hsi color model
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2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiulun Fan ◽  
Jipeng Yang

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.


Author(s):  
Suresh Sundarajoo ◽  
Ahmad Shahrizan Abdul Ghani

<p>Auto tracking mobile robot is a device that able to detect and track a target. For an auto tracking device, the most crucial part of the system is the object identification and tracking of the moving targets. In order to improve the accuracy of identification of object in different illumination and background conditions, the implementation of HSI color model is used in image processing algorithm. In this project HSI-based color filtering algorithm were used for object identification. This is because HSI parameter are more stable in different light and background conditions, so it is selected as the main parameters of this system. Pixy CMUcam5 is used as the vision sensor while Arduino Uno as the main microcontroller that controls all the input and output of the device. Besides that, L293D is used as the motor driver to control the movement of two DC motors that attached to the wheel of the robot. Moreover, two servo motors were used to control the pan-tilt movement of the vision sensor. Experimental results demonstrate that when HSI color-based filtering algorithm is applied to visual tracking it improves the accuracy and stability of tracking under the condition of varying brightness, or even in the low-light-level environment. Besides that, this algorithm also prevents tracking loss due to object color appears in the background.</p>


Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3583 ◽  
Author(s):  
Shiping Ma ◽  
Hongqiang Ma ◽  
Yuelei Xu ◽  
Shuai Li ◽  
Chao Lv ◽  
...  

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.


2018 ◽  
Vol 232 ◽  
pp. 03050 ◽  
Author(s):  
Peidi Shao ◽  
Minghui Wu ◽  
Xianwei Wang ◽  
Jun Zhou ◽  
Sheng Liu

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.


2017 ◽  
Vol 96 ◽  
pp. 81-87 ◽  
Author(s):  
Wei Yin ◽  
Xiaosheng Cheng ◽  
Jieru Xie ◽  
Haihua Cui ◽  
Yingying Chen

2015 ◽  
Vol 781 ◽  
pp. 616-619 ◽  
Author(s):  
Aeggarut Pinkaew ◽  
Tulaya Limpiti ◽  
Akraphon Trirat

Malaria is a serious global health problem and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to aid the diagnosis of malaria on thick blood films is developed. Morphological and automatic threshold selection techniques are applied on two color components from the HSI color model to identify chromatins of P. Falciparum and P. Vivax malaria species on the images. Chromatins are positively identified with good sensitivities for both species. After identifying the position of chromatins, the algorithm splits the image into small sub-images, each with a chromatin in the center. These small images can subsequently be used by technician to classify malaria species more conveniently.


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