Vanishing point detection and line classification with BPSO

Han, Lei; Huang, Chenrong; Zheng, Shengnan; Zhang, Zhen; Xu, Lizhong

doi:10.1007/s11760-016-0883-8

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…They can also be used for scheduling a realistic workflow on virtual machines (VMs) in the cloud. Examples of these categories are standard PSO [38][39][40][41], jumping PSO [42], learning PSO [41], bi-objective PSO [43,44], modified PSO [45][46][47][48][49][50][51][52], binary PSO and its variants [16,[53][54][55][56][57], and hybrid PSO [58]. Figure 4 below shows the classification of PSO-based algorithms in cloud computing.…”

Section: Pso-based Schedulingmentioning

confidence: 99%

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A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

et al. 2020

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Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified.

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Section: Pso-based Schedulingmentioning

confidence: 99%

“…The sigmoid transfer function was used to convert the velocity value from continuous to binary. BPSO has been used to solve diverse discrete optimization problems [53][54][55]. It has good convergence ability but it is affected by a lack of diversity in its premature convergence.…”

Section: Binary Pso (Bpso)mentioning

confidence: 99%

A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

et al. 2020

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“…The deviation function aims to quantify the deviation that exists between a VP

v p_{i, j} \in X_{i}

and a line segment

ξ_{k} \in E

. To this end, as propose in [25], our algorithm considers the orientation error, this is also consistently with the measuring method proposed in [27, 35]. Thereby, the deviation

d false(ξ_{k}, v p_{i, j} false)

is computed with the absolute value of the sine of the angle formed by the line

l_{k}

that passes through the segment

ξ_{k}

, and the line

{\hat{l}}_{k}

that connects

v p_{i, j}

with the centre of

l_{k}

.…”

Section: Tlbo Algorithm For Vanishing Point Detectionmentioning

confidence: 99%

“…Metaheuristics have delivered better solutions in comparison with traditional methods. Regarding vanishing point estimation, VP detectors have been proposed using an artificial bee colony (ABC) [25], the particle swarm optimisation (PSO) [26], the binary PSO (BPSO) [27], and the harmony search (HS) algorithm [28]. However, when using these metaheuristics, the need of many parameters tuning remains.…”

Section: Introductionmentioning

confidence: 99%

Vanishing point detection using the teaching learning‐based optimisation algorithm

Lopez-Martinez

Cuevas

2020

IET image process

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Within the computer vision field, estimating image vanishing points has many applications regarding robotic navigation, camera calibration, image understanding, visual measurement, 3D reconstruction, among others. Different methods for detecting vanishing points relies on accumulator space techniques, while others employ a heuristic approach such as RANSAC. Nevertheless, these types of methods suffer from low accuracy or high computational cost. To explore a different technique, this paper focuses on improving the efficiency of the metaheuristic search for vanishing points by using a recently proposed population‐based method: The Teaching Learning Based Optimisation algorithm (TLBO). The TLBO algorithm is a metaheuristic technique inspired by the teaching–learning process. In our method, the TLBO algorithm is used after a line segment detection, to cluster line segments according to their more optimal vanishing point. Thus, our algorithm detects both orthogonal and nonorthogonal vanishing points in real images. To corroborate the performance of our proposed algorithm, different comparison and tests with other approaches were carried out. The results validate the accuracy and efficiency of our proposed method. Our approach had an average computational time of1.42 seconds and obtained a cumulative focal length error of 1 pixel, and cumulative angular error of 0.1°.

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A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO

Islam

Mei

2017

Applied Soft Computing

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Vanishing point detection and line classification with BPSO

Cited by 5 publications

References 19 publications

A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

Vanishing point detection using the teaching learning‐based optimisation algorithm

A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO

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