scholarly journals Comparative Study on Thresholding

2011 ◽  
pp. 73-77
Author(s):  
K.C. Singh ◽  
Lalit Mohan Satapathy ◽  
Bibhudatta Dash ◽  
S.K. Routray
Keyword(s):  
Computational Complexity ◽  
Comparative Study ◽  
Hierarchical Cluster ◽  
Multilevel Thresholding ◽  
Clustering Method ◽  
Good Separation ◽  
Robust Estimate ◽  
Clear Separation ◽  
Cluster Organization ◽  
Multi Level

Criterion based thresholding algorithms are simple and effective for two-level thresholding. However, if a multilevel thresholding is needed, the computational complexity will exponentially increase and the performance may become unreliable. In this approach, a novel and more effective method is used for multilevel thresholding by taking hierarchical cluster organization into account. Developing a dendogram of gray levels in the histogram of an image, based on the similarity measure which involves the inter-class variance of the clusters to be merged and the intra-class variance of the new merged cluster . The bottom-up generation of clusters employing a dendogram by the proposed method yields good separation of the clusters and obtains a robust estimate of the threshold. Such cluster organization will yield a clear separation between object and background even for the case of nearly unimodal or multimodal histogram. Since the hierarchical clustering method performs an iterative merging operation, it is extended to multilevel thresholding problem by eliminating grouping of clusters when the pixel values are obtained from the expected numbers of clusters. This paper gives a comparison on Otsu’s & Kwon’s criterion with hierarchical based multi-level thresholding.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2017 ◽  
Vol 8 (4) ◽  
pp. 58-83 ◽  
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image ◽  
Better Than

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

Nearby Phase Search Algorithm with Reduced Complexity for Higher Order QAM Formats

2013 ◽  
Vol 760-762 ◽  
pp. 1869-1873
Author(s):  
Li Min Xia ◽  
Xian Zhou ◽  
Dong Yan ◽  
Na Na Zhang ◽  
Xiao Yun Wu
Keyword(s):  
Computational Complexity ◽  
Bit Error Rate ◽  
High Speed ◽  
Optical Transmission ◽  
Search Algorithm ◽  
Estimation Algorithm ◽  
Coherent Receivers ◽  
Multi Level ◽  
Reduced Complexity ◽  
Laser Line Width

This paper proposes a nearby phase search (NPS) algorithm based on BPS estimation algorithm in optical coherent receivers. And its suitable for arbitrary multi-level modulation. Making use of the continuity of phase change, the proposed NPS algorithm is applied to process nearby symbols by taking the pre-estimation phase of each symbol block as reference point. Compared to the traditional blind phase search (BPS) algorithm and its improved two-stage BPS algorithm, the performance of the proposed NPS algorithm is greatly improved in ultra-high speed coherent optical transmission system. By the simulation, the effectiveness and feasibility of the proposed algorithm are demonstrated in 28GBaud 16-QAM and 64-QAM system. Its shown that the computational complexity of the NPS algorithm greatly reduces in the guarantee of laser line width tolerance and bit error rate.

A Study on Darwinian Crow Search Algorithm for Multilevel Thresholding

2021 ◽  
pp. 2250012
Author(s):  
Ehsan Ehsaeyan ◽  
Alireza Zolghadrasli
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Search Space ◽  
Energy Curve ◽  
Local Optimum ◽  
Multilevel Thresholding ◽  
Curve Method ◽  
Heuristic Search Methods ◽  
Multi Level ◽  
Computationally Expensive

Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently.

Moth-Flame Optimization Algorithm Based Multilevel Thresholding for Image Segmentation

2018 ◽  
pp. 771-797
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Kapur’S Entropy ◽  
Moth Flame Optimization Algorithm ◽  
Popular Image

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

scholarly journals NOISE-BASED LOGIC: WHY NOISE? A COMPARATIVE STUDY OF THE NECESSITY OF RANDOMNESS OUT OF ORTHOGONALITY

2012 ◽  
Vol 11 (04) ◽  
pp. 1250021 ◽  
Author(s):  
HE WEN ◽  
LASZLO B. KISH
Keyword(s):  
Computational Complexity ◽  
Comparative Study ◽  
Power Dissipation ◽  
Time Complexity ◽  
Logic Systems ◽  
Sinusoidal Signals ◽  
Better Than

Although noise-based logic shows potential advantages of reduced power dissipation and the ability of large parallel operations with low hardware and time complexity the question still persist: Is randomness really needed out of orthogonality? In this Letter, after some general thermodynamical considerations, we show relevant examples where we compare the computational complexity of logic systems based on orthogonal noise and sinusoidal signals, respectively. The conclusion is that in certain special-purpose applications noise-based logic is exponentially better than its sinusoidal version: Its computational complexity can be exponentially smaller to perform the same task.

MULTILEVEL THRESHOLDING METHOD BASED ON NORMALIZED CUT

2009 ◽  
Vol 09 (04) ◽  
pp. 531-540
Author(s):  
LIYING ZHENG ◽  
KUIFENG LIU ◽  
LEI YU
Keyword(s):  
Wavelet Transform ◽  
Computational Complexity ◽  
Iterative Scheme ◽  
Undirected Graph ◽  
Multilevel Thresholding ◽  
Normalized Cut ◽  
Low Computational Complexity ◽  
Thresholding Method ◽  
Optimal Thresholds ◽  
Refinement Procedure

Based on valleys of the histogram of an image as well as the normalized cut (Ncut) partitioning method, a novel multilevel thresholding technique with low computational complexity has been developed in this paper. A wavelet transform is first adopted to reduce the length of the original histogram, and to smoothen the histogram. All the valleys of the reduced histogram are then regarded as candida te thresholds based on which a weighted undirected graph is constructed. An Ncut criteria and a refinement procedure are finally adopted to determine the optimal thresholds. A comparison between the proposed multithresholding method and the methods based on GA, exhaustive search or iterative scheme has been done on benchmark images showing that our method outperforms other multilevel thresholding techniques in terms of computational complexity.

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