Underwater Image Segmentation Combining Dual-Band Enhancing and Edge-Grouping

2011 ◽  
Vol 121-126 ◽  
pp. 1794-1798
Author(s):  
Kun Zhao ◽  
Yi Ping Xu ◽  
Fu Yuan Peng ◽  
Guo Liang Yang ◽  
Xin Wei Wang
Keyword(s):  
Image Segmentation ◽  
Experimental Results ◽  
Dual Band ◽  
Segmentation Method ◽  
Two Stage ◽  
Underwater Environment ◽  
Underwater Image ◽  
Grouping Method ◽  
Edge Grouping ◽  
Stability And Accuracy

In order to make segmentation more robust and accurate in the underwater environment, a two-stage segmentation method is proposed in this paper. In preprocessing stage, a dual-band enhancing technique is used to preserve the target contour and at the same time eliminate the fake edges generated by the noises; in segmentation stage, edge-grouping method is chosen for its advantageous characteristics over noisy images. Experimental results show that the proposed method can get a better performance both in stability and accuracy.

Comparison of Thresholding and Edge Detection Segmentation Techniques

2013 ◽  
Vol 860-863 ◽  
pp. 2783-2786
Author(s):  
Yu Bing Dong ◽  
Hai Yan Wang ◽  
Ming Jing Li
Keyword(s):  
Image Segmentation ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Experimental Results ◽  
Mean Square ◽  
Segmentation Method ◽  
Segmentation Algorithms

Edge detection and thresholding segmentation algorithms are presented and tested with variety of grayscale images in different fields. In order to analyze and evaluate the quality of image segmentation, Root Mean Square Error is used. The smaller error value is, the better image segmentation effect is. The experimental results show that a segmentation method is not suitable for all images segmentation.

PCNN Based Welding Seam Image Segmentation Algorithm

2012 ◽  
Vol 155-156 ◽  
pp. 861-866 ◽  
Author(s):  
Bei Ji Zou ◽  
Hao Yu Zhou ◽  
Zai Liang Chen ◽  
Hao Chen ◽  
Guo Jiang Xin
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Experimental Results ◽  
Spatial Proximity ◽  
Variance Ratio ◽  
Segmentation Method ◽  
Welding Seam ◽  
Pulse Coupled Neural Network ◽  
Image Segmentation Algorithm

A new welding seam image segmentation method based on pulse-coupled neural network (PCNN) is presented in this paper. The method segments image by utilizing PCNN’s specific feature that the fire of one neuron can capture firing of its adjacent neurons due to their spatial proximity and intensity similarity. The method can automatically confirm the best iteration times by comparing the maximum of variance ratio and get the best segmentation results. Experimental results show that the proposed method has good performance in both results and execution efficiency.

FEATURE EXTRACTION BY RANK-ORDER FILTERING FOR IMAGE SEGMENTATION

1988 ◽  
Vol 02 (02) ◽  
pp. 301-319 ◽  
Author(s):  
P. ZAMPERONI
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Density Function ◽  
Rank Order ◽  
Experimental Results ◽  
Textural Features ◽  
Segmentation Method ◽  
Unified Approach

The aim of this paper is to outline a unified approach to feature extraction for segmentation purposes by means of the rank-order filtering of grey values in a neighbourhood of each pixel of a digitized image. In the first section an overview of rank-order filtering for image processing is given, and a fast histogram algorithm is proposed. Section 2 deals with the extraction of a “locally most representative grey value”, defined as the maximum of the local histogram density function. In Section 3 several textural features are described, which can be extracted from the local histogram by means of rank-order filtering, and their properties are discussed. Section 4 formulates some general requirements to be met by the process of image segmentation, and describes a method based upon the features introduced in the former sections. In the last section some experimental results applied to aerial views obtained with the segmentation method of Sect. 4 are reported. These test images have been analyzed within the scope of an investigation centered on terrain recognition for agricultural and ecological purposes.

A New Medical Image Segmentation Method Based on Chan-Vese Model

2014 ◽  
Vol 513-517 ◽  
pp. 3750-3756 ◽  
Author(s):  
Yuan Zheng Ma ◽  
Jia Xin Chen
Keyword(s):  
Image Segmentation ◽  
Mathematical Morphology ◽  
Level Sets ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Experimental Results ◽  
Segmentation Method ◽  
Accuracy Requirement ◽  
Segmentation Accuracy ◽  
Segmentation Problem

The traditional segmentation method for medical image segmentation is difficult to achieve the accuracy requirement, and when the edges of the image are blurred, it will occurs incomplete segmentation problem, in order to solve this problem, we propose a medical image segmentation method which based on Chan-Vese model and mathematical morphology. The method integrates Chan-Vese model, mathematical morphology, composite multiphase level sets segmentation algorithm, first, through iterative etching operation to extract the outline of the medical image, and then the medical image is segmented by the Chan-Vese model based on the complex multiphase level sets, finally the medical image image is dilated iteratively by using morphological dilation to restore the image. The experimental results and analysis show that, this method improves the multi-region segmentation accuracy during the segmentation of medical image and solves the problem of incomplete segmentation.

Research on Low Quality Texture Image Segmentation Background

2014 ◽  
Vol 687-691 ◽  
pp. 3616-3619
Author(s):  
Ning Liu ◽  
Hong Xia Wang
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Experimental Results ◽  
Texture Image ◽  
Segmentation Method ◽  
Otsu Thresholding ◽  
Complex Background ◽  
Segmentation Methods ◽  
Texture Image Segmentation

In image processing, the texture image segmentation is one of the most important issues. Considering the problem that the traditional segmentation methods often fail to the low quality texture image segmentation, this paper proposes a modified OTSU thresholding segmentation method. Experimental results show that the proposed method not only is well adapt to the change of brightness and contrast, but also can be applied to much complex background.

Research on Multi-Threshold Color Image Segmentation Based on Rough Set

2011 ◽  
Vol 474-476 ◽  
pp. 771-776
Author(s):  
Guo Quan Zhang ◽  
Zhan Ming Li
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Color Image ◽  
Experimental Results ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Illumination Variation ◽  
Threshold Segmentation ◽  
Segmentation Accuracy ◽  
Novel Method

Aims at the problem that the threshold number and value are difficulty to determine automatically existing in multi-threshold color image segmentation method, a novel method of multi-threshold segmentation in HSV is proposed. First of all, the image is pre-processed in HSV, component H and V is projected to S and be quantified at the same time. Secondly, histogram and advanced Histon histogram (AHH) are constructed. According to concept of roughness in the theory of Rough Set, the histogram of roughness (RSH) is constructed. Finally, according to requirement of segmentation accuracy, set a threshold Hn on RSH to determine the number and scope of multi-threshold and the image is segmented with above thresholds. The experimental results show that this method can determine the threshold quantity automatically, segment image efficiently and robust against illumination variation.

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