Residential, industrial, commercial, institutional and recreational activities discharge degradable and non-degradable wastes that reach the coastal water through rivers and cause coastal pollution. In the present study, mass transport of pollutants by Adyar and Cooum Rivers to the coastal water as a result of land-based discharges was estimated during low tide. The lowest and the highest flow recorded in Adyar varied from 514.59 to 2,585.08x10(6) litres/day. Similarly, the flow in Cooum River fluctuated between 266.45 and 709.34x10(6) litres/day. The present study revealed that the Adyar River transported 53.89-454.11 t/d of suspended solids, 0.06-19.64 t/d of ammonia, 15.95-123.24 t/d of nitrate and 0.4-17.86 t/d of phosphate, 0.004-0.09 kg/d of cadmium, 0.15-1.29 kg/d of lead and 3.03-17.58 kg/d of zinc to the coastal water owing to its high discharge. Similarly, the Cooum River transported 11.87-120.06 t/d of suspended solids, 0.08-58.7 t/d of ammonia, 6.11-29.25 t/d of nitrate and 0.66-10.73 t/d of phosphate, 0.003-0.021 kg/d of cadmium, 0.02-0.44 kg/d of lead and 1.36-3.87 kg/d of zinc. A higher concentration of suspended solids was noticed in post monsoon and summer months. An increase in the mass transport of ammonia, nitrate, phosphate in summer months (April and May) and an increase in the mass transport of cadmium, lead and zinc were observed in monsoon months (October-December) in both the rivers. Thus mass transport of pollutants study reveal that Cooum and Adyar Rivers in Chennai contribute to coastal pollution by transporting inorganic and trace metals significantly through land drainage.

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Human face detection techniques play an important role in applications like face recognition, video surveillance, human computer interface, face image database management, and querying image databases. Using color information in images is one of the various possible techniques usedfor face detection. This paper proposes a novel techniquefor detecting faces in color images using an adaptive threshold and template matching techniques. The goal of the technique is to segment the skin regions from the non-skin regions. Experimental results demonstrate successful face detection over a wide range offacial variations in color, position, scale, orientation, 3D pose, and expression in images from several photo collections (both indoors and outdoors). This method is quite practical andfaster when compared to neural networks and other techniques especially suited well for multimedia applications on the web.

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In this paper, a novel method for automated diagnosis of cervical cancer by extracting cytoplasm and nuclei from cervical cytology images is described. The background is removed by preprocessing methods like Edge sharpening and Adaptive Histogram Equalization. Fuzzy thresholding and Active contours are used for extracting the region of interest containing the cytoplasm and nuclei. The nuclei are separated from the cytoplasm using linear contrast stretching. The nucleus to cytoplasm ratio is used to determine the stage of cancer.