Content Based Image Retrieval Using Low Level Features of Automatically Extracted Regions of Interest

This paper proposes a region based image retrieval system using the local colour and texture features of image sub regions. The regions of interest (ROI) are roughly identified by segmenting the image into fixed partitions, finding the edge map and applying morphological dilation. The colour and texture features of the ROIs are computed from the histograms of the quantized HSV colour space and Gray Level co- occurrence matrix (GLCM) respectively. Each ROI of the query image is compared with same number of ROIs of the target image that are arranged in the descending order of white pixel density in the regions, using Euclidean distance measure for similarity computation. Preliminary experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

Salient Features of the North Indian Ocean Associated with the Indian summer Monsoon

This study deals with the salient features of the north Indian ocean associated with the summer monsoon. The focus is given on the Arabian sea mini warm pool, which is a part of the Indian ocean. It primarily study the certain aspects of the atmosphere and ocean variability in the north Indian ocean. The attempt were made to understand various aspects of time –scale variability of major features occurring in the Indian summer monsoon. The result from the thesis can be utilized as an input for model studies for prediction of monsoon, understanding ocean dynamics, radar tracking and ranging etc.

Salient Features of the North Indian Ocean Associated with the Indian summer Monsoon

This study deals with the salient features of the north Indian ocean associated with the summer monsoon. The focus is given on the Arabian sea mini warm pool, which is a part of the Indian ocean. It primarily study the certain aspects of the atmosphere and ocean variability in the north Indian ocean. The attempt were made to understand various aspects of time –scale variability of major features occurring in the Indian summer monsoon. The result from the thesis can be utilized as an input for model studies for prediction of monsoon, understanding ocean dynamics, radar tracking and ranging etc.

Spectral features of laser induced plasma from YBa2Cu3O7 and GdBa2Cu3O7 highTc superconductors

Laser induced plasma emission spectra from highT c superconducting samples of YBa2Cu3O7 and GdBa2Cu3O7 obtained with 1.06µm radiation from a Q switched Nd:YAG laser beam has been analysed. The results clearly show the presence of diatomic oxides in addition to ionized species of the constituent metals in the plasma thus produced.

Southern Hemispheric Features and their Teleconnection with Indian Summer Monsoon

This thesis entitled southern hemispheric features and their Teleconnection with indian summer monsoon.Southern hemisphere is entirely distinct from the northern hemisphere in many aspects, which is well reflected in atmospheric and oceanic properties.The thesis consists of eight chapters, in which the first chapter contains an overview of southern hemisphere. In this chapter, variability in southern hemisphere is described along with Indian summer monsoon and its teleconnection. The different types of data sets used and various methodologies adopted in the present thesis were described in Chapter 2. The period of climate shift and the magnitude of anomalies after the climate shift, which extended from troposphere to stratopause level, were investigated in detail and presented in chapter 3. Chapter 4 depicts the recent trend and variability in southern stratosphere. The higher order variability during various months and the frequency of extremity is included in this chapter.Climatology of divergence and convergence after the documented shift is reported in chapter 5.Southern extratropical connection to Indian summer monsoon through the modulation of SAM is presented in Chapter 6.Chapter 7 deals with the modulation of SAM‐Monsoon link through North Atlantic Oscillation.

A study of the hydrological features of the shelf water along the west coast of India with an attempt to explain their influence upon the living resources of the region

The objective of the present study is to elucidate the hydrological conditions of the shelf waters along the southern half or the west coast of India and their relation to the sooplankton bionase and pelagic fish resources. Data from six hydrography-plankton sections worked during the 1972-75 period of cape camerin. Quilon. cochin. Kasaragod. Karwar and kotnagiri formed the basis of the present study.Stations were fixed along the transects 10 nautical miles apart. Starting with the first station at around 15 metre depth and were usually occupied 5 to 8 times in an year at an interval of about 6 weeks. Data relating to oil sardines and macherel fisheries were availed from published information relating to the period mainly of the Central Marine fisheries Research Institute. The range of different parameters namely temperature. salinity. density and dissolved oxygen at different depths and their sloping features against the coast are discussed. Three seasons. namely south-west monsoon (summer monsoon). north-east monsoon (winter monsoon) and hot weather season are designated and data of the core months of each of these seasons considered in the study.

Application of ocean colour data to study the oceanographic and atmospheric features off the southwest coast of India with special reference to upwelling

Satellite remote sensing is being effectively used in monitoring the ocean surface and its overlying atmosphere. Technical growth in the field of satellite sensors has made satellite measurement an inevitable part of oceanographic and atmospheric research. Among the ocean observing sensors, ocean colour sensors make use of visible band of electromagnetic spectrum (shorter wavelength). The use of shorter wavelength ensures fine spatial resolution of these parameters to depict oceanographic and atmospheric characteristics of any region having significant spaio-temporal variability. Off the southwest coast of India is such an area showing very significant spatio-temporal oceanographic and atmospheric variability due to the seasonally reversing surface winds and currents. Consequently, the region is enriched with features like upwelling, sinking, eddies, fronts, etc. Among them, upwelling brings nutrient-rich waters from subsurface layers to surface layers. During this process primary production enhances, which is measured in ocean colour sensors as high values of Chl a. Vertical attenuation depth of incident solar radiation (Kd) and Aerosol Optical Depth (AOD) are another two parameters provided by ocean colour sensors. Kd is also susceptible to undergo significant seasonal variability due to the changes in the content of Chl a in the water column. Moreover, Kd is affected by sediment transport in the upper layers as the region experiences land drainage resulting from copious rainfall. The wide range of variability of wind speed and direction may also influence the aerosol source / transport and consequently AOD. The present doctoral thesis concentrates on the utility of Chl a, Kd and AODprovided by satellite ocean colour sensors to understand oceanographic and atmospheric variability off the southwest coast of India. The thesis is divided into six Chapters with further subdivisions

An Improved Color Video Super-Resolution Using Kernel Regression and Fuzzy Enhancement

An improved color video super-resolution technique using kernel regression and fuzzy enhancement is presented in this paper. A high resolution frame is computed from a set of low resolution video frames by kernel regression using an adaptive Gaussian kernel. A fuzzy smoothing filter is proposed to enhance the regression output. The proposed technique is a low cost software solution to resolution enhancement of color video in multimedia applications. The performance of the proposed technique is evaluated using several color videos and it is found to be better than other techniques in producing high quality high resolution color videos

Grey Level Co-Occurrence Matrices: Generalisation And Some New Features

Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR.

Identifying Decisive Features for Distinctive Analysis of Writings in Malayalam

This paper presents a writer identification scheme for Malayalam documents. As the accomplishment rate of a scheme is highly dependent on the features extracted from the documents, the process of feature selection and extraction is highly relevant. The paper describes a set of novel features exclusively for Malayalam language. The features were studied in detail which resulted in a comparative study of all the features. The features are fused to form the feature vector or knowledge vector. This knowledge vector is then used in all the phases of the writer identification scheme. The scheme has been tested on a test bed of 280 writers of which 50 writers having only one page, 215 writers with at least 2 pages and 15 writers with at least 4 pages. To perform a comparative evaluation of the scheme the test is conducted using WD-LBP method also. A recognition rate of around 95% was obtained for the proposed approach

The Effect of SIFT Features as Content Descriptors in the Context of Automatic Writer Identification in Malayalam Language

The span of writer identification extends to broad domes like digital rights administration, forensic expert decisionmaking systems, and document analysis systems and so on. As the success rate of a writer identification scheme is highly dependent on the features extracted from the documents, the phase of feature extraction and therefore selection is highly significant for writer identification schemes. In this paper, the writer identification in Malayalam language is sought for by utilizing feature extraction technique such as Scale Invariant Features Transform (SIFT).The schemes are tested on a test bed of 280 writers and performance evaluated

Texture Description of low grade and high grade Glioma using Statistical features in Brain MRIs

Low grade and High grade Gliomas are tumors that originate in the glial cells. The main challenge in brain tumor diagnosis is whether a tumor is benign or malignant, primary or metastatic and low or high grade. Based on the patient's MRI, a radiologist could not differentiate whether it is a low grade Glioma or a high grade Glioma. Because both of these are almost visually similar, autopsy confirms the diagnosis of low grade with high-grade and infiltrative features. In this paper, textural description of Grade I and grade III Glioma are extracted using First order statistics and Gray Level Co-occurance Matrix Method (GLCM). Textural features are extracted from 16X16 sub image of the segmented Region of Interest(ROI) .In the proposed method, first order statistical features such as contrast, Intensity , Entropy, Kurtosis and spectral energy and GLCM features extracted were showed promising results. The ranges of these first order statistics and GLCM based features extracted are highly discriminant between grade I and Grade III. In this study which gives statistical textural information of grade I and grade III Glioma which is very useful for further classification and analysis and thus assisting Radiologist in greater extent.

Automatic Detection and Classification of Glioma Tumors using Statistical Features

The characterization and grading of glioma tumors, via image derived features, for diagnosis, prognosis, and treatment response has been an active research area in medical image computing. This paper presents a novel method for automatic detection and classification of glioma from conventional T2 weighted MR images. Automatic detection of the tumor was established using newly developed method called Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA).Statistical Features were extracted from the detected tumor texture using first order statistics and gray level co-occurrence matrix (GLCM) based second order statistical methods. Statistical significance of the features was determined by t-test and its corresponding p-value. A decision system was developed for the grade detection of glioma using these selected features and its p-value. The detection performance of the decision system was validated using the receiver operating characteristic (ROC) curve. The diagnosis and grading of glioma using this non-invasive method can contribute promising results in medical image computing