scholarly journals Research on the tea bud recognition based on improved k-means algorithm

2018 ◽  
Vol 232 ◽  
pp. 03050 ◽  
Author(s):  
Peidi Shao ◽  
Minghui Wu ◽  
Xianwei Wang ◽  
Jun Zhou ◽  
Sheng Liu
Keyword(s):  
Recognition Algorithm ◽  
Good Effect ◽  
Color Model ◽  
Tree Leaves ◽  
Tea Tree ◽  
S Factor ◽  
Young Leaves ◽  
Hsi Color Model ◽  
Research Objects ◽  
Image Collection

The identification and extraction of tea buds is the key technology for the development of automated tea picking robots. Machine vision technology is an effective tool for tea bud recognition. In this paper, the tea tree leaves in the tea garden picking period are taken as research objects, and the research experiments are carried out from the aspects of tea image collection, image enhancement, image segmentation, edge detection, binarization and foreground extraction. After continuous exploration and research, the HSI color model is finally selected. After the S factor was used to grayscale the tea image, the improved K-means algorithm was used to identify and separate the tea shoots. The experimental results show that the improved K-means algorithm has a good effect on the segmentation of young leaves in tea images. This study can provide reference and reference for tea bud recognition algorithm.

scholarly journals A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3583 ◽  
Author(s):  
Shiping Ma ◽  
Hongqiang Ma ◽  
Yuelei Xu ◽  
Shuai Li ◽  
Chao Lv ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Color Model ◽  
Low Light ◽  
Image Brightness ◽  
Low Contrast ◽  
Light Sensor ◽  
Hsi Color Model ◽  
Art Research ◽  
Enhancement Algorithm

Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (S) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (I). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.

scholarly journals Mean-Based Breakpoint Selection on Circular Histogram

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiulun Fan ◽  
Jipeng Yang
Keyword(s):  
Selection Criteria ◽  
Color Image ◽  
Selection Criterion ◽  
Threshold Value ◽  
Simple Expression ◽  
Circular Data ◽  
Cumulative Distribution ◽  
Color Model ◽  
Hsi Color Model ◽  
Selection Of

Circular histogram represents the statistical distribution of circular data; the H component histogram of HSI color model is a typical example of the circular histogram. When using H component to segment color image, a feasible way is to transform the circular histogram into a linear histogram, and then, the mature gray image thresholding methods are used on the linear histogram to select the threshold value. Thus, the reasonable selection of the breakpoint on circular histogram to linearize the circular histogram is the key. In this paper, based on the angles mean on circular histogram and the line mean on linear histogram, a simple breakpoint selection criterion is proposed, and the suitable range of this method is analyzed. Compared with the existing breakpoint selection criteria based on Lorenz curve and cumulative distribution entropy, the proposed method has the advantages of simple expression and less calculation and does not depend on the direction of rotation.

Computer Vision Based Orange Grading Using SVM

2013 ◽  
Vol 303-306 ◽  
pp. 1134-1138 ◽  
Author(s):  
Zhi Bin Pan ◽  
Xiao Yan Wei
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Experimental Result ◽  
Support Vector ◽  
Color Model ◽  
Fruit Grading ◽  
Hsi Color Model ◽  
Four Levels ◽  
Additional Value

Fruit grading is very important for promoting its additional value. We graded oranges based on its images. Four photos were taken from different view angles for each orange. Both RGB and HSI color model were utilized. We extracted a 28-dimensional feature which can describe the size and color of them. Then support vector machine was used to grade these oranges into four levels. Experimental result shows SVM has promising performance for orange grading.

High-speed 3D profilometry employing HSI color model for color surface with discontinuities

2017 ◽  
Vol 96 ◽  
pp. 81-87 ◽  
Author(s):  
Wei Yin ◽  
Xiaosheng Cheng ◽  
Jieru Xie ◽  
Haihua Cui ◽  
Yingying Chen
Keyword(s):  
High Speed ◽  
Color Model ◽  
3D Profilometry ◽  
Hsi Color Model

scholarly journals Measuring Leaf Area in Soy Plants by HSI Color Model Filtering and Mathematical Morphology

2011 ◽  
Vol 332 ◽  
pp. 012034 ◽  
Author(s):  
M Benalcázar ◽  
J Padín ◽  
M Brun ◽  
J Pastore ◽  
V Ballarin ◽  
...  
Keyword(s):  
Leaf Area ◽  
Mathematical Morphology ◽  
Color Model ◽  
Hsi Color Model

scholarly journals RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 925-928 ◽  
Author(s):  
Z. Wang ◽  
P. Liu ◽  
T. Cui
Keyword(s):  
Forest Fire ◽  
Clustering Algorithm ◽  
Color Space ◽  
Recognition Algorithm ◽  
Image Features ◽  
Image Feature ◽  
Color Model ◽  
Forest Environment ◽  
Fire Monitoring ◽  
In Fire

In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

scholarly journals To investigate the clinical effect of modified closed negative pressure suction technique combined with flap transplantation on the treatment of deep chronic refractory wounds

2019 ◽  
Vol 6 (1) ◽  
pp. 11
Author(s):  
Zhihui Hou ◽  
Mingjuan Gu
Keyword(s):  
Treatment Group ◽  
Negative Pressure ◽  
Clinical Effect ◽  
Random Number ◽  
Good Effect ◽  
Control Group ◽  
Flap Reconstruction ◽  
Random Number Table ◽  
Research Objects ◽  
Pressure Suction

Objective: To investigate the clinical effect of modified closed negative pressure suction technique combined with flap transplantation on the treatment of deep chronic refractory wounds.Methods: During March of 2015 to April of 2018, 52 cases of patients with deep chronic refractory wounds were selected as research objects. They were divided into the control group and the treatment group by use of the random number table method, with 26 cases in each group. Among them, the control group was given conventional debridement combined with flap reconstruction, and the treatment group was treated with modified closed negative pressure suction technique combined with flap transplantation to observe the clinical effect.Results: (1) According to the analysis on the effect of flap transplantation, the excellent and good rate of the treatment group was 92.3%, and in the control group, it was 76.9% (p < .05). (2) According to the statistics, the incidence of complications in the treatment group was lower than that in the control group (p < .05).Conclusions: Modified closed negative pressure suction technique combined with flap transplantation has a good effect on the treatment of deep chronic refractory wounds with fewer complications.

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