scholarly journals Ptf1a control of Dll1 reveals an alternative to the lateral inhibition mechanism

Development ◽  
2012 ◽  
Vol 139 (23) ◽  
pp. 4492-4492 ◽  
Author(s):  
J. Ahnfelt-Ronne ◽  
M. C. Jorgensen ◽  
R. Klinck ◽  
J. N. Jensen ◽  
E.-M. Fuchtbauer ◽  
...  
Keyword(s):  
Lateral Inhibition ◽  
Inhibition Mechanism

Whale optimization algorithm based on lateral inhibition for image matching and vision-guided AUV docking

2020 ◽  
pp. 1-12
Author(s):  
Zheping Yan ◽  
Jinzhong Zhang ◽  
Jialing Tang
Keyword(s):  
Lateral Inhibition ◽  
Optimization Algorithm ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Optimal Solution ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Inhibition Mechanism ◽  
Whale Optimization ◽  
Accuracy And Stability

The accuracy and stability of relative pose estimation of an autonomous underwater vehicle (AUV) and a target depend on whether the characteristics of the underwater image can be accurately and quickly extracted. In this paper, a whale optimization algorithm (WOA) based on lateral inhibition (LI) is proposed to solve the image matching and vision-guided AUV docking problem. The proposed method is named the LI-WOA. The WOA is motivated by the behavior of humpback whales, and it mainly imitates encircling prey, bubble-net attacking and searching for prey to obtain the globally optimal solution in the search space. The WOA not only balances exploration and exploitation but also has a faster convergence speed, higher calculation accuracy and stronger robustness than other approaches. The lateral inhibition mechanism can effectively perform image enhancement and image edge extraction to improve the accuracy and stability of image matching. The LI-WOA combines the optimization efficiency of the WOA and the matching accuracy of the LI mechanism to improve convergence accuracy and the correct matching rate. To verify its effectiveness and feasibility, the WOA is compared with other algorithms by maximizing the similarity between the original image and the template image. The experimental results show that the LI-WOA has a better average value, a higher correct rate, less execution time and stronger robustness than other algorithms. The LI-WOA is an effective and stable method for solving the image matching and vision-guided AUV docking problem.

scholarly journals Image Retrieval Using the Intensity Variation Descriptor

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Zhao Wei ◽  
Guang-Hai Liu
Keyword(s):  
Image Retrieval ◽  
Information Extraction ◽  
Lateral Inhibition ◽  
Intensity Variation ◽  
Spatial Layout ◽  
Inhibition Mechanism ◽  
Square Root ◽  
Optimal Direction ◽  
Selective Mechanism ◽  
Better Than

Variations between image pixel characteristics contain a wealth of information. Extraction of such cues can be used to describe image content. In this paper, we propose a novel descriptor, called the intensity variation descriptor (IVD), to represent variations in colour, edges, and intensity and apply it to image retrieval. The highlights of the proposed method are as follows. (1) The IVD combines the advantages of the HSV and RGB colour spaces. (2) It can simulate the lateral inhibition mechanism and orientation-selective mechanism to determine an optimal direction and spatial layout. (3) An extended weighted L1 distance metric is proposed to calculate the similarity of images. It does not require complex operations such as square or square root and leads to good performance. Comparative experiments on two Corel datasets containing 15,000 images show that the proposed method performs better than the SoC-GMM, CPV-THF, and STH methods and provides good matching of texture, colour, and shape.

scholarly journals Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition

2017 ◽  
Vol 2017 ◽  
pp. 1-14
Author(s):  
Qifang Luo ◽  
Sen Zhang ◽  
Yongquan Zhou
Keyword(s):  
Lateral Inhibition ◽  
Template Matching ◽  
Search Algorithm ◽  
Inhibition Mechanism ◽  
Edge Extraction ◽  
Matching Problems ◽  
Matching Technique ◽  
Image Edge ◽  
Stochastic Fractal Search Algorithm ◽  
Stochastic Fractal Search

Template matching is a basic and crucial process for image processing. In this paper, a hybrid method of stochastic fractal search (SFS) and lateral inhibition (LI) is proposed to solve complicated template matching problems. The proposed template matching technique is called LI-SFS. SFS is a new metaheuristic algorithm inspired by random fractals. Furthermore, lateral inhibition mechanism has been verified to have good effects on image edge extraction and image enhancement. In this work, lateral inhibition is employed for image preprocessing. LI-SFS takes both the advantages of SFS and lateral inhibition which leads to better performance. Our simulation results show that LI-SFS is more effective and robust for this template matching mission than other algorithms based on LI.

THE EXCITATION DISCHARGE RHYTHM OF SMALL-WORLD BIOLOGICAL NEURAL NETWORKS WITH LATERAL INHIBITION MECHANISM

2012 ◽  
Vol 26 (01) ◽  
pp. 1150001 ◽  
Author(s):  
RENHUAN YANG ◽  
AIGUO SONG ◽  
BAOGUO XU
Keyword(s):  
Neural Networks ◽  
Lateral Inhibition ◽  
Coupling Strength ◽  
Excitation Frequency ◽  
Small World ◽  
Threshold Value ◽  
Excitation Intensity ◽  
Inhibition Mechanism ◽  
Stimulation Intensity ◽  
Strong Stimulation

Research is conducted on the discharge rhythm properties of an artificial biological neural network with lateral inhibition mechanism, which is modeled by Hodgkin–Huxley (HH) equations and coupled by small-world (SW) network. The results show that the excitation frequency increases almost linearly along with increasing stimulation intensity, conversely, the average excitation intensity decreases near linearly with the increase of stimulation intensity, when the current stimulation intensity exceeds threshold value. In addition, the influence of coupling strength on excitation properties is noticeable under relatively weak stimulation. The stronger is the coupling strength, the lower is the excitation frequency, whereas the stronger is the coupling strength the greater is the excitation intensity. However, the influence of coupling strength on excitation properties is unnoticeable under relatively strong stimulation. For comparison, discharge rhythm properties of the neural networks under random connection and full connection are also investigated.

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