Efficient image segmentation through 2D histograms and an improved owl search algorithm

Río, Andrea H. del; Aranguren, Itzel; Oliva, Diego; Elaziz, Mohamed E. Abd; Cuevas, Erik

doi:10.1007/s13042-020-01161-z

Cited by 9 publications

(7 citation statements)

References 61 publications

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“…The new flow positions are estimated in two ways in the FDA. Firstly, it is to assume that a flow generates its β neighbor flow Neighbor_X (ref (3) in [18]) on its route to the drainage basin, and then, updates flow locations Flow_newX (ref (8) in [18]) based on the best neighbor flow. The flow positions are updated Flow_newX (ref ( 9) in [18]) in a second way by presuming that the present flow encounters any random flow, and changed its path.…”

Section: B Kapur's Entropy Thresholdingmentioning

confidence: 99%

“…It is studied from the literature that the multilevel thresholding (MTH) is the easiest way of achieving multiclass segmented outputs. In this context, many methodologies are reported [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]. Most of these methods use the image histogram-based entropy values.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the recent techniques [5], [6], [7], [8], [9], [10], [11], [12] are, more or less, concentrated on the use of the entropic values. The authors in [5] have presented a MTH method based on 2D histogram.…”

Section: Introductionmentioning

confidence: 99%

“…However, the method was similar to the earlier approaches. The authors in [8], [9], and [10] used the 2D Rényi entropy as an objective function for solving the MTH problem. However a 2D histogram is constructed using the lateral inhibition in [10].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the accuracy value is lessened. In addition, the local averaging adopted while constructing the 2D histogram in the above methods [5], [6], [7], [8], [9], [10], [11], [12] causes loss of the edge information, leading to less accuracy. This is the reason why the authors are motivated to suggest a novel decisive kind of entropy based objective function, coined as practical decisive row class entropy (PDRCE).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Novel Practical Decisive Row-Class Entropy-Based Technique for Multilevel Threshold Selection Using Opposition Flow Directional Algorithm

Panda

Swain²,

Naik

et al. 2022

IEEE Access

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One of today's inspiring issues is the 2D histogram-based multilevel threshold selection which is used for segmenting images into several regions. The image analysis warrants exploration of multiclass thresholding techniques using various entropy-based objective functions. In this context, the Shannon type of entropic function without inherent decision making capacity has been widely used for threshold selection in the last decade. Furthermore, a 2D histogram was constructed using local average intensity values resulting in loss of some edge information. To address these problems, this study proposes a new methodology using a novel practical decisive row-class entropy (PDRCE) based fitness function for multilevel thresholding. The PDRCE values are computed using the newly constructed 2D histogram-based on normal local variance. Further, an opposition flow directional algorithm (OFDA) is proposed to maximize the fitness function. The performance of the proposed technique is compared with five state-of-the-art 2D histogram-based entropic fitness functions. Moreover, the performance of OFDA is investigated through comparison with other global optimizers namely the genetic algorithm, particle swarm optimization and artificial bee colony. An image segmentation evaluation dataset (BSDS500) is used in this experiment. It is witnessed that the proposal is more efficient than state-of-the-art methods. Our fitness function would be useful for registration, segmentation, fusion, etc.

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