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Volume-4 Issue-11: Published on June 15, 2017
Volume-4 Issue-11: Published on June 15, 2017

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Volume-4 Issue-11, June 2017, ISSN: 2319–6386 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.



Prasanna.S, Durgadevi.S, Senthilkumar.S

Paper Title:

An Efficient Salient Region Detection on Underwater Image with Grabcut Algorithm

Abstract:  Saliency detection aims at automatically estimating visually salient object regions in an image, saliency segmentation and foreground extraction are two important applications of this. However, it is a challenge for underwater images to estimate salient regions by saliency detection methods because of the low-contrast and poor quality. In this paper, we address this problem by combining the detected object regions rather than the whole image, where Sobel edge detector and Active contour is used for proposing candidate regions. We extensively evaluated our method on underwater images, and experimental results show that the performances of saliency detection and segmentation are improved. These saliency segmentation masks are further used to extract the foreground objects of an image.

  Saliency segmentation, foreground extraction, fish localization, underwater image.


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4.       Rother, Carsten, Vladimir Kolmogorov, and Andrew Blake. "Grabcut: Interactive foreground extraction  using  iterated graph cuts."ACM Transactions on Graphics (TOG)       23.3 (2004): 309-314.

5.       Chen, Tao, et al. "Sketch2Photo: internet image montage." ACM Transactions on Graphics (TOG) 28.5 (2009): 124

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8.       Achanta, Ravi, et al. "Frequency-tuned salient region  detection." Computer vision and pattern recognition, 2009. cvpr 2009. IEEE conference on. IEEE, 2009.

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V. Parvathavarthini, S. M. Ramesh, M. Irshad Ahamed

Paper Title:

Detection of Prostate Cancer with MAPS Feature Model using Region Growing Algorithm

Abstract: In this paper we present a new method for automated and quantitative grading of prostate cancer. A total of 102 graph-based, morphological, and textural features are extracted from each tissue patch in order to quantify the arrangement of structures within digitized images of prostate cancer. A support vector machine (SVM) is used to classify the prostate into benign or malignant based on four appearance features extracted from registered images. Moreover, in this paper we introduce a new approach to generate level of  cancer, that illustrate the propagation of diffusion in prostate tissues based on the analysis of the MAPS of the change of the gray level values of prostate voxel using  (GGMRF) image model. Finally, the tumor boundaries are determined using a level set deformable model controlled by the diffusion information and the spatial interactions between the prostate voxels. Experimental results on 28 clinical diffusion weighted MRI data sets yield promising results.

Keywords: classifiers; C Timages; MAPS; Prostate cancer.


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Janhavi Suchet Vakil

Paper Title:

A Survey Paper on Identifying Candidate Features in Opinion Mining using Intrinsic and Extrinsic Domain Relevance

Abstract:  Opinion feature extraction is the process of obtaining candidate features from the existing set of features identified from reviews and opinions. We study few techniques and propose a novel method to identify candidate features using different pattern mining approaches and extract relevant information using a set of syntactic rules. Using Dependency Parsing (DP) we can extract Parts of Speech (POS). The POS can be used to extract candidate features using syntactic rules and thus obtain candidate features. According to previous studies candidate features that are less generic and more domain-specific are then confirmed as opinion features. Previous experimental results on two real evaluation domains show that this approach may surpass several other well-established methods for identifying opinion characteristics.

Keywords:  opinion mining. Sentiment analysis, Intrinsic, Extrinsic, Domain Relevance, Stanford NLP

1.       Zhen Hai, Kuiyu Chang, Jung-Jae Kim, and Christopher C. Yang, “Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance”, IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 3, March 2014
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A. Monica, D. Devarajan, L. Ramachandran

Paper Title:

Diffusion Tensor MRI of Human Heart using Wavelet Based Approach

Abstract: Diffusion tensor MRI of human heart is a promising but challenging technique in the medical field. This work proposed the wavelet based image fusion using MRI images and three-dimensional of the diffusion tensor imaging. Medical image fusion is the idea to improve the image content by fusing images taken from different imaging tools like Magnetic Resonance Imaging (MRI) and three dimensional Diffusion Tensor Imaging (DTI). Then the ten short axis slices images are taken entire heart using multiple shifted trigger delay under free breathing conditions. Then post processing scheme are used the algorithm based on wavelet based image fusion (WIF) method. Diffusion tensor Magnetic resonance image (MRI) image are fused and better it has been compared with existing method. The proposed of Wavelets are decompose the image into different sub-band images.

Keywords:   Diffusion tensor MRI, Wavelet transform, Image fusion, Haar wavelet based fusion

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