Abstract: Low contrast images can result from the wrong setting of image acquisition or poor illumination conditions. Such images may not be visually appealing and can be difficult for feature extraction. Contrast enhancement of color images can be useful in medical area for visual inspection. In this paper, a new technique is proposed to improve the contrast of color images. The RGB (red, green, blue) color image is transformed into normalized RGB color space. Adaptive histogram equalization technique is applied to each of the three channels of normalized RGB color space. The corresponding channels in the original image (low contrast) and that of contrast enhanced image with adaptive histogram equalization (AHE) are morphed together in proper proportions. The proposed technique is tested on seventy color images of acne patients. The results of the proposed technique are analyzed using cumulative variance and contrast improvement factor measures. The results are also compared with decorrelation stretch. Both subjective and quantitative analysis demonstrates that the proposed techniques outperform the other techniques.
Abstract: In this glasshouse study, we developed a new imagebased
non-destructive technique for detecting leaf P status of
different crops such as cotton, tomato and lettuce. The plants were
grown on a nutrient solution containing different P concentrations,
e.g. 0%, 50% and 100% of recommended P concentration (P0 = no P,
L; P1 = 2.5 mL 10 L-1 of P and P2 = 5 mL 10 L-1 of P). After 7 weeks
of treatment, the plants were harvested and data on leaf P contents
were collected using the standard destructive laboratory method and
at the same time leaf images were collected by a handheld crop image
sensor. We calculated leaf area, leaf perimeter and RGB (red, green
and blue) values of these images. These data were further used in
linear discriminant analysis (LDA) to estimate leaf P contents, which
successfully classified these plants on the basis of leaf P contents.
The data indicated that P deficiency in crop plants can be predicted
using leaf image and morphological data. Our proposed nondestructive
imaging method is precise in estimating P requirements of
different crop species.
Abstract: In this paper, we study on color transformation
method on website images for the color blind. The most common
category of color blindness is red-green color blindness which is
viewed as beige color. By transforming the colors of the images, the
color blind can improve their color visibility. They can have a better
view when browsing through the websites. To transform colors on
the website images, we study on two algorithms which are the
conversion techniques from RGB color space to HSV color space and
self-organizing color transformation. The comparative study focuses
on criteria based on the ease of use, quality, accuracy and efficiency.
The outcome of the study leads to enhancement of website images to
meet the color blinds- vision requirements in perceiving image
detailed.