Multi-level image thresholding using Otsu and chaotic bat algorithm

Satapathy, Suresh Chandra; Raja, N. Sri Madhava; Rajinikanth, V.; Ashour, Amira S.; Dey, Nilanjan

doi:10.1007/s00521-016-2645-5

Cited by 172 publications

(63 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Firstly, public dataset for segmentation and widely used images are evaluated separately using the proposed algorithm in this paper to segment each image into two, three, four, and five levels of threshold. And at the same time, quantitative methods are used to demonstrate the advantages of the proposed algorithm through comparing the Information Entropy (IE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) of images with other widely used algorithms such as BA (Bat Algorithm) [35], IBA (Improved Bat Algorithm) [39], MMSA (Meta-heuristic Moth Swarm Algorithm) [40], and OTUS [41] algorithms. Finally, the time advantage of the algorithm is confirmed via analyzing the time complexity of the algorithm.…”

Section: Methodsmentioning

confidence: 99%

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

Wang

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.

show abstract

Section: Methodsmentioning

confidence: 99%

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

Wang

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…This paper not only considers the samples of gestures in three environments, but also uses two features to describe gesture recognition from the global and local aspects, the HOG feature successfully solves the influence of illumination variation, scale size and small angle rotation on the recognition process, and combines the invariance of the rotation of the Hu moment itself, which improves the overall recognition rate of the gesture picture. Commonly used classification methods are BP neural network, K nearest neighbor [49][50], these methods were tested under the three conditions of this paper, and the recognition rates of various methods as shown in Figure 13 under different conditions were obtained. It can be seen that the method of this paper has a good recognition effect under various conditions.…”

Section: Fig12 Recognition Rate On Different Featuresmentioning

confidence: 99%

Research on gesture recognition of smart data fusion features in the IoT

Tan

Sun

et al. 2019

Neural Comput & Applic

View full text Add to dashboard Cite

With the rapid development of Internet of Things (IoT) technology, the interaction between people and things has become increasingly frequent. Use simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information, in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed, by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method.

show abstract

“…Many multilevel thresholding methods have been developed from these kinds of algorithms, and almost every new heuristic is used in multilevel thresholding not long after it is proposed as Human Mental Search (HMS) [13][14][15]. These optimization methods combined with Otsu's and Kapur's segmentation index are widely used in multithresholding segmentation [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Among the heuristic methods of multilevel thresholding, many are based on particle swam optimization (PSO) algorithms for its simple and practical features as well as computational efficiency [11,17,21,22]. PSO algorithm has 2 Mathematical Problems in Engineering been proven to be a powerful competitor to other heuristic algorithms in such optimization application as multilevel thresholding [23] and many enhanced PSO-based multilevel thresholding methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization

Qin

Wang

Qin

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Multilevel thresholding is to find the thresholds to segment the image with grey levels. Usually, the thresholds are so determined that some indicator functions of the segmented image are optimized. To improve the computational efficiency, we presented an optimization method for multilevel thresholding. First, the solution space is divided into subspaces. Second, the subspaces are searched to obtain their current local optimal value. Third, the subspaces that are of worse current optimal value are eliminated. Then, the next round of elimination is exerted in the remainder. The elimination is repeated until only one subspace is left and its optimal value is taken as the global optimum. In principle, any random search algorithm can be used to find the local optimum in a subspace block because it is a strategy to enhance the searching efficiency through eliminating hopeless regions as early as possible, rather than to improve the searching algorithm itself. To verify its performance, taking PSO (Particle swarm optimization) as the basic searching algorithm of subspaces, the presented method is applied to Otsu’s and Kapur’s multilevel thresholding of four different kinds of digital images. The presented method is compared with PSO, and it behaves better in efficiency.

show abstract

Multi-level image thresholding using Otsu and chaotic bat algorithm

Cited by 172 publications

References 42 publications

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

Research on gesture recognition of smart data fusion features in the IoT

A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization

Contact Info

Product

Resources

About