Color image segmentation algorithm based on neural networks

2000 ◽  
Author(s):  
Qizhi Teng ◽  
Xiaohai He ◽  
Li Jiang ◽  
Zhouyu Deng ◽  
Xiaoqiang Wu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Color Image ◽  
Segmentation Algorithm ◽  
Color Image Segmentation ◽  
Image Segmentation Algorithm

scholarly journals A SURVEY ON COLOR IMAGE SEGMENTATION THROUGH LEAKY INTEGRATE AND FIRE MODEL OF SPIKING NEURAL NETWORKS

2013 ◽  
pp. 117-122
Author(s):  
CHINKI CHANDHOK
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Color Image ◽  
Image Features ◽  
Spiking Neural Networks ◽  
Color Image Segmentation ◽  
Information Encoding ◽  
Integrate And Fire ◽  
Spiking Neuron Model ◽  
Database Clustering

Neurological research shows that the biological neurons store information in the timing of spikes. Spiking neural networks are the third generation of neural networks which take into account the precise firing time of neurons for information encoding. In SNNs, computation is performed in the temporal (time related) domain and relies on the timings between spikes. The leaky integrate-and-fire neuron is probably the best-known example of a formal spiking neuron model. In this paper, we have simulated LIF model of SNN for performing the image segmentation using K-Means clustering. Clustering can be termed here as a grouping of similar images in the database. Clustering is done based on different attributes of an image such as size, color, texture etc. The purpose of clustering is to get meaningful result, effective storage and fast retrieval in various areas. Image segmentation is the first step and also one of the most critical tasks of image analysis .Because of its simplicity and efficiency, clustering approach is used for the segmentation of (textured) natural images. After the extraction of the image features using wavelet; the feature samples, handled as vectors, are grouped together in compact but well-separated clusters corresponding to each class of the image. Simulation results therefore demonstrate how SNN can be applied with efficacy in Image Segmentation.

Roughness Measure Approach to Color Image Segmentation

2010 ◽  
Vol 36 (6) ◽  
pp. 807-816 ◽  
Author(s):  
Xiao-Dong YUE ◽  
Duo-Qian MIAO ◽  
Cai-Ming ZHONG
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation

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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