Digital Image Processing

P. Sivakumar ¹, S. Ravi ²

Affiliations
1 St. Peter's University, Chennai, IN
2 ECE Department, Dr. MGR University, Chennai-600095, IN

Abstract

This paper uses the wavelet, Contourlet transform and the two dimensional Principle Component Analysis (2DPCA) for image denoising. The simulation results is carried out to demonstrate the performance of proposed techniques and the result shows that 2DPCA is superior over contourlet and wavelet transform in maintaining high peak signal -to-noise -ratio. The denoising algorithm is validated by numerical experiments on different standard images and roughness images. Numerical result shows that the proposed method can obtain higher PSNR and less visual artifacts compared with other methods. Also, Contourlet can provide a much more detailed representation for natural images with abundant textural information than Wavelets. The improved two dimensional PCA (2DPCA) extended the traditional principal component analysis theory to the two dimensional situation, and proved to be especially efficient in the analysis of images.

Keywords

Image Denoising, Contourlet, Two-Dimensional PCA, Wavelet, PSNR.

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B. Muthukumar ¹, S. Ravi ²

Affiliations
1 Kathir College of Engineering, Coimbatore - 62, IN
2 Department of ECE in Dr.M.G.R. University, Chennai - 95, IN

Abstract

Level set-contour region based segmentation methods are the numerical techniques for tracking interfaces and shapes. They have been successfully used in image segmentation. It consists of an internal energy term that penalizes deviations of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature. The energy to be re-formulated in a local way. It considers local rather than global image statistics and evolves a contour based on local information. Localized contours are capable of segmenting objects with heterogeneous feature profiles that would be difficult to capture correctly using a standard global method.

Keywords

Level Set, Curve Evolution, Contour-Region Based Segmentation.

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G. Saravana Kumar ¹, S. Ravi ²

Affiliations
1 Sri Venkateswara College of Engineering & Technology, Chennai, IN
2 Department of ECE, Dr. M.G.R. University, Chennai-600 095, IN

Abstract

In this work, a method to reduce electroencephalogram (EEG) artifacts from Visual-Evoked Potentials (VEP) in brain-computer interface (BCIs) design is presented. For the test composite signal the frequency ranges corresponding to stimulus-related VEP components were located using cyclo stationary (CS) analysis based algorithm.  The resulting cyclic frequency spectrum provides VEP frequency band detection.  Using this identified frequency ranges, low pass or band pass filtering is employed for EEG artifacts reduction.  The proposed Statistical Coefficient Selection (SCS) and wavelet-based method called Wavelet Denoising Algorithm (WDA) are used to distinguish VEP components and EEG artifacts. The proposed scheme exhibits satisfactory results with various datasets

Keywords

Brain Computer Interface (BCI), Statistical Coefficient Selection (SCS), Wavelet Denoising Algorithm, Visual Evoked Potential (VEP).

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