Informatics Publishing Limited

S. Kanchana ¹, G. Balakrishnan ²

Affiliations
1 Anna University of Technology, Trichy - 620024, Tamil Nadu, IN
2 Indra Ganesan College of Engineering, Trichy - 620012, Tamil Nadu, IN

Abstract

Objectives: An effective feature extraction and segmentation model is employed for palm print images to improve accuracy, computation efficiency and robustness of palm print features. Methods/Analysis: The novel Gaussian Measure Curve let based Feature Segmentation and Extraction (GMC-SE) method is introduced for removal of unwanted execution time by using Edge Based Tangent (EBT) model. In addition, to improve the computation efficiency of features being segmented, competent Gaussian measure is obtained by integrating both local and global palm print features. Findings: Experiment is conducted using Poly U 2D palm print database to measure the effectiveness of the proposed work in terms of execution time ratio, computation efficiency, feature extraction accuracy and robustness in palm print recognition. The proposed scheme GMC-SE method is compared against the existing Fine Ridge Structure Dictionary (FRSD) and Personal Identification using Left and Right Palm Print images (PI-LRPP). As a result, the GMC-SE method improves the computation efficiency by 12% compared to existing FRSD model. Conclusion/Application: An effective feature extraction and segmentation are analyzed for palm print images and experimental results are compared. GMC-SE method for palm print images handled different images in an efficient manner compared to existing works.

Keywords

Edge based Tangent Model, Gaussian Measure Curvelet based Segmentation, Palmprint Images, Palm Segmentation

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S. Mohan Kumar ¹, G. Balakrishnan ²

Affiliations
1 Karpagam University, Coimbatore, Tamil Nadu, IN
2 Indra Ganesan College of Engineering, Trichy, Tamil Nadu, IN

Abstract

Mammogram is measured the most consistent method for early detection of breast cancer. Computer-aided diagnosis system is also able to support radiologist to detect abnormalities earlier and more rapidly. In this paper the performance evaluation of the computer aided diagnostic system for the classification of microcalcification in digital mammogram based on Discrete Wavelet Transform (DWT), Stochastic Neighbor Embedding (SNE) and the Support Vector Machine (SVM) is presented. This proposed system classifies the mammogram images into normal or abnormal, and the abnormal severity into benign or malignant. Mammography Image Analysis society (MIAS) database is used to evaluate the proposed system. The average classification rate achieved is very satisfied.

Keywords

Discrete Wavelet Transform, Stochastic Neighbor Embedding, Digital Mammograms, Microcalcification.

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S. Mohan Kumar ¹, G. Balakrishnan ²

Affiliations
1 Department of Computer Science and Engineering, New Horizon College of Engineering, Outer Ring Road,Near Marathalli, Bellandur Main Road, Bengaluru – 560103 , Karnataka, IN
2 Indra Ganesan College of Engineering, Madurai Main Road (NH-45B), Manikandam, Thiruchirappalli – 620012, Tamil Nadu, IN

Abstract

Mammography is the most perceptive method for the detection of early breast cancer. The abnormalities of breast are analyzed by digital mammogram images and the most important indicators of breast malignancy are microcalcifications and masses. An efficient Computer Aided Diagnosis (CAD) system for breast cancer classification is proposed in this study based on Discrete Wavelet Transform (DWT), Symmetric Stochastic Neighbor Embedding (SSNE) and Support Vector Machine (SVM) using digital mammogram images. Two technical approaches are employed for feature selection from the wavelet decomposed mammogram for classification. They are based on the application of SSNE over the decomposed image. At first, SSNE is applied to the whole wavelet decomposed image whereas in the second technique it is applied to individual sub band of the wavelet decomposed image. The whole mammogram classification system is implemented in two consecutive stages. The first stage of the proposed system classifies the mammogram image into normal or abnormal. The severity of the predicted abnormality is further classified either it is benign or malignant associated with mass or microcalcification images. The performance of the proposed mammogram classification system is evaluated using Mammographic Image Analysis Society (MIAS) database images.

Keywords

Digital Mammogram, Discrete Wavelet Transform, Mass, Microcalcification, Symmetric Stochastic Neighbor Embedding, Support Vector Machine.

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R. Vijayanandh ¹, G. Balakrishnan ²

Affiliations
1 Department of Computer Applications, J.J. College of Engineering & Technology, Tamil Nadu, IN
2 Department of Computer Science and Engineering, Indra Ganesan College of Engineering, Tamil Nadu, IN

Abstract

Face recognition is important in research areas like machine vision and complex security systems. Skin region detection is a vital factor for processing in such systems. Hence the proposed paper focuses on isolating the regions of an image corresponding to human skin region through the hybrid method. This paper intends to combine the skin region detected from RGB and YCbCr color spaces image by the explicit skin color conditions and the skin label cluster identified from CIELab color space image, which is clustered by Hillclimbing segmentation with K-Means clustering algorithm. Then the resultant image is dilated by arbitrary shape and filtered by the median filter, in order to enhance the skin region and to avoid the noise respectively. The proposed method has been tested on various real images, which contain one or more human beings and the performance of skin region detection is found to be quite satisfactory.

Keywords

Color Spaces, Dilation, HillClimbing Segmentation with K-Means, Median Filter.

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Michael Kamaraj ¹, G. Balakrishnan ²

Affiliations
1 Department of Computer Applications, Pavendar Bharathidasan College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Indra Ganesan College of Engineering, IN

Abstract

Many applications such as intelligent transportation, video surveillance, robotics of computer vision mainly depend on task of multiple target tracking. It consists of process of detection, classifications and tracking. In this novel approach of multi target tracking, cost terms are formulated to attain the global optimization which includes the entire representation of the issues such as target tracking, operational representation, collision handling and trajectory processing. Furthermore, two optimization strategies such as the gradient descent which is performed on multiple feature space to obtain local minima of a density function from the given sample of data and gradient ascent which is carried out to achieve a likelihood matching of the target and used to handle the partial evidence of the image, and also uncertainty of the various targets are minimized. . In this study, the proposed works are tested on different publicly available datasets using the metric evaluation and also compared with the various methods based on issues of target tracking. This study will also provide a better understanding of the problem, knowledge of the methods, and experimental evaluation skill for further research works.

Keywords

Multiple Target Tracking, Surviellance, Cost Minimization, Optimization, Tracking Metrics.

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G. Balakrishnan ¹, C. Jebaraj ¹

Affiliations
1 AU-FRG Institute for CAD/CAM,CEG, Anna University, Chennai - 600 025, IN

Abstract

Product Data Management (PDM) is a tool that helps engineers and others to manage both data and the product development process. PDM systems keep track of the volume of data and information generated during the design and manufacturing phases of product development and also support the maintenance phase of the products. These systems integrate and manage processes, applications and information that define products across multiple systems and media. Although PDM benefits an organisation in many ways, it is just a technology and software, and it has to be applied judiciously as It is very expensive. In general, the major obstacles in PDM implementation, relate to (difficulties in assessing Its business value and the management understanding of the same. This paper highlights a model (Return on Investment) that has been developed which could aid in assessing the accrued benefits, tangible benefits, costs, as well as the major needs/reasons for Implementing data management. The return on investment model presents a generalized methodology for evaluating the benefits that an organisation realizes from the implementation and use of a Product data management system. This model has been developed using Spreadsheets, which help to define the projects on which PDM has an impact in a user-friendly manner. This model breaks down the various benefits of PDM implementation into five basic areas (Sub models) and calculates project benefits through different modules. Finally, it gives the summarized results such as the payback period, net present value with discounted flows over the years and the internal rate of return on investment. Also, sensitivity analysis provides insight by changing the parameters relating to any of the benefits which were difficult to estimate.

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G. Balakrishnan ¹, R. Velavan ¹, S. Syed Naser ¹

Affiliations
1 Department of Physics, Bharath Institute of Science and Technology, Bharath Institute of Higher Education and Research, Chennai - 600073, Tamil Nadu, IN

Abstract

In the present work, NiO thin films were prepared on glass and silicon substrates by Radio Frequency (RF) magnetron sputtering technique. NiO films are deposited with the argon flow rate of 10 and 20 sccm at room temperature. The 2^” NiO target was used for the deposition of NiO films and was characterized using X-Ray Diffraction (XRD), Photoluminescence (PL), UV-Visible spectroscopy and Hall Effect measurement to study the structural, optical and electrical properties of the films. The XRD pattern shows the small intense peak, revealing the nanocrystallinity of the NiO film. The transmittance spectra indicated the high transmittance in the order of ~90%. The photoluminescence studies indicated the bandgap of 3.52 eV. The Hall Effect studies demonstrated the p-type behaviour of NiO films. The film showed the p-type conductivity and hole concentration ∼5.34 x10¹⁹ cm⁻³ with Hall mobility of ∼612 cm²/V·s for the film deposited at 20 sccm.

Keywords

Photoluminescence and Hall Measurement, Thin Films, UV-Visible Spectroscopy, X-Ray Diffraction, NiO.

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