Automatic crop detection under field conditions using the HSV colour space and morphological operations

Hamuda, Esmael; McGinley, Brian; Glavin, Martin; Jones, Edward

doi:10.1016/j.compag.2016.11.021

Cited by 211 publications

(93 citation statements)

References 33 publications

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“…a, b, cx, cy, ec x , ec y , and θ, which are parameters used in Equation (2), are extracted from the clustered skin color distribution regions in the C b 'C r ' space through repeated testing. In order to remove unnecessary noises located in the extracted skin color regions, morphological operation [28][29][30][31], which is widely used in the image processing fields, is executed. Subsequently, labeling [32][33][34] is executed to extract the skin color regions in which skin color pixels are connected in the unit of region.…”

Section: Detection Of Skin Color Distribution Regionsmentioning

confidence: 99%

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects' regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

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Section: Detection Of Skin Color Distribution Regionsmentioning

confidence: 99%

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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“…The illuminant-invariant (ill-inv) maps created based on the illuminant-invariant color space (Finlayson, Hordley, & Drew, 2002) were found able to reduce the shadow effects within the original color image (Figure 8). The HSV color space was also found reliable to distinguish green plants from the background (Hamuda, Mc Ginley, Glavin, & Jones, 2017). Thus, both ill-inv maps and the HSV color space image were selected to be candidates for calculating the weights.…”

Section: Segmentation Using Depth and Colormentioning

confidence: 99%

Automated crop plant detection based on the fusion of color and depth images for robotic weed control

Gai

Tang

Steward

2019

Journal of Field Robotics

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Robotic weeding enables weed control near or within crop rows automatically, precisely and effectively. A computer‐vision system was developed for detecting crop plants at different growth stages for robotic weed control. Fusion of color images and depth images was investigated as a means of enhancing the detection accuracy of crop plants under conditions of high weed population. In‐field images of broccoli and lettuce were acquired 3–27 days after transplanting with a Kinect v2 sensor. The image processing pipeline included data preprocessing, vegetation pixel segmentation, plant extraction, feature extraction, feature‐based localization refinement, and crop plant classification. For the detection of broccoli and lettuce, the color‐depth fusion algorithm produced high true‐positive detection rates (91.7% and 90.8%, respectively) and low average false discovery rates (1.1% and 4.0%, respectively). Mean absolute localization errors of the crop plant stems were 26.8 and 7.4 mm for broccoli and lettuce, respectively. The fusion of color and depth was proved beneficial to the segmentation of crop plants from background, which improved the average segmentation success rates from 87.2% (depth‐based) and 76.4% (color‐based) to 96.6% for broccoli, and from 74.2% (depth‐based) and 81.2% (color‐based) to 92.4% for lettuce, respectively. The fusion‐based algorithm had reduced performance in detecting crop plants at early growth stages.

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“…Equation (1) is used to extract a color eye map. In Equation (1) (2) is used to create an eye map in the way of executing dilation and erosion operations [15][16][17] with the use of gray value. In Equation (2),  means dilation and  represents erosion, and g(x, y) is a gray value of an input image.…”

Section: Hand Region Segmentationmentioning

confidence: 99%

User-Friendly Interface Control Using Segmented Skin Areas

Jang¹,

Lee²

2018

IJAST

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Automatic crop detection under field conditions using the HSV colour space and morphological operations

Cited by 211 publications

References 33 publications

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Automated crop plant detection based on the fusion of color and depth images for robotic weed control

User-Friendly Interface Control Using Segmented Skin Areas

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