scholarly journals Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3918 ◽  
Author(s):  
Wei Lu ◽  
Mengjie Zeng ◽  
Ling Wang ◽  
Hui Luo ◽  
Subrata Mukherjee ◽  
...  
Keyword(s):  
Processing Speed ◽  
Color Space ◽  
Boundary Line ◽  
Agricultural Field ◽  
Vision Navigation ◽  
Ycbcr Color Space ◽  
Navigation Algorithm ◽  
Guided Filtering ◽  
Tillage Operation ◽  
Morphology Algorithm

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094   s in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of 0.113   s . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of 140   ×   260 pixels could achieve high-precision vision navigation while the course deviation angle was not more than 7.5 ° . The maximum tractor speed of the optimal template and global template were 51.41   km / h and 27.47   km / h , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.

scholarly journals Navigation Algorithm Based on the Boundary Line of New & Old Soil Using Improved Anti-Noise Morphology

2019 ◽  
Author(s):  
Wei Lu ◽  
Mengjie Zeng ◽  
Ling Wang ◽  
Hui Luo ◽  
Yiming Deng
Keyword(s):  
Processing Speed ◽  
Color Space ◽  
Boundary Line ◽  
Agricultural Field ◽  
Vision Navigation ◽  
Ycbcr Color Space ◽  
Navigation Algorithm ◽  
Guided Filtering ◽  
Tillage Operation ◽  
Morphology Algorithm

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. Firstly, the two key steps, Guided Filtering and improved anti-noise morphology navigation line extraction, were addressed in detail. Then the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption, 0.094 s, compared with HSV, HIS and 2R-G-B color spaces. The Guided Filtering method can enhance the new & old soil boundary effectively than any other methods such as Tarel, Multi-scale Retinex, Wavelet-based Retinex and Homomorphic Filtering, meanwhile, has the fastest processing speed of 0.113 s. The extracted soil boundary line of the improved anti-noise morphology algorithm has best precision and speed compared with other operators such as Sobel, Roberts, Prewitt and Log. After comparing different size of image template, the optimal template with the size of 140×260 pixels can meet high precision vision navigation while the course deviation angle is not more than 7.5°. The maximum tractor speed of the optimal template and global template are 51.41 km/h and 27.47 km/h respectively which can meet real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the new & old soil boundary line extracted by the proposed improved anti-noise morphology algorithm which has broad application prospect.

scholarly journals Navigation Algorithm Based on the Boundary Line of New and Old Soil Combined Using Guided Filtering and Improved Anti-noise Morphology

2019 ◽  
Author(s):  
Wei LU ◽  
Mengjie Zeng ◽  
Ling WANG ◽  
Hui LUO ◽  
Subrata Mukherjee ◽  
...  
Keyword(s):  
Processing Speed ◽  
Color Space ◽  
Boundary Line ◽  
Agricultural Field ◽  
Vision Navigation ◽  
Ycbcr Color Space ◽  
Navigation Algorithm ◽  
Guided Filtering ◽  
Tillage Operation ◽  
Morphology Algorithm

An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first the two key steps, Guided Filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and it’s application condition were studied for improving the image processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094 s in comparison with HSV, HIS and 2R-G-B color spaces. The Guided Filtering method can effectively distinguish the boundary between the new and old soil than other competing vanilla methods such as Tarel, Multi-scale Retinex, Wavelet-based Retinex and Homomorphic Filtering inspite of having the fastest processing speed of 0.113 s. The extracted soil boundary line of the improved anti-noise morphology algorithm has best precision and speed compared with other operators such as Sobel, Roberts, Prewitt and Log. After comparing different size of image template, the optimal template with the size of 140×260 pixels can meet high precision vision navigation while the course deviation angle is not more than 7.5°. The maximum tractor speed of the optimal template and global template are 51.41 km/h and 27.47 km/h respectively which can meet real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the new and old soil boundary line extracted by the proposed improved anti-noise morphology algorithm which has broad application prospect.

scholarly journals Deteksi Api pada Video dengan Gaussian Mixture Model Untuk Deteksi Gerakan Dan Segmentasi Warna Api Dalam Ruang Warna YCbCr

2021 ◽  
Vol 3 (1) ◽  
pp. 108-119
Author(s):  
Ristirianto Adi ◽  
I Gede Pasek Suta Wijaya
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Processing Speed ◽  
Color Space ◽  
Gaussian Mixture ◽  
Color Segmentation ◽  
Test Results ◽  
Ycbcr Color Space ◽  
Property Loss ◽  
Better Than

Fire is a disaster that can endanger lives and cause property loss. The solution to detect fire that is commonly used today is to use a sensor. Fire sensors can be used together with surveillance cameras (CCTV) which are now being installed in many office buildings. This study tries to build a model for detecting fire in video with a digital image processing approach using the Gaussian Mixture Model for motion detection and fire color segmentation in the YCbCr color space. The model is then tested with metrics for accuracy, precision, recall, and processing speed. The dataset used is in the form of videos with small, medium, large fire sizes, and videos that only have fire-like objects. The test results show that the algorithm is able to detect fire when the size of the fire is not too small or the position of the fire is close enough to the camera. For videos with a resolution of 800x600 and a framerate of 30 fps, it can achieve 66.89% accuracy, 73.77% precision, and 66.66% recall. The performance during the day is relatively better than at night. Algorithm processing speed is too slow to be implemented in real-time

Efficient RGB to YCbCr Color Space Conversion for Embedded Application

2014 ◽  
Vol 543-547 ◽  
pp. 2873-2878
Author(s):  
Hui Yong Li ◽  
Hong Xu Jiang ◽  
Ping Zhang ◽  
Han Qing Li ◽  
Qian Cao
Keyword(s):  
Direct Method ◽  
Color Space ◽  
Video Data ◽  
Floating Point ◽  
Portable Devices ◽  
Embedded Processor ◽  
Color Space Conversion ◽  
Ycbcr Color Space ◽  
Original Approach ◽  
General Method

Modern embedded portable devices usually have to deal with large amounts of video data. Due to massive floating-point multiplications, the color space conversion is inefficient on the embedded processor. Considering the characteristics of RGB to YCbCr color space conversion, this paper proposed a strategy for truncated-based LUT Multiplier (T-LUT Multiplier). On this base, an original approach converting RGB to YCbCr is presented which employs the T-LUT Multiplier and the pipeline-based adder. Experimental results demonstrate that the proposed method can obtain maximum operating frequency of 358MHz, 3.5 times faster than the direct method. Furthermore, the power consumption is less than that of the general method approximately by 15%~27%.

Human Body Parts Detection Using YCbCr Color Space

2013 ◽  
Vol 393 ◽  
pp. 556-560
Author(s):  
Nurul Fatiha Johan ◽  
Yasir Mohd Mustafah ◽  
Nahrul Khair Alang Md Rashid
Keyword(s):  
Human Body ◽  
Skin Color ◽  
Color Space ◽  
Detection Algorithm ◽  
Color Segmentation ◽  
Color Model ◽  
Body Parts ◽  
Color Spaces ◽  
Morphological Operation ◽  
Ycbcr Color Space

Skin color is proved to be very useful technique for human body parts detection. The detection of human body parts using skin color has gained so much attention by many researchers in various applications especially in person tracking, search and rescue. In this paper, we propose a method for detecting human body parts using YCbCr color spaces in color images. The image captured in RGB format will be transformed into YCbCr color space. This color model will be converted to binary image by using color thresholding which contains the candidate human body parts like face and hands. The detection algorithm uses skin color segmentation and morphological operation.

scholarly journals Tea leaf maturity levels based on ycbcr color space and clustering centroid

2019 ◽  
Vol 1367 ◽  
pp. 012028
Author(s):  
Bagaskara Aji Wicaksono ◽  
Ledya Novamizanti ◽  
Nur Ibrahim
Keyword(s):  
Color Space ◽  
Leaf Maturity ◽  
Ycbcr Color Space ◽  
Tea Leaf

A Skin Detection Algorithm Based on Bayes Decision in the YCbCr Color Space

2011 ◽  
Vol 121-126 ◽  
pp. 672-676 ◽  
Author(s):  
Xin Yan Cao ◽  
Hong Fei Liu
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Bayesian Decision ◽  
Color Classification ◽  
Color Distribution ◽  
Ycbcr Color Space ◽  
The Face ◽  
Good Coverage

Skin color detection is a hot research of computer vision, pattern identification and human-computer interaction. Skin region is one of the most important features to detect the face and hand pictures. For detecting the skin images effectively, a skin color classification technique that employs Bayesian decision with color statistics data has been presented. In this paper, we have provided the description, comparison and evaluation results of popular methods for skin modeling and detection. A Bayesian approach to skin color classification was presented. The statistics of skin color distribution were obtained in YCbCr color space. Using the Bayes decision rule for minimum cot, the amount of false detection and false dismissal could be controlled by adjusting the threshold value. The results showed that this approach could effectively identify skin color pixels and provide good coverage of all human races, and this technique is capable of segmenting the hands and face quite effectively. The algorithm allows the flexibility of incorporating additional techniques to enhance the results.

Shadow removal based on YCbCr color space

2015 ◽  
Vol 151 ◽  
pp. 252-258 ◽  
Author(s):  
Xia Zhu ◽  
Renwen Chen ◽  
Huakang Xia ◽  
Piaoyan Zhang
Keyword(s):  
Color Space ◽  
Shadow Removal ◽  
Ycbcr Color Space
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