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<p>The area of image processing has made rapid strides because of enormous applications it has in different fields. This growth can also be attributed to the increasing use of fuzzy logic in all tasks of image processing as the fuzzy logic facilitates the representation of inherent uncertainty in the image information which can be local or global. For problems like enhancement global information is of interest whereas the local information is needed for the problems of edge detection, segmentation, and recognition. However we need both for the noise removal. The images are of varied types: Medical images (CT scans, MR, X-rays, ECG, etc.), satellite images, natural scenes, videos, games, multimedia, biometrics, industrial, astronomical so on and so forth. The approaches to tackle different images have to be different. For example skin texture can't represented by a colour model.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.405-407</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1192</strong></strong></p>

<p>Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.415-430</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1177</strong></strong></p>

<p>This paper presents the finger knuckle based biometric authentication system using the approaches like structure entropy, GSHP (Gaussian smoothed High pass), GSOD (Gaussian Smoothed Oriented Directives) and also the well known method for surface roughness measurement called the fractal profiles represented by Topothesy and fractal dimension which describe not only the roughness but also the affine self similarity. We have also implemented Daisy descriptor for the representation of texture. The results of fractal parameters along with the refined scores are comparable to those of the compcode and impcompcode.</p>

<p>In this paper an attempt has been made to detect the face using the combination of integral image along with the cascade structured classifier which is built using Adaboost learning algorithm. The detected faces are then passed through a filtering process for discarding the non face regions. They are individually split up into five segments consisting of forehead, eyes, nose, mouth and chin. Each segment is considered as a separate image and Eigenface also called principal component analysis (PCA) features of each segment is computed. The faces having a slight pose are also aligned for proper segmentation. The test image is also segmented similarly and its PCA features are found. The segmental Euclidean distance classifier is used for matching the test image with the stored one. The success rate comes out to be 88 per cent on the CG(full) database created from the databases of California Institute and Georgia Institute. However the performance of this approach on ORL(full) database with the same features is only 70 per cent. For the sake of comparison, DCT(full) and fuzzy features are tried on CG and ORL databases but using a well known classifier, support vector machine (SVM). Results of recognition rate with DCT features on SVM classifier are increased by 3 per cent over those due to PCA features and Euclidean distance classifier on the CG database. The results of recognition are improved to 96 per cent with fuzzy features on ORL database with SVM.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.431-442</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1178</strong></strong></p>