HSV and NDVI Color Space Analysis and Sampling Procedure for Counting of Seedlings in Eucalyptus spp Plantations from High Definition Aerial Images

2017 ◽  
Author(s):  
Guilherme Pereira Jorge Franze ◽  
Emanuel Rocha Woiski ◽  
Luiz Carlos Sandoval Goes
Keyword(s):  
Color Space ◽  
Sampling Procedure ◽  
Aerial Images ◽  
High Definition ◽  
Space Analysis

scholarly journals Segmentation of Apples in Aerial Images under Sixteen Different Lighting Conditions Using Color and Texture for Optimal Irrigation

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1634 ◽  
Author(s):  
Sajad Sabzi ◽  
Yousef Abbaspour-Gilandeh ◽  
Ginés García-Mateos ◽  
Antonio Ruiz-Canales ◽  
José Miguel Molina-Martínez
Keyword(s):  
Precision Agriculture ◽  
Irrigation Management ◽  
Color Space ◽  
Texture Features ◽  
Apple Fruit ◽  
Segmentation Algorithm ◽  
Aerial Images ◽  
Aerial Photographs ◽  
Local Standard ◽  
Image Pixels

Due to the changes in the lighting intensity and conditions throughout the day, machine vision systems used in precision agriculture for irrigation management should be prepared for all possible conditions. For this purpose, a complete segmentation algorithm has been developed for a case study on apple fruit segmentation in outdoor conditions using aerial images. This algorithm has been trained and tested using videos with 16 different light intensities from apple orchards during the day. The proposed segmentation algorithm consists of five main steps: (1) transforming frames in RGB to CIE L*u*v* color space and applying thresholds on image pixels; (2) computing texture features of local standard deviation; (3) using intensity transformation to remove background pixels; (4) color segmentation applying different thresholds in RGB space; and (5) applying morphological operators to refine the results. During the training process of this algorithm, it was observed that frames in different light conditions had more than 58% color sharing. Results showed that the accuracy of the proposed segmentation algorithm is higher than 99.12%, outperforming other methods in the state of the art that were compared. The processed images are aerial photographs like those obtained from a camera installed in unmanned aerial vehicles (UAVs). This accurate result will enable more efficient support in the decision making for irrigation and harvesting strategies.

Metamerism of Tanzanite Under Altered Illuminators

2013 ◽  
Vol 591 ◽  
pp. 305-308
Author(s):  
Ma Ye ◽  
Ying Guo
Keyword(s):  
Color Space ◽  
Color Difference ◽  
Color Appearance ◽  
Visual Effect ◽  
Space Analysis ◽  
Commercial Grade ◽  
Angle Change ◽  
Violet Color ◽  
Hue Angle ◽  
Difference Form

The color parameters of 19 pieces purity transparent and color uniform commercial grade tanzanites were measured by colorimeter Color i5 and this article introduces the concept of Metamerism and calculates the color quantitatively, based on CIE1976 L*a*b* uniform color space, analysis the contribution of different illuminators to the bluish-violet color of tanzanite. The change of hue-angle h0 is in contrast to the actual visual effect, this is called abnormal hue-angle change of the gemstone tanzanite; both of the contributions of lightness difference and chroma difference play the same role to the change of the color difference, form the bluish-violet of tanzanite; it is shown that Metamerism indexes (Mt) of different samples are different and the same sample’s main wavelength follows the change while illuminators altered. Therefore, while illuminators altered, the color of tanzanite samples change from blue to bluish-violet, and the Mt larger, the change of color is greater, namely, the violet hue is more clear, with the better color appearance.

scholarly journals Radial greed algorithm with rectified chromaticity for anchorless region proposal applied in aerial surveillance

2019 ◽  
Vol 5 (3) ◽  
pp. 193
Author(s):  
Anton Louise Pernez De Ocampo ◽  
Elmer Dadios
Keyword(s):  
Color Space ◽  
Computational Cost ◽  
Sliding Window ◽  
Aerial Images ◽  
Trust Regions ◽  
Aerial Surveillance ◽  
Hit Rate ◽  
Window Method ◽  
Human Figure ◽  
Sliding Window Method

In aerial images, human figures are often rendered at low resolution and in relatively small sizes compared to other objects in the scene, or resemble likelihood to other non-human objects. The localization of trust regions for possible containment of the human figure becomes difficult and computationally exhaustive. The objective of this work is to develop an anchorless region proposal which can emphasize potential persons from other objects and the vegetative background in aerial images. Samples are taken from different angles, altitudes and environmental factors such as illumination. The original image is rendered in rectified color space to create a pseudo-segmented version where objects of close chromaticity are combined. The geometric features of segments formed are then calculated and subjected to Radial-Greed Algorithm where segments resembling human figures are selected as the proposed regions for classification. The proposed method achieved 96.76% less computational cost against brute sliding window method and hit rate of 95.96%. In addition, the proposed method achieved 98.32 % confidence level that it can hit target proposals at least 92% every time.

scholarly journals Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression

2018 ◽  
Vol 8 (8) ◽  
pp. 1269 ◽  
Author(s):  
Dae Seo ◽  
Yong Kim ◽  
Yang Eo ◽  
Wan Park
Keyword(s):  
Random Forest ◽  
Color Image ◽  
Color Space ◽  
Elevation Angle ◽  
Aerial Images ◽  
Aerial Image ◽  
Grayscale Image ◽  
Color Information ◽  
Image Colorization ◽  
Lab Color Space

Image colorization assigns colors to a grayscale image, which is an important yet difficult image-processing task encountered in various applications. In particular, grayscale aerial image colorization is a poorly posed problem that is affected by the sun elevation angle, seasons, sensor parameters, etc. Furthermore, since different colors may have the same intensity, it is difficult to solve this problem using traditional methods. This study proposes a novel method for the colorization of grayscale aerial images using random forest (RF) regression. The algorithm uses one grayscale image for input and one-color image for reference, both of which have similar seasonal features at the same location. The reference color image is then converted from the Red-Green-Blue (RGB) color space to the CIE L*a*b (Lab) color space in which the luminance is used to extract training pixels; this is done by performing change detection with the input grayscale image, and color information is used to establish color relationships. The proposed method directly establishes color relationships between features of the input grayscale image and color information of the reference color image based on the corresponding training pixels. The experimental results show that the proposed method outperforms several state-of-the-art algorithms in terms of both visual inspection and quantitative evaluation.

scholarly journals Color space analysis of mutual illumination

1993 ◽  
Vol 15 (12) ◽  
pp. 1319-1326 ◽  
Author(s):  
B.V. Funt ◽  
M.S. Drew
Keyword(s):  
Color Space ◽  
Space Analysis
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