An improved pulse coupled neural network with spectral residual for infrared pedestrian segmentation

He, Fenghua; Guo, Yongcai; Gao, Chao

doi:10.1016/j.infrared.2017.09.017

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…At the same time, the local similarity information based on regions is used to effectively control the influence of noise and unevenness. He et al [16] presents an improved PCNN model. Firstly, the weight matrix of the feeding input field is designed by the anisotropic Gaussian kernels (ANGKs), in order to suppress the infrared noise effectively.…”

Section: A Far-infrared Image Segmentationmentioning

confidence: 99%

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Far-Infrared Object Segmentation Focus on Transmission of Overall Semantic Information

Zang

et al. 2020

IEEE Access

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In the vision task of a self-driving system, the use of visible light images to segment an object often loses its functionality at night or in harsh weather. The far-infrared image shows different pixel values according to the thermal radiation quantity of the object itself, so it can be adapted to perform well at night and in harsh weather conditions. However, at the same time, it has insufficient texture features, blurred object boundaries and temperature inversion, which has a great impact on the segmentation task of traditional algorithms. In response to the above problems, this paper proposes a far-infrared object segmentation algorithm using deep learning. In the current popular encoding-decoding structure, multiscale pooling layers are used to obtain receptive fields of different sizes. This is used to solve the effects caused by the blurring of infrared objects. The feature enhancement module is designed for the multireceptive field feature map, which can filter out the most versatile and highly semantic feature channels to reduce the effect of temperature inversion on segmentation. The obtained high semantic feature map is guided into the decoding structure and is fused with the features obtained by the encoder and the decoder. This allows richer information to be obtained between different feature maps. Finally, we also release a new low-resolution far-infrared segmentation dataset. Experiments are performed on three datasets, and the segmentation result of the mIoU(mean Intersection over Union) reaches 70.59%, 30.98% and 60.67%. A large number of experiments confirm the effectiveness and robustness of the network in far-infrared images and verify that the dataset released in this paper has strong reference significance.

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Section: A Far-infrared Image Segmentationmentioning

confidence: 99%

“…In recent years, deep learning technology has been used in the field of visible light image segmentation. We believe that deep learning method can also be used for far-infrared images [7,16]. However, the current popular semantic segmentation method for deep learning is not completely suitable for far-infrared images.…”

Section: Introductionmentioning

confidence: 99%

Far-Infrared Object Segmentation Focus on Transmission of Overall Semantic Information

Zang

et al. 2020

IEEE Access

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“…Zhou and Shao [18] proposed an extended PCNN model based on the strategy of the decision tree, and established the relationship between parameters and image features. He et al [19] made the normalized spectral residual saliency as the linking coefficient and used the improved dynamic threshold based on the average gray values of the iteration segmentation to simplify the PCNN model. Lian et al [20] used an optimal histogram threshold to determine the parameters of SPCNN for various images and added an offset to improve the segmentation precision.…”

Section: Introductionmentioning

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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“…Pulse coupled neural network (PCNN), based on the phenomena of propagating oscillating pulses in the brain visual cortex of cats (Eckhorn et al 1988), has the characteristic that the group of neurons with similar stimulus could spark synchronous oscillating pulses. PCNN had been successfully applied in image segmentation (Na et al 2012;Deng and Ma 2014;Zhou and Shao 2017;He et al 2017), image fusion (Kong and Liu 2013;Xiang et al 2015;Ganasala and Kumar 2016;Wang and Gong 2017) and so on. Due to the exponential decay of the dynamic threshold and the pulse coupling among neurons, the PCNN neurons present a non-linear transformation for stimuli such as images.…”

Section: Introductionmentioning

confidence: 99%

Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare

Nie

Cao

et al. 2018

J Ambient Intell Human Comput

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With the rapid growth of medical big data, medical signal processing measurement techniques are facing severe challenges. Enormous medical images are constantly generated by various health monitoring and sensing devices, such as ultrasound, MRI machines. Hence, based on pulse coupled neural network (PCNN) and the classical visual receptive field (CVRF) with the difference of two Gaussians (DOG), a contrast enhancement of MRI image is suggested to improve the accuracy of clinical diagnosis for smarter mobile healthcare. As one premise, the parameters of DOG are estimated from the fundamentals of CVRF; then the PCNN parameters in image enhancement are estimated eventually with the help of DOG. As a result, the MRI images can be enhanced adaptively. Due to the exponential decay of the dynamic threshold and the pulses coupling among neurons, PCNN effectively enhances the contrast of low grey levels in MRI image. Moreover, because of the inhibitory effects from inhibitory region in CVRF, PCNN also effectively preserves the structures such as edges for enhanced results. Experiments on several MRI images show that the proposed method performs better than other methods by improving contrast and preserving structures well.

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An improved pulse coupled neural network with spectral residual for infrared pedestrian segmentation

Cited by 10 publications

References 27 publications

Far-Infrared Object Segmentation Focus on Transmission of Overall Semantic Information

Far-Infrared Object Segmentation Focus on Transmission of Overall Semantic Information

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

Pulse coupled neural network based MRI image enhancement using classical visual receptive field for smarter mobile healthcare

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