Texture Mineralogy and Geochemistry Of Sediments From The Coastal Plains Between Kodungallur And Chellanam Central Kerala India

The present study addresses to understand the sedimentological properties of the coasts of kodungallur and chellanam, central Kerala to bring out the relationship between the textural, mineralogical and geochemical characters with that of the respective environment. The grain size study of the beach ridge sediments from different pits has been investigated at close intervals, which enables to understand the grain size variations with depth. The sediment samples from various pits of the beach ridges indicate that the sediments range primarily from medium to very fine sand, well to moderately sorted, fine to coarse skewed and leptokurtic to platykurtic. The study area is considered as a prograding coast. Variations in grain size down the pit give three phases of beach building activities i.e.; a coarsening upward sequence in the bottom layers, a fining upward in the middle and coarsening upward in the top. Beach ridges are formed by swash built sediments with cross bedding and setting lag type sediments with seaward dipping/horizontal units. Geochemical signatures in the study area have been brought out through the analysis of major and trace elements. Iron is significantly enriched and its control over many trace elements is evident. Copper, chromium, cobalt, lithium, lead and zinc show decreasing trend with depth, while sodium, potassium,strontium,nickel and organic carbon increases. The association of many trace elements with organic carbon has also been established. Dissolution of trace elements in anoxic environment, at depth and reprecipitation in the oxic layers, at near or subsurface, are the major mechanism that brought out the variation of certain environmentally sensitive elements

CBIR Using Local and Global Properties of Image Sub-blocks

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods