IMAGE SEGMENTATION USING NCUT IN THE WAVELET DOMAIN

2006 ◽  
Vol 06 (04) ◽  
pp. 569-582 ◽  
Author(s):  
EMMA REGENTOVA ◽  
DONGSHENG YAO ◽  
SHAHRAM LATIFI ◽  
JUN ZHENG
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Graph Partitioning ◽  
Perceptual Grouping ◽  
Image Features ◽  
Discrete Wavelet ◽  
Wavelet Domain ◽  
Segmentation Method ◽  
Normalized Cuts ◽  
Grouping Problem

A new image segmentation method is developed that combines the advantage of the normalized cuts (Ncut) algorithm to solve the perceptual grouping problem by means of graph partitioning, and the ability of wavelet transform to capture image features by decomposing signal both in time and frequency. We derive image features from orientation histograms defined on the detail subbands of the discrete wavelet transform. The segmentation is implemented by partitioning a graph representing an image at the coarsest transform level, while the weights of the graph are calculated from all the scales. Due to the reduced dimensionality of the dataset, the speed of Ncut is increased. Even though segmentation is carried out at a coarsest level of transform, the results are accurate for images of different structural contents, including textures.

Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

2011 ◽  
Vol 90-93 ◽  
pp. 2836-2839 ◽  
Author(s):  
Jian Cui ◽  
Dong Ling Ma ◽  
Ming Yang Yu ◽  
Ying Zhou
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Mean Shift ◽  
Image Features ◽  
Small Scale ◽  
Region Merging ◽  
Segmentation Method ◽  
Shift Method ◽  
Scale Parameters

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

Multifocus image fusion based on coefficient significance of redundant discrete wavelet transform

2019 ◽  
Vol 46 (3) ◽  
pp. 377-383
Author(s):  
Jianhua Liu ◽  
Peng Geng ◽  
Hongtao Ma
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Discrete Wavelet Transform ◽  
Fusion Rule ◽  
Discrete Wavelet ◽  
Wavelet Domain ◽  
Content Type ◽  
Ripplet Transform ◽  
Detail Coefficient ◽  
Redundant Discrete Wavelet Transform

Purpose This study aims to obtain the more precise decision map to fuse the source images by Coefficient significance method. In the area of multifocus image fusion, the better decision map is very important the fusion results. In the processing of distinguishing the well-focus part with blur part in an image, the edge between the parts is more difficult to be processed. Coefficient significance is very effective in generating the better decision map to fuse the multifocus images. Design/methodology/approach The energy of Laplacian is used in the approximation coefficients of redundant discrete wavelet transform. On the other side, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient. Findings Due to the shift-variance of the redundant discrete wavelet and the effectiveness of fusion rule, the presented fusion method is superior to the region energy in harmonic cosine wavelet domain, pixel significance with the cross bilateral filter and multiscale geometry analysis method of Ripplet transform. Originality/value In redundant discrete wavelet domain, the coefficient significance based on statistic property of covariance is proposed to merge the detail coefficient of source images.

scholarly journals Image enhancement in wavelet domain based on histogram equalization and median filter

2021 ◽  
Author(s):  
Haval Sulaiman Abdullah ◽  
◽  
Firas Mahmood Mustafa ◽  
Atilla Elci ◽  
◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Noise Reduction ◽  
Discrete Wavelet Transform ◽  
Image Enhancement ◽  
Median Filter ◽  
Histogram Equalization ◽  
Image Noise ◽  
Discrete Wavelet ◽  
Wavelet Domain ◽  
Image Noise Reduction

During the acquisition of a new digital image, noise may be introduced as a result of the production process. Image enhancement is used to alleviate problems caused by noise. In this work, the purpose is to propose, apply, and evaluate enhancement approaches to images by selecting suitable filters to produce improved quality and performance results. The new method proposed for image noise reduction as an enhancement process employs threshold and histogram equalization implemented in the wavelet domain. Different types of wavelet filters were tested to obtain the best results for the image noise reduction process. Also, the effect of canceling one or more of the high-frequency bands in the wavelet domain was tested. The mean square error and peak signal to noise ratio are used for measuring the improvement in image noise reduction. A comparison made with two related works shows the superiority of the methods proposed and implemented in this research. The proposed methods of applying the median filter before and after the histogram equalization methods produce improvement in performance and efficiency compared to the case of using discrete wavelet transform only, even with the cases of multiresolution discrete wavelet transform and the cancellation step.

scholarly journals Performance Analysis of Iris Recognition using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

2019 ◽  
Vol 8 (6) ◽  
pp. 1-10
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Iris Recognition ◽  
Image Features ◽  
Discrete Wavelet ◽  
Multi Stage ◽  
Human Iris ◽  
The Individual ◽  
Matching Score ◽  
Bicubic Interpolation

Biometric identification is highly reliable for human identification. Biometric is a field of science used for analyzing the physiological or behavioural characteristics of human. Iris features are unique, stable and can be visible from longer distances. It uses mathematical pattern-recognition techniques on video images of one or both iris of an individual's. Compared to other biometric traits, iris is more challenging and highly secured tool to identify the individual. In this paper iris recognition based on the combination of Discrete Wavelet Transform (DWT), Inverse Discrete Wavelet Transform (IDWT), Independent Component Analysis (ICA) and Binariezed Statistical Image Features (BSIF) are adopted to generate the hybrid iris features. The first level and second level DWT are employed in order to extract the more unique features of the iris images. The concept of bicubic interpolation is applied on high frequency coefficients generated by first level decomposition of DWT to produce new set of sub-bands. The approximation band generated by second level decomposition of DWT and new set of sub-bands produced by second level decomposition of DWT are applied on IDWT to reconstruct the coefficients. The ICA 5x5 filters and BSIF are adopted for selecting the appropriate images to extract the final features. Finally based on the matching score between the database image and test image the genuine and imposters are identified. Using CASIA database, training and testing of the features is performed and performance is evaluated considering different combinations of Person inside Database (PID) and Person outside Database (POD).

scholarly journals Computer Graphic and Photographic Image Classification using Local Image Descriptors

2017 ◽  
Vol 67 (6) ◽  
pp. 654 ◽  
Author(s):  
Gajanan K Birajdar ◽  
Vijay H Mankar
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Computer Graphic ◽  
Image Features ◽  
Feature Subset Selection ◽  
Support Vector ◽  
Natural Image ◽  
Discrete Wavelet ◽  
Feature Subset ◽  
Graphic Images

<p class="p1">With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy.</p>

scholarly journals Spatial downscaling of radar-derived rainfall field by two-dimensional wavelet transform

2020 ◽  
Vol 51 (3) ◽  
pp. 456-469
Author(s):  
Vahid Nourani ◽  
Armin Farshbaf ◽  
S. Adarsh
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet ◽  
Main Concern ◽  
Two Dimensional ◽  
Wavelet Domain ◽  
Global Circulation ◽  
Global Circulation Models ◽  
Pattern Display ◽  
Actual Size ◽  
Good Agreement

Abstract Downscaling of rainfall fields, either as images or products of global circulation models, have been the motive of many hydrologists and hydro-meteorologists. The main concern in downscaling is to transform high-resolution properties of the rainfall field to lower resolution without introducing erroneous information. In this paper, rainfall fields obtained from Next Generation Weather Surveillance Radar (NEXRAD) Level III were examined in the wavelet domain which revealed sparsity for wavelet coefficients. The proposed methodology in this work employs a concept named Standardized Rainfall Fluctuation (SRF) to overcome the sparsity of rainfall fields in wavelet domain which also exhibited scaling behaviors in a range of scales. SRFs utilizes such scaling behaviors where upscaled versions of the rainfall fields are downscaled to their actual size, using a two-dimensional discrete wavelet transform, to examine the reproduction of the rainfall fields. Furthermore, model modifications were employed to enhance the accuracy. These modifications include removing the negative values while conserving the mean and applying a non-overlapping kernel to restore high-gradient clusters of rainfall fields. The calculated correlation coefficient, statistical moments, determination coefficient and spatial pattern display a good agreement between the outputs of the downscaling method and the observed rainfall fields.

An Improved Image Segmentation Method Based Onmulti-Resolution Analysis and Watershed Transformation

2012 ◽  
Vol 500 ◽  
pp. 709-715
Author(s):  
Yan Wang ◽  
Yan Ma
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Input Image ◽  
Segmentation Method ◽  
Watershed Segmentation ◽  
Watershed Transformation ◽  
Inverse Transform ◽  
Multi Resolution Analysis ◽  
Segmentation Accuracy ◽  
Resolution Analysis

This paper presents an improved image segmentation method based on multi-resolution analysis of wavelet transform and watershed transformation. In the marked-controlled watershed segmentation, we not only enhance the contours of low-resolution input image to acquire segmentation function image, but also use minima imposition technology to apply filters to input image to acquire marked function image. In order to improve segmentation accuracy, we use regional fusion and coarse-fine segmentation in wavelet inverse transform. The experimental results show that the proposed image segmentation method can efficiently reduce over-segmentation, as well as improve the effect of image segmentation. In addition, the proposed method is robust.

The Research of Semi-Fragile Watermarking Algorithm Based on Dynamic Segmentation

2010 ◽  
Vol 121-122 ◽  
pp. 1044-1047
Author(s):  
Sheng Bing Che ◽  
Xu Shu
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fragile Watermarking ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Segmentation Method ◽  
Human Eye ◽  
Dynamic Segmentation ◽  
Pixel Value ◽  
Visual Characteristics

Based on human eye visual characteristics of brightness and texture, a new region segmentation method and quantitative formulas in transform domain were put forward. By dynamic quantization, the transparency of carrier images and the anti-attack capability of watermarking images were improved. And a new kind of adjustment operator was brought up, which adjusted the pixel value after inverse discrete wavelet transform.

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