Image segmentation based on the HSI color space and an improved mean shift

2013 ◽  
Author(s):  
Zengwei Ju ◽  
Jiazhong Chen ◽  
Jingli Zhou
Keyword(s):  
Image Segmentation ◽  
Mean Shift ◽  
Color Space ◽  
Hsi Color Space

scholarly journals Human Perception Based Color Image Segmentation

2013 ◽  
Vol 2 (3) ◽  
Author(s):  
Neeta Pradeep Gargote ◽  
Savitha Devaraj ◽  
Shravani Shahapure
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Color Image ◽  
Color Space ◽  
Human Perception ◽  
Activation Function ◽  
Color Image Segmentation ◽  
Neural Network Architecture ◽  
Hsi Color Space

Color image segmentation is probably the most important task in image analysis and understanding. A novel Human Perception Based Color Image Segmentation System is presented in this paper. This system uses a neural network architecture. The neurons here uses a multisigmoid activation function. The multisigmoid activation function is the key for segmentation. The number of steps ie. thresholds in the multisigmoid function are dependent on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSI color space. Here the main use of neural network is to detect the number of objects automatically from an image. It labels the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images.

An image segmentation method using histograms and the human characteristics of HSI color space for a scene image

2006 ◽  
Vol 10 (1) ◽  
pp. 6-10 ◽  
Author(s):  
Seiji Ito ◽  
Michifumi Yoshioka ◽  
Sigeru Omatu ◽  
Kouji Kita ◽  
Kouichi Kugo
Keyword(s):  
Image Segmentation ◽  
Color Space ◽  
Segmentation Method ◽  
Scene Image ◽  
Hsi Color Space

scholarly journals Seeded Region Growing pada Ruang Warna HSI untuk Segmentasi Citra Ikan Tuna

2017 ◽  
Vol 9 (1) ◽  
pp. 56
Author(s):  
Wanvy Arifha Saputra ◽  
Agus Zainal Arifin
Keyword(s):  
Image Segmentation ◽  
Mathematical Morphology ◽  
Color Space ◽  
Region Growing ◽  
Saturation Intensity ◽  
Seeded Region Growing ◽  
A Value ◽  
Hsi Color Space ◽  
Process Classification

The image of the tuna before entering process classification, it must have a good segmentation results. The result of good segmentation is object and background separate clearly. The image of tuna which has a distribution of light that is uneven and has a complex texture will produce an error segmentation. One method of image segmentation was seeded region growing and parameters that used only two, namely seed and threshold. This research proposed method seeded region growing in the HSI color space for image segmentation of tuna. The Color space of RGB (red green blue) on image of tuna transformed into a color space HSI (hue saturation intensity) then only the hue color space used as segmentation by using seeded region growing. Determination of seed and threshold parameters can do manually and the result of the segmentation do refinement with mathematical morphology. The experiment using 30 image of tuna to segmentation and evaluation methods using RAE (relative foreground area error), MAE (missclassification error) and the MHD (modified Hausdroff distance). The image of the tuna successfully performed segmentation evidenced by a value RAE, ME and MHD respectively are 5,40%, 1,53% dan 0,41%.

scholarly journals Unsupervised segmentation of greenhouse plant images based on modified Latent Dirichlet Allocation

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5036 ◽  
Author(s):  
Yi Wang ◽  
Lihong Xu
Keyword(s):  
Image Segmentation ◽  
Latent Dirichlet Allocation ◽  
Mean Shift ◽  
Language Model ◽  
Color Space ◽  
Main Idea ◽  
Coordinate Space ◽  
Unsupervised Segmentation ◽  
Greenhouse Plant ◽  
Dirichlet Allocation

Agricultural greenhouse plant images with complicated scenes are difficult to precisely manually label. The appearance of leaf disease spots and mosses increases the difficulty in plant segmentation. Considering these problems, this paper proposed a statistical image segmentation algorithm MSBS-LDA (Mean-shift Bandwidths Searching Latent Dirichlet Allocation), which can perform unsupervised segmentation of greenhouse plants. The main idea of the algorithm is to take advantage of the language model LDA (Latent Dirichlet Allocation) to deal with image segmentation based on the design of spatial documents. The maximum points of probability density function in image space are mapped as documents and Mean-shift is utilized to fulfill the word-document assignment. The proportion of the first major word in word frequency statistics determines the coordinate space bandwidth, and the spatial LDA segmentation procedure iteratively searches for optimal color space bandwidth in the light of the LUV distances between classes. In view of the fruits in plant segmentation result and the ever-changing illumination condition in greenhouses, an improved leaf segmentation method based on watershed is proposed to further segment the leaves. Experiment results show that the proposed methods can segment greenhouse plants and leaves in an unsupervised way and obtain a high segmentation accuracy together with an effective extraction of the fruit part.

Color image segmentation based on mean shift and multi-feature fusion

2009 ◽  
Vol 29 (8) ◽  
pp. 2074-2076
Author(s):  
Hua LI ◽  
Ming-xin ZHANG ◽  
Jing-long ZHENG
Keyword(s):  
Image Segmentation ◽  
Feature Fusion ◽  
Color Image ◽  
Mean Shift ◽  
Color Image Segmentation
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