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

2017 ◽  
Vol 133 ◽  
pp. 97-107 ◽  
Author(s):  
Esmael Hamuda ◽  
Brian Mc Ginley ◽  
Martin Glavin ◽  
Edward Jones
Keyword(s):  
Field Conditions ◽  
Morphological Operations ◽  
Colour Space ◽  
Hsv Colour Space

scholarly journals Research on the Application of Artificial Intelligence Technology in Computer Graphics Processing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042037
Author(s):  
Xia Yang
Keyword(s):  
Artificial Intelligence ◽  
Threshold Value ◽  
Shooting Methods ◽  
Colour Space ◽  
Space Transformation ◽  
Artificial Intelligence Technology ◽  
Colour Saturation ◽  
Hsv Colour Space ◽  
Graphics Processing ◽  
Bright Area

Abstract In structured light geometric reconstruction, due to the complexity of shooting methods and scene lighting conditions, the resulting images may be lack of image details due to uneven light. For this reason, the article proposes a Retinex algorithm with colour restoration and colour saturation correction strategy based on HSV colour space transformation based on artificial intelligence technology. Then distinguish whether it is a bright area according to the threshold value, and modify the insufficient transmittance estimation of the bright area. Finally, the intensity component and saturation value are restored in the HIS colour space, and the histogram is used to stretch the intensity component.

scholarly journals Robust Object Detection in Colour Images Using a Multivariate Percentage Occupancy Hit-or-Miss Transform

2021 ◽  
Vol 5 (1) ◽  
pp. 128-152
Author(s):  
Fraser Macfarlane ◽  
Paul Murray ◽  
Stephen Marshall ◽  
Benjamin Perret ◽  
Adrian Evans ◽  
...  
Keyword(s):  
Object Detection ◽  
Mathematical Morphology ◽  
Morphological Operations ◽  
Design Tools ◽  
Partial Occlusion ◽  
Colour Space ◽  
Multivariate Images ◽  
Optimal Values ◽  
Ordering Schemes ◽  
Robust To Noise

Abstract The extension of Mathematical Morphology to colour and multivariate images is challenging due to the need to define a total ordering in the colour space. No one general way of ordering multivariate data exists and, therefore, there is no single, definitive way of performing morphological operations on colour images. In this paper, we propose an extension to mathematical morphology, based on reduced ordering, specifically the morphological Hit-or-Miss Transform which is used for object detection. The reduced ordering employed transforms multivariate observations to scalar comparisons allowing for an order to be derived and for both flat and non-flat structuring elements to be used. We also compare other definitions of the Hit-or-Miss Transform and test alternative colour ordering schemes presented in the literature. Our proposed method is shown to be intuitive and outperforms other approaches to multivariate Hit-or-Miss Transforms. Furthermore, methods of setting the parameters of the proposed Hit-or-Miss Transform are introduced in order to make the transform robust to noise and partial occlusion of objects and, finally, a set of design tools are presented in order to obtain optimal values for setting these parameters accordingly.

scholarly journals Color-Based for Tree Yield Fruits Image Counting

2017 ◽  
Author(s):  
Faisel G. Mohammed ◽  
Wejdan A. Amer
Keyword(s):  
Shape Analysis ◽  
Vision System ◽  
Automatic Segmentation ◽  
Research Work ◽  
Low Complexity ◽  
Morphological Operations ◽  
Agricultural Crop ◽  
Colour Space ◽  
Citrus Grove ◽  
Moderate Cost

Identifying the total number of fruits on trees has long been of interest in agricultural crop estimation work. Yield prediction of fruits in practical environment is one of the hard and significant tasks to obtain better results in crop management system to achieve more productivity with regard to moderate cost. Utilized color vision in machine vision system to identify citrus fruits, and estimated yield information of the citrus grove in-real time. Fruit recognition algorithms based on color features to estimate the number of fruit. In the current research work, some low complexity and efficient image analysis approach was proposed to count yield fruits image in the natural scene. Semi automatic segmentation and yield calculation of fruit based on shape analysis is presented. Color and shape analysis was utilized to segment the images of different fruits like apple, pomegranate obtained under different lighting conditions. First the input sectional tree image was converted from RGB colour space into the colour space transform (i.e., YUV, YIQ, or YCbCr). The resultant image was then applied to the algorithm for fruit segmentation. After it is applied Morphological Operations which is enhanced image then execute Blob counting method which identify the object and count the number of it. Accuracy of this algorithm used in this thesis is 82.21% for images that have been scanned.

Using Fuzzy Segmentation for Colour Image Enhancement of Computed Tomography Perfusion Images

2011 ◽  
pp. 253-262
Author(s):  
Martin Tabakov
Keyword(s):  
Computed Tomography ◽  
Image Enhancement ◽  
Brain Perfusion ◽  
Colour Space ◽  
Computed Tomography Perfusion ◽  
Fuzzy Similarity ◽  
Colour Image ◽  
Fuzzy Similarity Relation ◽  
Hsv Colour Space ◽  
Fuzzy Segmentation

This chapter presents a methodology for an image enhancement process of computed tomography perfusion images by means of partition generated with appropriately defined fuzzy relation. The proposed image processing is used to improve the radiological analysis of the brain perfusion. Colour image segmentation is a process of dividing the pixels of an image in several homogenously- coloured and topologically connected groups, called regions. As the concept of homogeneity in a colour space is imprecise, a measure of dependency between the elements of such a space is introduced. The proposed measure is based on a pixel metric defined in the HSV colour space. By this measure a fuzzy similarity relation is defined, which next is used to introduce a clustering method that generates a partition, and so a segmentation. The achieved segmentation results are used to enhance the considered computed tomography perfusion images with the purpose of improving the corresponding radiological recognition.

An Improved Keyframe Extraction Method Based on HSV Colour Space

2013 ◽  
Vol 8 (7) ◽  
Author(s):  
Zhong Qu ◽  
Lidan Lin ◽  
Tengfei Gao ◽  
Yongkun Wang
Keyword(s):  
Extraction Method ◽  
Colour Space ◽  
Keyframe Extraction ◽  
Hsv Colour Space
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