Face Recognition Based on Unit-Linking PCNN Time Signature

Li, Haiyan; Xu, Dan; Zong, Ruowen

doi:10.1109/icacc.2009.60

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…So far, there have been lots of modified models, such as ICM [33], unit linking PCNN model [34], fast-linking PCNN model [35], SCM [36]. It has been proved that the SCM has lower computational complexity and higher accuracy rates for image segmentation when compared with other models [15]; so SPCNN which is derived from SCM model and showed in Figure 2 could be used in this paper and is described as followed:…”

Section: B Simplified Pcnnmentioning

confidence: 99%

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

Jiao

Zhang

2019

IEEE Access

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This paper proposed a novel image segmentation method based on simplified pulse coupled neural network (SPCNN), which was optimized by gbest led gravitational search algorithm (GLGSA) that combined gravitational search algorithm (GSA) with gbest agent memory ability. To evaluate the performance of GLGSA, we applied it to 23 standard benchmark functions and compared with GSA and GGSA. The results showed that the GLGSA had better performance in term of convergence and avoidance of local minima. Besides, in order to improve the accuracy of segmentation, the fitness function consisted of cross entropy parameter, edge matching, and noise control. To verify the efficiency of our method, we compared it with the state-of-the-art algorithms, such as Otsu, GA Renyi, and PSO-PCNN, using the gray nature images from the Berkeley segmentation dataset. Finally, the subjective visual analysis and quantitative analysis that included the uniformity measure, region contrast measure, structural similarity, and comprehensive evaluation were used to evaluate the segmented images. The comparison results demonstrated that our proposed method could get better segmentation results. INDEX TERMS Image segmentation, gravitational search algorithm, pulse coupled neural network, edge matching, noise control.

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Section: B Simplified Pcnnmentioning

confidence: 99%

A Novel Method for Image Segmentation Based on Simplified Pulse Coupled Neural Network and Gbest Led Gravitational Search Algorithm

Jiao

Zhang

2019

IEEE Access

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“…In addition it was successfully explored in many applications such as car plate number detection [25], face recognition [26], and object detection [27].…”

Section: Pulse-coupled Neural Networkmentioning

confidence: 99%

Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks

Hage

Hamade

2015

J Bone Miner Metab

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In cortical bone, solid (lamellar and interstitial) matrix occupies space left over by porous microfeatures such as Haversian canals, lacunae, and canaliculi-containing clusters. In this work, pulse-coupled neural networks (PCNN) were used to automatically distinguish the microfeatures present in histology slides of cortical bone. The networks' parameters were optimized using particle swarm optimization (PSO). When forming the fitness functions for the PSO, we considered the microfeatures' geometric attributes-namely, their size (based on measures of elliptical perimeter or area), shape (based on measures of compactness or the ratio of minor axis length to major axis length), and a two-way combination of these two geometric attributes. This hybrid PCNN-PSO method was further enhanced for pulse evaluation by combination with yet another method, adaptive threshold (AT), where the PCNN algorithm is repeated until the best threshold is found corresponding to the maximum variance between two segmented regions. Together, this framework of using PCNN-PSO-AT constitutes, we believe, a novel framework in biomedical imaging. Using this framework and extracting microfeatures from only one training image, we successfully extracted microfeatures from other test images. The high fidelity of all resultant segments was established using quantitative metrics such as precision, specificity, and Dice indices.

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