Image Segmentation with PCNN Model and Immune Algorithm

Li, Jianfeng; Zou, Beiji; Ding, Lei; Gao, Xu

doi:10.4304/jcp.8.9.2429-2436

Cited by 10 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our hybrid tracking strategy is implemented by C++ in VS 2010 without any optimization on Windows 7 system, and runs at about 25 frames per second (FPS) on a core i3 with CPU 2.10 GHz and 4 GB RAM notebook computer. Our source codes fuse the pixel information of PCNN [16] and online MIL [12] to decide the most important positive sample. The tracker is implemented by [17].…”

Section: B the Visual Object Tracking Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

A new hybrid tracking strategy based on Pulse Coupled Neural Network

Jia

Dong

et al. 2015

2015 IEEE International Conference on Information and Automation

View full text Add to dashboard Cite

Visual object tracking is a fundamental research topic in computer vision. In this paper, we proposed a novel hybrid tracking method based on Pulse Coupled Neural Network (PCNN) and Multiple Instance Learning (MIL). Most modern trackers may be inaccurate when the training samples are imprecise which causes drift. To resolve these problems, MIL method is introduced into the tracking task, which can alleviate drift to some extent. However, the MIL tracker may detect the positive sample that is less important. PCNN is different from traditional artificial neural networks, which can be applied in many image processing fields, such as image segmentation. So, the PCNN was employed as sample detector which can know the most important sample when training the classifier. Then, a more robust and much faster tracker is proposed to approximately maximize the bag likelihood function. Empirical results on a large set of sequences demonstrate the superior performance of the proposed approach in robustness, stability and efficiency to state-of-the-art methods in the literature.

show abstract

Section: B the Visual Object Tracking Experimentsmentioning

confidence: 99%

“…Wang et al [15] used simplified PCNN for separating the cucumber from complex background. Li et al [16] worked on Immune algorithm which helped PCNN parameters to be adjusted adaptively by taking image entropy as the evaluation basis. Their experimental results proved that the proposed method yielded better segmentation performance.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid tracking strategy based on Pulse Coupled Neural Network

Jia

Dong

et al. 2015

2015 IEEE International Conference on Information and Automation

View full text Add to dashboard Cite

show abstract

“…After the ANN was used in the target detection, many kinds of immune neural networks are used for image target processing. The pulse coupled neural network (PCNN) was proposed based on immune algorithm, which took entropy as the evaluation basis of the best fitness of PCNN [12]. For dim and small targets with few features on complex background, a feature deep neural network was proposed, which has high detection precision [13].…”

Section: Introductionmentioning

confidence: 99%

Infrared Target Extraction Based on Immune Extension Neural Network

Dong

Hao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper addresses the difficulties of target extraction from low contrast low resolution infrared images. The extension neural network is integrated with the negative selection algorithm (NSA) in artificial immunity. Based on the specific recognition principle of long non-coding RNA (lncRNA) in biological immune system, the mature self-extension detectors are calculated to extract infrared target and background. Upon the convergence of clustering process, the output layer of network merges the categories corresponding to the target to be detected. The proposed method, it needs less detectors than the traditional NSA algorithm. Compared with several target detection algorithms, the proposed algorithm has higher detection accuracy and higher time efficiency. INDEX TERMS Artificial immunity, extension analysis, negative selection algorithm (NSA), long noncoding RNA (lncRNA), infrared target extraction.

show abstract

“…In 1993, Johnson and Ritter proposed pulse coupled neural network (PCNN) [8] based on Eckhorn research in cat's visual cortex. It has widely been used in image segmentation [9,10], image fusion [11][12][13], image retrieval [14], and so forth. In PCNN, the similarity group neurons will issue synchronous pulses under the effect of mutual coupling pulses.…”

Section: Introductionmentioning

confidence: 99%

Facial Feature Extraction Using Frequency Map Series in PCNN

Nie¹,

Zhou

He³

et al. 2016

Journal of Sensors

View full text Add to dashboard Cite

Pulse coupled neural network (PCNN) has been widely used in image processing. The 3D binary map series (BMS) generated by PCNN effectively describes image feature information such as edges and regional distribution, so BMS can be treated as the basis of extracting 1D oscillation time series (OTS) for an image. However, the traditional methods using BMS did not consider the correlation of the binary sequence in BMS and the space structure for every map. By further processing for BMS, a novel facial feature extraction method is proposed. Firstly, consider the correlation among maps in BMS; a method is put forward to transform BMS into frequency map series (FMS), and the method lessens the influence of noncontinuous feature regions in binary images on OTS-BMS. Then, by computing the 2D entropy for every map in FMS, the 3D FMS is transformed into 1D OTS (OTS-FMS), which has good geometry invariance for the facial image, and contains the space structure information of the image. Finally, by analyzing the OTS-FMS, the standard Euclidean distance is used to measure the distances for OTS-FMS. Experimental results verify the effectiveness of OTS-FMS in facial recognition, and it shows better recognition performance than other feature extraction methods.

show abstract

Image Segmentation with PCNN Model and Immune Algorithm

Cited by 10 publications

References 7 publications

A new hybrid tracking strategy based on Pulse Coupled Neural Network

A new hybrid tracking strategy based on Pulse Coupled Neural Network

Infrared Target Extraction Based on Immune Extension Neural Network

Facial Feature Extraction Using Frequency Map Series in PCNN

Contact Info

Product

Resources

About