Shape Matching Optimization via Atomic Potential Function and Artificial Bee Colony Algorithms with Various Search Strategies

Bai, Li; Hu, Mandong; Zhou, Changjun

doi:10.1109/iscid.2015.252

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…Given a similarity measurement, a search process is then implemented for a geometric transformation with satisfactory similarity. In this section, the APM model is systematically described as follows (Li, 2016;Li et al, , 2015.…”

Section: Variable Neighbourhood Search Schemementioning

confidence: 99%

“…Applications of visual shape matching span areas including face recognition (Esmaili et al, 2015), automatic drive (Dickmanns et al, 1990), unmanned aerial vehicle navigation (Temel & Unaldi, 2014), human motion detection (Chaquet et al, 2013) and medical image analysis (Yang et al, 2015). Existing shape matching methods can be divided into three types in general: pixel-based models, low-level-feature models and high-level-feature models (Li, 2016;Li et al, , 2015. Here, low-level shape features refer to extracting features such as edges, corners, and contours from the original image.…”

mentioning

confidence: 99%

“…On the other hand, the contours in the test image are extracted and also regarded as another set of atoms B. Through geometric transformation, the configuration that yields the potential field A attracts atoms B best indicates an ideal matching between the template shape and the best shape (Li et al, 2015). Compared to the traditional edge potential function (EPF) model, APM is less sensitive to complex backgrounds in the test images and lower cost in the computation process .…”

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confidence: 99%

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Golden sine cosine SALP swarm algorithm for shape matching using atomic potential function

Xiang

Zhou

et al. 2021

Expert Systems

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Salp swarm algorithm (SSA) is one of the efficient recent meta-heuristic optimization algorithms, where it has been successfully utilized in a wide range of optimization problems in different fields. In the research process, it is found that it is very difficult to maintain the balance between the exploration and exploitation capabilities of a certain algorithm. Therefore, one of the main purposes of this article is to provide an algorithm that can intelligently balance between exploration and exploitation, so that it can balance exploration and exploitation capabilities. Later, in the research process, it was found that the sine and cosine function and the salp foraging trajectory have a high mathematical similarity, which greatly improves the optimization ability of the algorithm. In addition, the variable neighbourhood strategy can appropriately expand the optimization range of the algorithm. So in this paper, a novel golden sine cosine salp swarm algorithm with variable neighbourhood search scheme (GSCSSA-VNS) is proposed, the another objective of proposing this algorithm is as a new optimization method for shape matching. As a relatively new branch, atomic potential matching (APM) model is inspired by potential field attractions. Compared to the conventional edge potential function (EPF) model, APM has been verified to be less sensitive to intricate backgrounds in the test image and far more cost effective in the computation process. Experimental results of four realistic examples show that GSCSSA-VNS is able to provide very competitive results and outperforms the other algorithms.

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Section: Variable Neighbourhood Search Schemementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Golden sine cosine SALP swarm algorithm for shape matching using atomic potential function

Xiang

Zhou

et al. 2021

Expert Systems

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“…Potential based methods originated from the edge potential function (EPF) model [16]. EPF based method has been applied to many real world problems, e.g., ground target detection [17,18], ear detection [19], aperture radar scene matching [20] and video retrieval [21,22].Also, Atomic Potential Matching (APM) model provides a remedy for the conventional EPF model [23].…”

Section: Related Wokmentioning

confidence: 99%

New Automatic Target Recognition Approach based on Hough Transform and Mutual Information

Bahy¹

2018

IJIGSP

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This paper presents a new automatic target recognition approach based on Hough transform and mutual information. The Hough transform groups the extracted edge points in edged images to an appropriate set of lines which helps in features extraction and matching processes in both of target and stored database images. This gives an initial indication about realization and recognition between target image and its corresponding database image. Mutual information is used to emphasize the recognition of the target image and its verification with its corresponding database image. The proposed recognition approach passed through five stages which are: edge detection by Sobel edge detector, thinning as a morphological operation, Hough transformation, matching process and finally measuring the mutual information between target and the available database images. The experimental results proved that, the target recognition is realized and gives more accurate and successful recognition rate than other recent recognition techniques which are based on stable edge weighted HOG.

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Using flower pollination algorithm and atomic potential function for shape matching

Zhou

Zhang

Luo

et al. 2016

Neural Comput & Applic

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Shape Matching Optimization via Atomic Potential Function and Artificial Bee Colony Algorithms with Various Search Strategies

Cited by 3 publications

References 23 publications

Golden sine cosine SALP swarm algorithm for shape matching using atomic potential function

Golden sine cosine SALP swarm algorithm for shape matching using atomic potential function

New Automatic Target Recognition Approach based on Hough Transform and Mutual Information

Using flower pollination algorithm and atomic potential function for shape matching

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