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Low contrast satellite image restoration based on adaptive histogram equalization and discrete wavelet transform

Low contrast satellite image restoration based on adaptive h...

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2016 International Conference on Communication and Signal Processing, ICCSP 2016

作者： Megha, P. Swarna, M. Sowmya, V. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita School of Engineering Coimbatore India

ISBN: (纸本)9781509003969

Normally images obtained from satellites are of low-contrast type which hides major information carried by the image. Hence, image restoration is necessary in the image processing domain to extract all the information present in the images. The low contrast satellite image restoration based on adaptive histogram equalization combined with Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed in this paper. The proposed technique is experimented on three different satellite images. The effectiveness of the method introduced in this paper is shown by comparing it against the existing techniques based on gamma correction and histogram equalization combined with DCT and DWT. The comparison is done based on the standard parameters called Peak Signal to Noise Ratio (PSNR) and Standard Deviation. The result and analysis on the basis of PSNR values shows that adaptive histogram equalization combined with DWT is more effective approach compared to adaptive histogram equalization combined with DCT. © 2016 IEEE.

关键词： Discrete cosine transforms

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Tamil characters classification using random Kitchen Sink Algorithm (RKS) 14

Tamil characters classification using random Kitchen Sink Al...

引用

2014 International Conference on Interdisciplinary Advances in Applied Computing, ICONIAAC 2014

作者： Karthieswari, R. Sreethivya, M. Deva Rajendra Kumar, D. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781450329088

Tamil is a classical south Indian language in India that contains more letters among other Indian languages. The motivation behind our work was to develop an efficient character recognition technique for analyzing the scanned Tamil documents. Initially character recognition was done with the help of frequently used Tamil characters. Based on that, we have created a new datasets by the process of segmentation using levelsets, binarization, and skew correction. In this paper we have recognized the Tamil characters by generating the random features using Random Kitchen Sinks. It gives the accuracy about 98.7%. Copyright 2014 ACM.

关键词： Kitchens

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Filter design using sparse signal restoration techniques

Filter design using sparse signal restoration techniques

引用

2011 IEEE Recent Advances in Intelligent computational Systems, RAICS 2011

作者： Lakshmi, K. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781424494774

The paper proposes the design of linear phase filters using the optimization techniques based on methods for sparse signal restoration. The filter response is treated as weighted sum of the phase shifted versions of the signal. The filter design problem is converted into form Ax ≈ b, where A has rows given by [1 cos(-ω) cos(-2ω...) ], corresponding to a given frequency ω. x corresponds to the filter coefficients and b = R(ω) cos φ(ω), where R stands for the magnitude and φ the phase of the desired response. The problem is formulated into an L1 term and and L2 term and solved iteratively using ISTA (Iterative Soft Thresholding Algorithm [1]) to get the filter coefficients. The visible outcome of the approach is the considerable reduction in the non-zero coefficients. © 2011 IEEE.

关键词： Signal reconstruction

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AMRITA - CEN@NEEL : Identification and linking of twitter entities 5

AMRITA - CEN@NEEL : Identification and linking of twitter en...

引用

5th Workshop on Making Sense of Microposts: Big Things Come in Small Packages, #Microposts 2015, at the 24th International Conference on the World Wide Web, WWW 2015

作者： Barathi Ganesh, H.B. Abinaya, N. Anand Kumar, M. Vinayakumar, R. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

A short text gets updated every now and then. With the global upswing of such micro posts, the need to retrieve information from them also seems to be incumbent. This work focuses on the knowledge extraction from the micro posts by having entity as evidence. Here the extracted entities are then linked to their relevant DBpedia source by featurization, Part Of Speech (POS) tagging, Named Entity Recognition (NER) and Word Sense Disambiguation (WSD). This short paper encompasses its contribution to #Micropost2015 - NEEL task by experimenting existing Machine Learning (ML) algorithms. Copyright © 2015 held by author(s).

关键词： Learning systems

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AmritaCEN-NLP @ FIRE 2015 language identification for indian languages in social media text 7

AmritaCEN-NLP @ FIRE 2015 language identification for indian...

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7th Workshops at the Forum for Information Retrieval Evaluation, FIRE-WS 2015

作者： Rahul Venkatesh Kumar, R.M. Anand Kumar, M. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

The progression of social media contents, similar like Twitter and Facebook messages and blog post, has created, many new opportunities for language technology. The user generated contents such as tweets and blogs in most of the languages are written using Roman script due to distinct social culture and technology. Some of them using own language script and mixed script. The primary challenges in process the short message is identifying languages. Therefore, the language identification is not restricted to a language but also to multiple languages. The task is to label the words with the following categories L1, L2, Named Entities, Mixed, Punctuation and Others This paper presents the AmritaCen-NLP team participation in FIRE2015-Shared Task on Mixed Script Information Retrieval Subtask 1: Query Word Labeling on language identification of each word in text, Named Entities, Mixed, Punctuation and Others which uses sequence level query labelling with Support Vector Machine.

关键词： Information retrieval

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AMRITA-CEN@FIRE-2014: Named entity recognition for Indian languages using rich features 6

AMRITA-CEN@FIRE-2014: Named entity recognition for Indian la...

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6th International Workshop of the Forum for Information Retrieval Evaluation, FIRE 2014

作者： Abinaya, N. John, Neethu Barathi Ganesh, H.B. Anand Kumar, M. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781450337557

This paper aims at implementing Named Entity Recognition (NER) for four languages such as English, Tamil, Hindi and Malayalam. The results obtained from this work are submitted to a research evaluation workshop Forum for Information Retrieval and Evaluation (FIRE 2014). This system detects three levels of named entity tags which are referred as nested named entities. It is a multi-label problem solved using chain classifier method. In this work, Conditional Random Field (CRF) and Support Vector Machine (SVM) are used for implementing NER system. In FIRE 2014, we developed a English NER system using CRF and other NER system for Tamil, Hindi and Malayalam are based on SVM. The FIRE estimated the average precision for all the four languages as 41.93 for outermost level and 33.25 for inner *** order to improve the performance of Indian languages, we implemented CRF based NER system for the same corpus in Tamil, Hindi and Malayalam. The average precision measure for these mentioned languages are 42.87 for outer level and 36.31 for inner level. The overall performance of the NER system improved by 2.24% for outer level and 9.20% for inner level. © 2015 ACM.

关键词： Support vector machines

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AMRITA_CEN-NLP@SAIL2015: Sentiment analysis in indian language using regularized least square approach with randomized feature learning 3rd

AMRITA_CEN-NLP@SAIL2015: Sentiment analysis in indian langua...

引用

3rd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2015

作者： Sachin Kumar, S. Premjith, B. Anand Kumar, M. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783319268316

The present work is done as part of shared task in Sentiment Analysis in Indian Languages (SAIL 2015), under constrained category. The task is to classify the twitter data into three polarity categories such as positive, negative and neutral. For training, twitter dataset under three languages were provided Hindi, Bengali and Tamil. In this shared task, ours is the only team who participated in all the three languages. Each dataset contained three separate categories of twitter data namely positive, negative and neutral. The proposed method used binary features, statistical features generated from SentiWordNet, and word presence (binary feature). Due to the sparse nature of the generated features, the input features were mapped to a random Fourier feature space to get a separation and performed a linear classification using regularized least square method. The proposed method identified more negative tweets in the test data provided Hindi and Bengali language. In test tweet for Tamil language, positive tweets were identified more than other two polarity categories. Due to the lack of language specific features and sentiment oriented features, the tweets under neutral were less identified and also caused misclassifications in all the three polarity categories. This motivates to take forward our research in this area with the proposed method. © Springer International Publishing Switzerland 2015.

关键词： Sentiment analysis

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Machine learning approach for correcting preposition errors using SVD features 3

Machine learning approach for correcting preposition errors ...

引用

3rd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014

作者： Aravind, Anuja Kumar M., Anand Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781479930791

Non-native English writers often make preposition errors in English language. The most commonly occurring preposition errors are preposition replacement, preposition missing and unwanted preposition. So, in this method, a system is developed for finding and handling the English preposition errors in preposition replacement case. The proposed method applies 2-Singular Value Decomposition (SVD2) concept for data decomposition resulting in fast calculation and these features are given for classification using Support Vector Machines (SVM) classifier which obtains an overall accuracy above 90%. Features are retrieved using novel SVD2 based method applied on trigrams which is having a preposition in the middle of the context. A matrix with the left and right vectors of each word in the trigram is computed for applying SVD2 concept and these features are used for supervised classification. Preliminary results show that this novel feature extraction and dimensionality reduction method is the appropriate method for handling preposition errors. © 2014 IEEE.

关键词： Support vector machines

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Integer linear programming approach to dependency parsing for Malayalam

Integer linear programming approach to dependency parsing fo...

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International Conference on Recent Trends in Information, Telecommunication, and Computing, ITC 2010

作者： Aparnna, T. Raji, P.G. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore India

This paper is an attempt to show that a dependency parser for Malayalam language can be produced using Integer Linear Programming Approach. We describe a process for developing a parser by incorporating the Paninian G... 详细信息

ISBN: (纸本)9780769539751

关键词： Integer programming

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A cryptographic approach to video watermarking based on compressive sensing, Arnold transform, sum of absolute deviation and SVD

A cryptographic approach to video watermarking based on comp...

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2013 Annual International Conference on Emerging Research Areas, AICERA 2013 and 2013 International Conference on Microelectronics, Communications and Renewable Energy, ICMiCR 2013

作者： Jyothish Lal, G. Veena, V.K. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore-641112 India

ISBN: (纸本)9781467351492

Video watermarking is relatively a new technology to ensure protection of intellectual property rights and to stop video piracy. The ownership information or watermark is normally hidden in the video sequences. In this paper, a novel video watermarking method is proposed to protect the ownership information in a robust way. This method encrypts the watermark by combining the strengths of Compressive Sensing and Arnold scrambling. The frames for watermark embedding are chosen by computing the Sum of Absolute Deviation between successive frames. The cipher watermark is then embedded into chosen frames based on SVD watermark embedding algorithm. The decryption stage performs SVD watermark extraction algorithm, Arnold inverse transform and L1 optimization for retrieval of watermark. Experimental results show that proposed method enhances the security of the ownership information embedded. © 2013 IEEE.

关键词： Compressed sensing

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