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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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International Conference on Communications and Signal Processing

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

ISBN: (纸本)9781467385497

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.

关键词： Histograms Adaptive equalizers Discrete wavelet transforms Discrete cosine transforms Satellites Image restoration Standards

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Aerial image classification using GURLS and LIBSVM

Aerial image classification using GURLS and LIBSVM

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International Conference on Communications and Signal Processing

作者： Aleena Ajay K Deepa Merlin Dixon V. Sowmya K. P. Soman Centre for Excellence in Computational Engineering and Networking Coimbatore Tamil Nadu Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu IN

ISBN: (纸本)9781467385497

Image classification using kernels have very great importance in remote sensing data. The goal of this work is to efficiently classify the large set of aerial images into different classes. This paper introduces a kernel based classification for aerial images. It uses Grand Unified Regularized Least Square (GURLS) and library for support vector machines (LIBSVM). This paper compares the performance of different kernel methods used in GURLS and LIBSVM. The experiment is performed on three sets of aerial image data sets which are obtained from electrical engineering department of Banja Luka University under the DSP laboratory, funded by the WUSAUSTRIA project of the European Union. From the experiment performed, it can be deduced that GURLS library is better compared to LIBSVM in terms of its prediction accuracy. The advantage of GURLS library package over LIBSVM is its automatic parameter selection.

关键词： Kernel Support vector machines Training Feature extraction Libraries Testing Classification algorithms

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Impact of orbital perturbations on LEO solar power satellite formation in a wireless broadband over power system

Impact of orbital perturbations on LEO solar power satellite...

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International Conference on Communications and Signal Processing

作者： M. Aswathi G. A. Shanmugha Sundaram K. P. Soman Centre for Excellence Computational engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu India Amrita Vishwa Vidyapeetham Coimbatore Tamil Nadu IN

ISBN: (纸本)9781467385497

Increasing power requirements lead the way for the development of new innovative ideas to generate electricity like the introduction of solar power satellites. This system consists of a number of satellites such that the maximum solar energy is collected. This energy is then converted to electromagnetic beams and transmitted to Earth wirelessly. Simultaneous wireless power and data transmission is performed for better efficiency. There are some external forces which act on the satellite formation, as a result of which they are deviated from their orbit. The orbital elements of the formation and the perturbing forces which they are acted upon are determined. A periodic variation on the orbital elements is observed. The receiver on the Earth station, called the rectenna, is designed to collect both wireless power and data simultaneously. A novel design for the solar power satellite formation in a low earth orbit has been proposed.

关键词： Earth Orbits Rectennas Low earth orbit satellites Moon Satellite broadcasting

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

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

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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-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...

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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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AMRITA-CEN@SAIL2015: Sentiment analysis in Indian languages 3rd

AMRITA-CEN@SAIL2015: Sentiment analysis in Indian languages

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

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

ISBN: (纸本)9783319268316

The contemporary work is done as slice of the shared task in Sentiment Analysis in Indian Languages (SAIL 2015), constrained variety. Social media allows people to create and share or exchange opinions based on many perspectives such as product reviews, movie reviews and also share their thoughts through personal blogs and many more platforms. The data available in the internet is huge and is also increasing exponentially. Due to social media, the momentousness of categorizing these data has also increased and it is very difficult to categorize such huge data manually. Hence, an improvised machine learning algorithm is necessary for wrenching out the information. This paper deals with finding the sentiment of the tweets for Indian languages. These sentiments are classified using various features which are extracted using words and binary features, etc. In this paper, a supervised algorithm is used for classifying the tweets into positive, negative and neutral labels using Naive Bayes classifier. © Springer International Publishing Switzerland 2015.

关键词： Sentiment analysis

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Author identification based on word distribution in word space

Author identification based on word distribution in word spa...

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International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015

作者： Barathi Ganesh, H.B. Reshma, U. Anand Kumar, M. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Coimbatore641112 India

ISBN: (纸本)9781479987917

Author attribution has grown into an area that is more challenging from the past decade. It has become an inevitable task in many sectors like forensic analysis, law, journalism and many more as it helps to detect the author in every documentation. Here unigram/bigram features along with latent semantic features from word space were taken and the similarity of a particular document was tested using Random forest tree, Logistic Regression and Support Vector Machine in order to create a global model. Dataset from PAN Author Identification shared task 2014 is taken for processing. It has been observed that the proposed model shows state-of-art accuracy of 80% which is significantly greater when compared to the Author Identification PAN results of the year 2014. © 2015 IEEE.

关键词： Support vector machines

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AMRITA-CEN@FIRE2015: Automated story illustration using word embedding 7

AMRITA-CEN@FIRE2015: Automated story illustration using word...

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

作者： Sanjay, S.P. Ezhilarasan, Nivedhitha Anand Kumar, M. Soman, K.P. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Ettimadai Coimbatore India

Story books are copiously filled with image illustration in which the illustrations are essential to the enjoyment and understanding of the story. Often the photos themselves turn out to be more important than content. In such cases, our principle job is to locate the best pictures to show. Stories composed for kids must be improved with pictures to manage the enthusiasm of a tyke, for words usually can't do a picture justice. This system is built as a part of shared task of Forum of Information Retrieval and Evaluation (FIRE) 2015 workshop. In this system we provide a methodology for automatically illustrating a given Children's story using the Wikipedia ImageCLEF 2010 dataset, with appropriate images for better learning and understanding.

关键词： Image retrieval

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AMRITA-CEN-NLP@FIRE 2015:CRF based named entity extraction for Twitter microposts 7

AMRITA-CEN-NLP@FIRE 2015:CRF based named entity extraction f...

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

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

This proposed method implements the Named Entity Recognition (NER) for four dialects Such as English, Tamil, Malayalam, and Hindi. The results obtained from this work are submitted to a research evaluation workshop Forum for Information Retrieval and Evaluation (FIRE 2015). It is single-layered problem which is divided into multi-layered this step is called pre-processing;it has three levels of named entity tags which are referred as BIO format. This format is trained using Condition Random field(CRF) are used for implementing in NER system, the results obtained are grouped back to single-label or single-tagged referred as Format converting. In FIRE 2015, we developed English, Tamil, Malayalam, and Hindi NER system using CRF. The FIRE estimated the average precision for all the four languages.

关键词： Extraction

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