FLORES: A JAVA Based Image Database for Ornamentals

Heijden, Gerie van der; Polder, G.; Eck, Jan Wouter van

doi:10.1007/3-540-48762-x_79

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Digital image processing is a new and promptly developing field which finds more and more application in various information and technical systems such as: radar-tracking, communications, television, astronomy, etc. [1]. There are numerous methods of digital image processing techniques such as: histogram processing, local enhancement, smoothing and sharpening, color segmentation, a digital image filtration and edge detection.…”

Section: Introductionmentioning

confidence: 99%

Developing a new color model for image analysis and processing

2007

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The theoretical outcomes and experimental results of new color model implemented in algorithms and software of image processing are presented in the paper. This model, as it will be shown below, may be used in modern real time video processing applications such as radar tracking and communication systems. The developed model allows carrying out the image process with the least time delays (i.e. it speeding up image processing). The proposed model can be used to solve the problem of true color object identification Experimental results show that the time spent during RGI color model conversion may approximately four times less than the time spent during other similar models. .

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Section: Introductionmentioning

confidence: 99%

Developing a new color model for image analysis and processing

2007

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“…There is a considerable need for a system which allows image comparison of new variety applications with an up to date digital reference collection. Therefore a distributed image database system, FLORES™, has been developed in the Netherlands 5 . It runs from the webbrowser and uses Java applets to allow RGB-color image matching across the Internet.…”

Section: Introductionmentioning

confidence: 99%

<title>Comparison of multispectral images across the Internet</title>

Heijden¹,

Polder

Gevers

1999

Internet Imaging

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Comparison in the RGB domain is not suitable for precise color matching, due to the strong dependency of this domain on factors like spectral power distribution of the light source and object geometry. We have studied the use of multispectral or hyperspectral images for color matching, since it can be proven that hyperspectral images can be made independent of the light source (color constancy) and object geometry (normalized color constancy). HyperspeGtral images have the disadvantage that they are large compared to regular RGB-images, which makes it infeasible to use them for image matching across the Internet. For red roses, it is possible to reduce the large number of bands (>100) of the spectral images to only three bands, the same number as of an RGB-image, using Principal Component Analysis, while maintaining 99% of the original variation. The obtained PCA-images of the roses can be matched using for example histogram cross correlation. From the principal coordinates plot, obtained from the histogram similarity matrices of twenty images of red roses, the discriminating power seems to be better for normalized spectral images than for color constant spectral images and RGB-images, the latter being recorded under highly optimized standard conditions.

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Evaluation of the tepal colour of Begonia x tuberhybrida Voss. for DUS testing using image analysis

Lootens¹,

Waes²,

Carlier³

2006

Euphytica

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In the framework of variety testing, evaluation of the flower of Begonia x tuberhybrida Voss. is done by experts. The colours of the flowers are compared with the RHS colour chart. For each flower colour a code is given. This method has several disadvantages (data cannot be statistically analyzed with standard ANOVA techniques, bias by the expert is possible, difficult measurement depending on the light environment,...) that can be overcome with image analysis. In this study several parameters were tested to distinguish the different varieties. Varieties were split in different colour groups (white, red and yellow-orange) and parameters were analyzed. Parameters based on the green colour plain of the RGB colour space resulted in the largest discriminating power. Parameters based on the HSV colour space performed less well.

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FLORES: A JAVA Based Image Database for Ornamentals

Cited by 4 publications

References 4 publications

Developing a new color model for image analysis and processing

Developing a new color model for image analysis and processing

<title>Comparison of multispectral images across the Internet</title>

Evaluation of the tepal colour of Begonia x tuberhybrida Voss. for DUS testing using image analysis

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