Classification of Extended Delaunay Triangulation for Fingerprint Indexing

2014 ◽  
Author(s):  
Ilaiah Kavati ◽  
VamshiKrishna Chenna ◽  
Munaga V.N.K. Prasad ◽  
Chakravarthy Bhagvati
Keyword(s):  
Delaunay Triangulation ◽  
Fingerprint Indexing

Development of a Digital Analysis System to Evaluate Peanut Maturity

2014 ◽  
Vol 41 (1) ◽  
pp. 8-16 ◽  
Author(s):  
B. C. Colvin ◽  
D. L. Rowland ◽  
J. A. Ferrell ◽  
W. H. Faircloth
Keyword(s):  
Imaging System ◽  
Extension Agents ◽  
Current Profile ◽  
Color Classification ◽  
Color Class ◽  
Scanned Image ◽  
Overall Performance ◽  
Pixel Color ◽  
Analysis System

ABSTRACT The profile color class method developed by Williams and Drexler in 1981 for the prediction of peanut harvest has proven to be a relative description of peanut maturity and is currently used by growers. However, the method requires the subjective visual classification of pods based on the development of color in the mesocarp layer of the hull which naturally introduces variability and possible error in maturity prediction based solely on observer bias. A Digital Image Model (DIM) was developed to eliminate subjectivity in pod color classification. The DIM is a method in which a scanned image of pod mesocarp colors is analyzed using a color definition algorithm. The final output of the DIM is a ratio of pixel color classes. To develop the DIM, replicated plots were established in Florida in 2010 and 2011 and sequentially harvested starting at 120 days after planting (DAP) and then progressing at wk intervals through 155 DAP. At harvest, yield and grade were evaluated for each plot and pod samples were collected for color classification by a single observer using the current profile board method. These same pod samples were then imaged and analyzed with the DIM method. The percentage of black and brown pods (mature pods) classified by the profile board and the DIM method were evaluated to determine the overall performance of the DIM in comparison to the profile board. The DIM method was successful in predicting the percentage of black and brown pods similarly to the profile board in both years with R2 0.63 to 0.82 with images acquired from the saddle region of the pod. There was more variability in matching the DIM prediction to the profile board when imaging pods from random regions, with R2 0.19 to 0.82. The goal of this research was to develop an imaging system that could be accessed by growers, consultants, and extension agents for objective analysis and prediction of peanut maturity.

Web Harvesting

2017 ◽  
pp. 351-378 ◽  
Author(s):  
B. Umamageswari ◽  
R. Kalpana
Keyword(s):  
Web Mining ◽  
Data Extraction ◽  
Web Pages ◽  
Extraction Techniques ◽  
Web Data ◽  
Web Data Extraction ◽  
Region Extraction ◽  
Universal Applicability ◽  
Web Harvesting

Web mining is done on huge amounts of data extracted from WWW. Many researchers have developed several state-of-the-art approaches for web data extraction. So far in the literature, the focus is mainly on the techniques used for data region extraction. Applications which are fed with the extracted data, require fetching data spread across multiple web pages which should be crawled automatically. For this to happen, we need to extract not only data regions, but also the navigation links. Data extraction techniques are designed for specific HTML tags; which questions their universal applicability for carrying out information extraction from differently formatted web pages. This chapter focuses on various web data extraction techniques available for different kinds of data rich pages, classification of web data extraction techniques and comparison of those techniques across many useful dimensions.

Classification of Endoscopic Images Using Delaunay Triangulation-Based Edge Features

2010 ◽  
pp. 131-140 ◽  
Author(s):  
M. Häfner ◽  
A. Gangl ◽  
M. Liedlgruber ◽  
Andreas Uhl ◽  
A. Vécsei ◽  
...  
Keyword(s):  
Delaunay Triangulation ◽  
Endoscopic Images ◽  
Edge Features
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