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Image fusion is defined as the process of combining two or more different images into a new single image retaining important features from each image with extended information content. The result of image fusion is a new image which is more suitable for human and machine perception or further image processing tasks such as segmentation, feature extraction and object recognition. This paper presents review on some of the image fusion techniques i.e. simple average, simple maximum, PCA, DCT, DWT. Comparative study of all these techniques concludes that DWT is better approach.

In olden days people were only information consumers but since advent of Web 2.0 they plays more important role in publishing information on Web in the form of comments and reviews. The user generated content forced organization to pay attention towards analyzing this content for better visualization of public's opinion. Opinion mining or Sentiment analysis is an autonomous text analysis and summarization system for reviews available on Web. Opinion mining aims for distinguishing the emotions expressed within the reviews, classifying them into positive or negative and summarizing into the form that is quickly understood by users. Feature based opinion mining performs fine-grain analysis by recognizing individual features of an object upon which user has expressed opinion. This paper gives an insight of various methods proposed in the area of feature based opinion mining and also discuss the limitations of existing work and future direction in feature based opinion mining.

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