Automatic Image Segmentation using Wavelets

—A new color image segmentation approach based on OWT is presented in this work. OWT extracts wavelet features which give a good separation of different patterns. Moreover the proposed algorithm uses morphological operators for effective segmentation. From the qualitative and quantitative results, it is concluded that our proposed method has improved segmentation quality and it is reliable, fast and can be used with reduced computational complexity than direct applications of Histogram Clustering. The main advantage of this method is the use of single parameter and also very faster. While comparing with five color spaces, segmentation scheme produces results noticeably better in RGB color space compared to all other color spaces.

Pattern Recognition Letters, 2003

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. This paper describes a novel technique of feature extraction for characterization and segmentation of texture at multiple scales based on block by block comparison of wavelet co-occurrence features. The performance of this segmentation algorithm is superior to traditional single resolution techniques such as texture spectrum, co-occurrences, local linear transforms, etc. The results of the proposed algorithm are found to be satisfactory.

2004

We have recently demonstrated that fully unsupervised segmentations of still images and 2D+T sequences is possible by Bayesian methods, on the basis of a Hidden Markovian Model (HMM) and a Potts-Markov Random Field (PMRF), in the pixel domain. The use of a high number of iterations to reach convergence in a segmentation where the number of segments, or "classes" labels, is important makes the algorithm rather slow for the processing of a large quantity of data like in image sequences. We more recently have worked out a new version of this algorithm by first operating our segmentation in the wavelet transform domain rather than in the direct domain. Doing so, we take advantage of the local decay property, or "peaky" distribution of the wavelet coefficients, in an orthogonal decomposition. This decomposition is a fast pyramidal, O(N 2 ), decomposition, so the Bayesian segmentation is performed only once on the first coarse image then on all subbands up to the highest resolution level. Moreover, we have improved our Potts-Markov model in order to take into account the three main orientations of the wavelets band-pass, or so-called, detail, subbands. The main advantage of such an algorithm, in comparison with the direct domain Bayesian segmentation, is that the high frequency coefficients, i.e. the coefficients of all sub-bands except the coarsest, are segmented in only 2 classes : 1 for the weak energy coefficients and 2 for the few, and most representative, high energy coefficients, thus enabling to speed up the convergence process of the segmentation.

This paper describes a new fully unsupervised image segmentation method based on a Bayesian approach and a Potts-Markov Random Field (PMRF) model that are performed in the wavelet domain. A Bayesian segmentation model, based on a PMRF in the direct domain, has already been successfully developed and tested in . This model performs a fully unsupervised segmentation, on images composed of homogeneous regions, by introducing a hidden Markov Model (HMM) for the regions to be classified and Gaussian distributions for the noise and for the pixels pertaining to each regions. The computation of the posterior laws, deduced from these a priori distributions for the pixels, is done by a Markov Chain Monte Carlo (MCMC) approach and uses a Gibbs sampling algorithm. The use of a high number of iterations to reach convergence in a segmentation, where the number of segments, or "classes" labels, is important, makes the algorithm rather slow for the processing of a large quantity of data like image sequences . To overcome this problem we have taken advantage of the property of the wavelet coefficients, in an orthogonal decomposition, to be modeled by a mixture of two Gaussians. Thus, by projecting an observable noisy image in the wavelet domain, we are able to segment, in this same domain, the wavelet subbands in only two classes.

SPIE Proceedings, 2016

Aim to that Neutrosophic C-mean clustering segmentation does not consider the membership distribution of every sample point to different classes. Herein, an image-segmentation algorithm based on wavelet and data-driven neutrosophic fuzzy clustering is proposed. When the maximum membership value of a sample point is far greater than other membership values, the centre of the class with the maximum membership value is taken as the centre of the fuzzy class. Otherwise, the average value of the centre of the two classes with the highest and second-highest membership values is used as the centre of the fuzzy class. In the preprocessing stage, wavelet technology is used to remove noise from the processed image, and the improved Bayesian algorithm is employed to calculate the filter threshold. The experiment results for synthetic and natural images show that the proposed method is more accurate and effective than the existing methods.

One of the difficulties in pattern recognition is to develop a good evaluation of the classes present on a scene. To suitably describe those classes it is necessary to find feature spaces, which allow distinguishing between them. In this work, we propose an unsupervised segmentation/classification technique based on wavelet textural analysis and self-organizing maps clustering.

Lecture Notes in Computer Science, 2011

We propose an automated image segmentation algorithm for segmenting multisensor images, in which the texture features are extracted based on the wavelet transform and modeled by generalized Gaussian distribution (GGD). First, the image is roughly segmented into textured and non-textured regions in the dual-tree complex wavelet transform (DT-CWT) domain. A multiscale segmentation is then applied to the resulting regions according to the local texture characteristics. Finally, a novel statistical region merging algorithm is introduced by measuring a Kullback-Leibler distance (KLD) between estimated GGD models for the neighboring segments. Experiments demonstrate that our algorithm achieves superior segmentation results.

2010

Hierarchical Texture Segmentation using Wavelet packet decomposition performs an unsupervised classification of Texture features, extracted using wavelet packet decomposition to generate a segmented image. Recursive decomposition of both the approximation and the detail coefficients derived from the original signal provides a wider spectrum for richer feature extraction. This Texture Segmentation works well for Texture images with only two Textures. This approach can be used for shape extraction, detecting industrial defects, and for text extraction.

In the past decade, remarkable progress has been made towards the multimedia data retrieval; however there is a challenging need for efficient and realistic image retrieval model. One such approach for image retrieval is content based technique. In this pilot study, we have proposed a new algorithm for digital image indexing and retrieval using wavelet based block processing approach. Original image has been converted into HSV co-ordinate and it has been divided into 4x4 pixel block. A block clustering algorithm is applied for image segmentation using K-means algorithm. Feature parameters were extracted from each cluster and have been stored for future use. At the time of image query, the feature vectors of the query image were collected and Euclidean distance between each region is computed. The result has been sorted in descending order based on vector value. Results demonstrate that, our image retrieval system improves the accuracy and robustness. In future, same technique could be used for automatic diagnosing of medical images and also for health care sector; for patient database maintenance.

2019

Model-Based picture segmentation plays a dominant function in picture evaluation and picture retrieval. To analyze the features of the picture, model based segmentation set of rules could be greater efficient as compared to non-parametric techniques. In this paper, we proposed Automatic Pixel depth Image Segmentation using Wavelets (AISWT) to make segmentation rapid and simpler. The approximation band of photo Discrete Wavelet Transform is considered for segmentation which incorporates vast records of the input image. The Histogram based set of rules is used to achieve the number of regions and the initial parameters like imply, variance and mixing component. The final parameters are received via the use of the Expectation and Maximization algorithm. The segmentation of the approximation coefficients is decided by means of Maximum Likelihood function. It is found that the proposed approach is computationally green permitting the segmentation of huge photos and plays a whole lot adva...