Journal of Information and Computing Science JICS Volume 5, Number 1

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Volume 5, Number 1

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A Cellular Neural Network- Based Model for Edge Detection

Hezekiah Babatunde, Olusegun Folorunso and Adio Akinwale

J. Info. Comput. Sci. , 5 (2010), pp. 003-010.

[An open-access article; the PDF is free to any online user.]

Full PDF 191 3272 Abstract

Abstract

This study employed the use of Cellular Neural Networks (CNN) for Edge Detection in images due to its high operational speed. The process of edge detection is unavoidable in many image processing tasks such as obstacle detection and satellite picture processing .The conventional edge detector models such as Sobel Operator, Robert Cross, among which Canny is the best, have high computational time. The CNN Model is a class of Differential Equation that has been known to have many application areas and high operational speed. The work investigated four parameters: resolutions, processing time, false alarm rate, and usability for performance evaluation. The CNN Model was modified by using hyperbolic tangent (tanh x) and Von Neumann Neighborhood. The modified CNN Model and enhanced Canny Model were implemented using MATLAB 7.0 running on Pentium III and 128 MB RAM Personal Computer. A series of images served as input for both Canny and modified CNN Model. With several images tested, the overall results indicated that the two models have similar resolutions with average computational time of 1.1078 seconds and 2.293 seconds for CNN-based and Enhanced Canny Model respectively. The hyperbolic entry a22 of the cloning template A made our work fully controllable since max(tanh x) = +1 and min(tanh x) = -1. A consideration of the set of digital images showed that edge maps which result from Canny Model have adjacent boundaries that tend to merge. The CNN model obviously produced the optimum edge map with 2FP    edges that are one-pixel wide and unbroken. The false alarm rate of noise variance probability for which an edge detector can easily declare an edge pixel given that there is no edge, showed the value 0.8525 for CNN Model and 0.4595 for Canny Model. The CNN parameters can be adjusted and modeled to solve Partial Differential Equations and Maximum likelihood problems for edge detection while Canny Model cannot be easily adjusted for any other functionality. The CNN-based edge detector performed better than the popular canny operator in terms of the computational time required, usability, and false alarm rate.

Anti-Synchronization of two Different Chaotic Systems via Optimal Control with Fully Unknown Parameters

HonglanZhu

J. Info. Comput. Sci. , 5 (2010), pp. 011-018.

[An open-access article; the PDF is free to any online user.]

Full PDF 202 3514 Abstract

Structural Risk Minimization Principle Based on Complex Fuzzy Random Samples

Zhiming Zhang, Jingfeng Tian

J. Info. Comput. Sci. , 5 (2010), pp. 019-040.

[An open-access article; the PDF is free to any online user.]

Full PDF 203 3503 Abstract

Linearisation of Boundary Optimal Control Problems by Finite Element Method

N. Davoodi, A. Kerayechian , M. Gachpazan

J. Info. Comput. Sci. , 5 (2010), pp. 041-046.

[An open-access article; the PDF is free to any online user.]

Full PDF 194 3303 Abstract

Efficient DCT-Domain Blind Measurement of Blocking Artifacts

Jagroop Singh, Sukwinder Singh, Dilbag Singh and MoinUddin

J. Info. Comput. Sci. , 5 (2010), pp. 047-054.

[An open-access article; the PDF is free to any online user.]

Full PDF 192 3523 Abstract

An Iterative Conjugate Gradient Regularization Method for Image Restoration

Jianjun Zhang , Qin Wang

J. Info. Comput. Sci. , 5 (2010), pp. 055-062.

[An open-access article; the PDF is free to any online user.]

Full PDF 198 3339 Abstract

Adaptive Projective Synchronization in Weighted Dynamical Complex Networks with Time-Varying Coupling Delay

Haijian Shao and Guoliang Cai

J. Info. Comput. Sci. , 5 (2010), pp. 063-070.

[An open-access article; the PDF is free to any online user.]

Full PDF 201 3481 Abstract

Modelling of Textile Structure for Advanced Applications

Xiaogang Chen

J. Info. Comput. Sci. , 5 (2010), pp. 071-080.

[An open-access article; the PDF is free to any online user.]

Full PDF 193 3348 Abstract

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