Research on large color difference evaluation using printed samples
A grey-scale psychophysical experiment was carried out for evaluating color differences using printed color patches. In total, 100 pairs of printed samples were prepared with an average ΔE*ab of 13.8 units. Each pair was assessed 60 times by a panel of 20 normal color vision observers. The visual results were used to test the existing color-difference formulae. The results showed that all formulae gave quite satisfactory performance except OSA-Ep-Gu, which is the formula for calculating the magnitude of small-medium color difference. The optimized kL values were used to compare with the medium color difference (MCD) of the earlier studies. The results showed that they agreed well with each other.
Colour-Difference Evaluation Using CRT Colours. Part II: Parametric Effects
A large set of colour discrimination data based upon cathode ray tube (CRT) colours was accumulated and described in Part I of this article. The experiment was divided into 16 different phases according to different viewing parameters studied: sample size (large and small), background (mid-gray, white, black, gray, red, yellow, green, and blue), frame (black and no frame), and width (no gap, 1-pixel, 2-pixel, and large gap) and colour (mid-gray and black) of separations between pairs of samples. This part of the article investigates the changes of perceived colour differences caused by these variables. In addition, investigations were made to find the results from which phase agree best with results for surface colours. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 403–412, 2001
Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation
In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R2 up to 0.99.