A bio-inspired authentication approach to colour image transmitted on open non-secure public network

Kou, Guangjie; Ma, Yunyan; Yue, Jun; Li, Shaojing

doi:10.1504/ijcnds.2017.082103

Cited by 2 publications

(3 citation statements)

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“…; Y (n) are outputted when TPCNN is iterated n times. The image invariant feature usually can be calculated by two methods: time signature [7] and entropy signature [10]. As the entropy is more suitable for measuring the information contained in the output pulse image, the entropy signature is used in the following sections.…”

Section: B Topological Properties Motivated Pcnnmentioning

confidence: 99%

“…Thus, more and more attention has been paid to the image authentication technology in recent years. In brief, image authentication is one kind of technology to protect the image integrity from illegally modified [7], [22]. There are two categories of image authentication schemes: signature-based schemes and watermark-based schemes.…”

Section: Image Authentication and Tampering Area Detectionmentioning

confidence: 99%

“…As a bio-inspired neural network model, the pulse coupled neural network (PCNN) [1], [2] has many good characteristics, such as a single layer, no prior training is required, possessing a good theoretical basis of the biological vision system, etc. Nowadays, PCNN is widely used in image segmentation [3]- [5], image enhancement [6], image authentication [7], [8], image fusion [9], feature extraction and pattern recognition [10]- [14] etc. Though it already has a good performance about the invariant features (also called image signature) extracted by basic PCNN, great changes may happen when the target's shaping changes a little.…”

Section: Introductionmentioning

confidence: 99%

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Color Image Invariant Feature Extraction by a Topological Property Motivated PCNN Model

Kou

Yue

et al. 2019

IEEE Access

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Topological invariant features take priority over other vision features in early visual perception stage, which is the core idea of topological perception theory. In order to improve the robustness and distinguishability of the invariant features extracted by pulse coupled neural network (PCNN), the topological properties are integrated into PCNN. The improved PCNN model is called as topological property motivated PCNN (TPCNN), which adopts the saliency map calculated by the spectral residual approach as the important topological properties (the connectivity, and the number of holes in target). In TPCNN, firstly, the normalized saliency map is used as a linking coefficient to enhance the importance of saliency object when we calculating the invariant features. Secondly, the entropy signature of the saliency map is treated as an additional new feature and merged into original features calculated by PCNN, then the final invariant feature is obtained. The proposed TPCNN is used to calculate the invariant feature of different kinds of fish in the paper. Experimental results show that TPCNN outperforms the state-of-art models on invariant features extraction.INDEX TERMS Pulse coupled neural network, invariant feature extraction, topological perception theory, saliency map, spectral residual approach.

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Section: B Topological Properties Motivated Pcnnmentioning

confidence: 99%

Section: Image Authentication and Tampering Area Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Color Image Invariant Feature Extraction by a Topological Property Motivated PCNN Model

Kou

Yue

et al. 2019

IEEE Access

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Research on the Application of Visual Sensing Technology in Art Education

Wen-li

Zhang

2021

Journal of Sensors

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It is essential to have a new understanding of the development of visual sensing technology in digital image art at this stage, in order to make traditional art education have new professional ability teaching. Based on the current research results in related fields, a three-dimensional (3D) image visual communication system based on digital image automatic reconstruction is proposed with two schemes as the premise. In scheme 1, the hardware part is divided into two modules. The hardware used by the analysis of the 3D image layer module is the HUJ-23 3D image processor. The acquisition of a 3D image layer module uses the hardware of a realistic infrared camera. The software of the system consists of two parts: a 3D image computer expression module and a 3D image reconstruction module. A simulation platform is established. The test data of 3D image reconstruction accuracy and visual communication integrity of the designed system show that both of them show a good trend. In scheme 2, regarding digital image processing, the 3D image visual perception reconstruction is affected by the modeling conditions, and some images are incomplete and damaged. The depth camera and image processor that can be used in the visual communication technology are selected, and their internal parameters are modified to borrow them in the original system hardware. Gaussian filtering model combined with scale-invariant feature transform (SIFT) feature point extraction algorithm is adopted to select image feature points. Previous system reconstruction technology is used to upgrade the 3D digital image, and the feature point detection equation is adopted to detect the accuracy of the upgraded results. Based on the above hardware and software research, the 3D digital image system based on visual communication is successfully upgraded. The test platform is established, and the test samples are selected. Unlike the previous systems, the 3D image reconstruction accuracy of the designed visual communication system can be as high as 98%; the upgraded system has better image integrity and stronger performance than the previous systems and achieves higher visual sensing technology. In art education, it can provide a new content perspective for digital image art teaching.

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A bio-inspired authentication approach to colour image transmitted on open non-secure public network

Cited by 2 publications

References 13 publications

Color Image Invariant Feature Extraction by a Topological Property Motivated PCNN Model

Color Image Invariant Feature Extraction by a Topological Property Motivated PCNN Model

Research on the Application of Visual Sensing Technology in Art Education

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