Informatics Publishing Limited

S. Kother Mohideen ¹, S. Arumuga Perumal ², N. Krishnan ³, K. Kannan ⁴

Affiliations
1 Department of Computer Science, Sadakathullah Appa College, Tirunelveli-627011, Tamilnadu, IN
2 Department of Computer Science, S. T. Hindu College, Nagarcoil-629002, Tamilnadu, IN
3 Centre for Information Technology and Engineering, Manonmanium Sundaranar University, Tirunelveli, Tamilnadu-627012, IN
4 Department of Electronics and Communication Engineering, Kamaraj College of Engineering and Technology, SPGC Nagar, Post Box No.12, Virudhunagar-626001, Tamilnadu, IN

Abstract

Removing noise from the original image is still a challenging problem for researchers. A traditional way to remove noise from image data is to employ spatial filters. With wavelet transform gaining popularity in the last two decades, various algorithms for denoising in wavelet domain were introduced. In this paper, it is proposed to combine Neighshrink and Sureshrink to denoise an image corrupted by additive white Gaussian noise in wavelet domain.

Keywords

Image Denoising, Dual Tree Discrete Wavelet Packet Transform, Root Mean Square Error, Peak Signal to Noise Ratio, Quality Index and Normalized Weighted Performance Metric.

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T. C. Rajakumar ¹, S. Arumuga Perumal ², N. Krishnan ³

Affiliations
1 Department of Computer Science, St. Xavier’s College, Tamil Nadu, IN
2 Department of Computer Science, S.T. Hindu College, Tamil Nadu, IN
3 Department of Information Technology, M.S. University, Tamil Nadu, IN

Abstract

Contour detection is the basic property of image processing. Fuzzy Filtering technique is proposed to generate thick edges in two dimensional gray images. Fuzzy logic is applied to extract value for an image and is used for object contour detection. Fuzzy based pixel selection can reduce the drawbacks of conventional methods(Prewitt, Robert). In the traditional methods, filter mask is used for all kinds of images. It may succeed in one kind of image but fail in another one. In this frame work the threshold parameter values are obtained from the fuzzy histogram of the input image. The Fuzzy inference method selects the complete information about the border of the object and the resultant image has less impulse noise and the contrast of the edge is increased. The extracted object contour is thicker than the existing methods. The performance of the algorithm is tested with Peak Signal Noise Ratio(PSNR) and Complex Wavelet Structural Similarity Metrics(CWSSIM).

Keywords

Contour Detection, Threshold, Histogram, Fuzzy Filtering, Fuzzy Logic.

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T. C. Raja Kumar ¹, S. Arumuga Perumal ², N. Krishnan ³

Affiliations
1 Department of Computer Science, St. Xavier’s College, Tamil Nadu, IN
2 Department of Computer Science, S.T. Hindu College, Tamil Nadu, IN
3 Department of Information Technology, Manonmaniam Sundaranar University, Tamil Nadu, IN

Abstract

Contrast Stretching is an important part in medical image processing applications. Contrast is the difference between two adjacent pixels. Fuzzy statistical values are analyzed and better results are produced in the spatial domain of the input image. The histogram mapping produces the resultant image with less impulsive noise and smooth nature. The probabilities of gray values are generated and the fuzzy set is determined from the position of the input image pixel. The result indicates the good performance of the proposed fuzzy based stretching. The inverse transform of the real values are mapped with the input image to generate the fuzzy statistics. This approach gives a flexible image enhancement for medical images in the presence of noises.

Keywords

Contrast Stretching, Fuzzy Logic, Fuzzy statistics, Histogram Specification, Probability Density Function (PDF), Cumulative Density Function (CDF).

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N. Krishnan ¹, S. Sathyapriya ², D. Anitha ²

Affiliations
1 Department of Computer Science, Sankara College of Science and Commerce, Coimbatore, IN
2 Department of Computer Science, Sri Ramakrishna Arts and Science College for Women, Coimbatore, IN

Abstract

The significant chore of estimation mining is to extract opinion targets and estimation vocabulary from a huge number of product reviews. The one of the come within reach of proposes a method base on partially supervised word arrangement representation, in which estimation relations naming is consider as an alignment process. Manipulative estimation relationship between words is an important for constructing Co-Ranking graph; to find self-assurance of each applicant graph based co-ranking algorithm is used. Higher confidence applicant are extracted as estimation targets or estimation words. Prior information also consider in finding confidence of applicant as being estimation target or estimation word. Previous methods are based on sentence structure based, compared to these methods proposed model minimizes negative effects of parsing errors. Due to use of partial supervision proposed model achieves better accuracy compared to unsupervised word alignment model. Final task is to extractive summary generation from estimation Targets and estimation Words with Word Alignment Model.

Keywords

Estimation Mining, Estimation Target Extraction, Estimation Word Extraction, Text Mining.

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