Ship target recognition with the Mellin transform aided by neural networks

Serretta, Hyram; Inggs, M.R.

doi:10.1109/comsig.1998.736949

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…In many signal and image processing applications, such as speech processing [1], image registration [2]- [3], ship target recoginition [4]- [5], etc., it is required to establish the equivalence between different signals which are actually the scaled version of an original signal. Mellin transform [6] can be utilized for this purpose due to its scale invariant property.…”

Section: Introductionmentioning

confidence: 99%

Parallel architecture for real time computation of discrete Mellin transform

Ray

Dhar

2014

2014 2nd International Conference on Emerging Technology Trends in Electronics, Communication and Networking

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In this paper, a parallel architecture for the computation of one dimensional discrete Mellin transform (DMT) is proposed for real time hardware implementation. Since Mellin transform is a useful technique in pattern recognition, speech processing, image registration and signal detection due to its scale invariant property, so it is essential to develop VLSI architecture of this transformation for hardware implementation in real time applications. The proposed parallel architecture for one dimensional DMT is based on ROM and MAC(Multiply and Accumulator), which is designed using verilog hardware description language, and further synthesized using Xilinx synthesis tool(XST) targeting the commercially available Xilinx FPGA device "XC3SPQ208". Synthesis result shows that it can be operated at a clock frequency of 32MHz making it suitable for real time applications.

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Section: Introductionmentioning

confidence: 99%

Parallel architecture for real time computation of discrete Mellin transform

Ray

Dhar

2014

2014 2nd International Conference on Emerging Technology Trends in Electronics, Communication and Networking

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“…Gouaillier recognize the ships using principal component analysis (PCA) method [1] . Serretta achieves the implementation and use of the mellin transform and neural networks for radar ship target recognition [2] . DeSilva describes the simulated ship recognition of all-aspect using moment feature [3] .…”

Section: Introductionmentioning

confidence: 99%

Simulated ship recognition using two-dimensional PCA

et al. 2007

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This paper proposes a fast and robust algorithm for classification and recognition of ships based on the two-dimensional Principal Component Analysis (2DPCA) method. The three-dimensional ship models achieve by modeling software of MultiGen, and then they are projected by Vega simulating software for two-dimensional ship silhouettes. The 2DPCA method as against conventional PCA method for simulated ship recognition using training and testing experiments, as the training and testing sample size is large, and there are great variations in different azimuth and elevation for ship viewpoints. The experiment of ship recognition using the global feature of ships is not satisfied with us, so we proposed an improved 2DPCA method based on the local feature of ships. Some recognition results from simulated data are presented, it shows that the improved 2DPCA method outperform PCA in ship recognition and also superior to PCA in terms of computational efficiency for feature extraction. So our method is more preferable for ship classification and recognition.

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An expert target recognition system using a genetic wavelet neural network

Avcı

2012

Appl Intell

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Ship target recognition with the Mellin transform aided by neural networks

Cited by 5 publications

References 4 publications

Parallel architecture for real time computation of discrete Mellin transform

Parallel architecture for real time computation of discrete Mellin transform

Simulated ship recognition using two-dimensional PCA

An expert target recognition system using a genetic wavelet neural network

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