scholarly journals Crop Disease Leaf Image Segmentation Method Based on Color Features

2008 ◽  
pp. 713-717 ◽  
Author(s):  
Lidi Wang ◽  
Tao Yang ◽  
Youwen Tian
Keyword(s):  
Image Segmentation ◽  
Segmentation Method ◽  
Color Features ◽  
Crop Disease ◽  
Leaf Image

Crop Disease Leaf Image Segmentation Based on Genetic Algorithm and Maximum Entropy

2015 ◽  
Vol 713-715 ◽  
pp. 1670-1674 ◽  
Author(s):  
Ming Gang Du ◽  
Shan Wen Zhang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Fitness Function ◽  
Convergence Criterion ◽  
Segmentation Method ◽  
Uniform Illumination ◽  
Crop Disease ◽  
Main Spot ◽  
Leaf Image

Crop disease leaf image segmentation is a key step in crop disease recognition. In the paper, a segmentation method of crop disease leaf image is proposed to segment leaf image with non-uniform illumination based on maximum entropy and genetic algorithm (GA). The information entropy is regarded as the fitness function of GA, the maximum entropy as convergence criterion of GA. After genetic operation, the optimal threshold is obtained to segment the image of disease leaf. The experimental results of the maize disease leaf image show that the proposed method can select the threshold automatically and efficiently, and has an advantage over the other three algorithms, and also can reserve the main spot features of the original disease leaf image.

An Algorithm of Leaf Image Segmentation Based on Color Features

2011 ◽  
Vol 474-476 ◽  
pp. 846-851 ◽  
Author(s):  
Jie Yun Bai ◽  
Hong E Ren
Keyword(s):  
Image Segmentation ◽  
Digital Image ◽  
Color Information ◽  
Color Features ◽  
Green Component ◽  
Good Consistency ◽  
High Degree ◽  
Similarity Distance ◽  
Leaf Image ◽  
Rgb Model

The paper proposes a digital image extraction and segmentation algorithm based on color features. The traditional transformation from RGB model to HSI model is improved, meanwhile the leaf color information is extracted by similarity distance between pixels. The green component of leaf image in the RGB model is strengthened, and then the digital image is transformed to the HSI model by the improved method. Finally the image is divided by similarity distance of pixels’ H weight which determines whether the pixel belongs to the blade. The results of simulation experiment shows that this algorithm can achieve a good image segmentation effect, and it has a high degree of accuracy as well as a clearly distinguish degree and many other advantages such as good consistency with human visual system. It completely meets the effectiveness and clarity requirements of image segmentation.

Unsupervised Image Segmentation via Affinity Propagation

2014 ◽  
Vol 610 ◽  
pp. 464-470 ◽  
Author(s):  
Wei Fu Peng ◽  
Shu Du ◽  
Fu Xiang Li
Keyword(s):  
Image Segmentation ◽  
Natural Images ◽  
Affinity Propagation ◽  
Complex Nature ◽  
Segmentation Method ◽  
Local Texture ◽  
Color Features ◽  
Segmentation Methods ◽  
Complex Scenes ◽  
Unsupervised Image Segmentation

Image segmentation is an important research subject in the area of image processing. Most of the existing image segmentation methods partition the image based on the single cue of the image, the color, which brings a serious limitation when the complex scenes involve in the natural images. In this paper, we introduce a novel unsupervised image segmentation method via affinity propagation which takes into local texture and color features with superpixel map. The new method fuses color and texture information as local feature of each superpixel. The experimental results show that the proposed method performs better and steadier when partitioning various complex nature images, comparing to the existing methods.

Multiresolution Bio-Medical Image Segmentation using Fuzzy C-Means Clustering

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Experimental Result ◽  
Segmentation Method ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Robust Image ◽  
Multiple Clusters ◽  
Single Pixel ◽  
Popular Image

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.

scholarly journals Active Learning with Bayesian UNet for Efficient Semantic Image Segmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 37
Author(s):  
Isah Charles Saidu ◽  
Lehel Csató
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Active Learning ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Segmentation Method ◽  
Semantic Image Segmentation ◽  
Batch Normalization ◽  
Set Up ◽  
Image Datasets

We present a sample-efficient image segmentation method using active learning, we call it Active Bayesian UNet, or AB-UNet. This is a convolutional neural network using batch normalization and max-pool dropout. The Bayesian setup is achieved by exploiting the probabilistic extension of the dropout mechanism, leading to the possibility to use the uncertainty inherently present in the system. We set up our experiments on various medical image datasets and highlight that with a smaller annotation effort our AB-UNet leads to stable training and better generalization. Added to this, we can efficiently choose from an unlabelled dataset.

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