Mean-shift-based color segmentation of images containing green vegetation

Authors:
Liying Zheng;Jingtao Zhang;Qianyu Wang
Affiliations:
School of Computer Science and Technology, Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin 150001, China and Hejiang Institute of Agriculture Science, Heilongjiang Acad ...;Hejiang Institute of Agriculture Science, Heilongjiang Academy of Agricultural Science, Jiamusi 154007, China;Hejiang Institute of Agriculture Science, Heilongjiang Academy of Agricultural Science, Jiamusi 154007, China
Venue:
Computers and Electronics in Agriculture
Year:
2009

Citing 9
Cited 13

Real-time segmentation of plants and weeds

Real-Time Imaging
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Processing, Analysis, and Machine Vision

Image Processing, Analysis, and Machine Vision
A vision-based method for weeds identification through the Bayesian decision theory

Pattern Recognition
Original paper: A vision based row detection system for sugar beet

Computers and Electronics in Agriculture
Crop/weed discrimination in perspective agronomic images

Computers and Electronics in Agriculture
Individual leaf extractions from young canopy images using Gustafson-Kessel clustering and a genetic algorithm

Computers and Electronics in Agriculture
The estimation of the gradient of a density function, with applications in pattern recognition

IEEE Transactions on Information Theory

Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminant

Pattern Recognition Letters
Original paper: Automatic segmentation of relevant textures in agricultural images

Computers and Electronics in Agriculture
Original paper: Real-time image processing for crop/weed discrimination in maize fields

Computers and Electronics in Agriculture
Original paper: Segmentation of pores in wood microscopic images based on mathematical morphology with a variable structuring element

Computers and Electronics in Agriculture
Recognition of porosity in wood microscopic anatomical images

ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
A method for identification and classification of medicinal plant images based on level set segmentation and SVM classification

International Journal of Computational Vision and Robotics
Support Vector Machines for crop/weeds identification in maize fields

Expert Systems with Applications: An International Journal
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends

Machine Graphics & Vision International Journal
Automatic expert system for weeds/crops identification in images from maize fields

Expert Systems with Applications: An International Journal
Generalized Transport Mean Shift algorithm for ubiquitous intelligence

Simulation
A new Expert System for greenness identification in agricultural images

Expert Systems with Applications: An International Journal
An image segmentation method for apple sorting and grading using support vector machine and Otsu's method

Computers and Electronics in Agriculture
An approach based on simulated annealing to optimize the performance of extraction of the flower region using mean-shift segmentation

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Separating green vegetation in color images is a complex task especially when there are noises and shadows in the images. Our objective is to improve the segmentation rate of the images containing green vegetation by introducing a mean-shift procedure into the segmentation algorithm. The proposed algorithm mainly consists of two stages-feature extraction and image segmentation. At the first step, multiple color features, such as hue and saturation in HSI color space were extracted, as well as red, green and blue value in RGB color space. At the second step, with the extracted features, mean-shift segmentation algorithm and a BPNN, the image was classified into two parts: green and non-green vegetation. The algorithm's performance was assessed on 100 images, which were acquired under field conditions, covering different plant types, illuminations, and soil types. The test showed that the median of mis-segmentation of green and non-green vegetation of proposed method is about 4.2%.