“Studies on the Biology, Morphometrics and Biochemical composition of the Ommastrephid squid, Sthenoteuthis oualaniensis (Lesson,1830) of the south west coast of India”

With the stabilization of world finfish catches in general, and the depletion of a number of fish stocks that used to support industrial-scale fisheries, increasing attention is now being paid, to the so-called unconventional marine resources, which include many species of cephalopods. One of such important cephalopod resource is the tropical Indo-Pacific pelagic oceanic squid Sthenoteuthis oualaniensis. It is the most abundant large sized squid in the Indo- Pacific region with an estimated biomass of 8-11 metric tons. However, its distribution, biology, life cycle and nutrient value in the south west coast of India are still poorly known. So any new information of this species in the waters off the south west coast of India has important scientific significance for effective and rational utilization of this Oceanic fishery resources, especially during the time of depletion of shallow water resources. In view of that this study investigated different aspects of the Sthenoteuthis oualaniensis, such as morphometry, growth, mortality, maturation, spawning, food, feeding and biochemical composition in the south west coast of India to understand its possible prospective importance for commercial fishing and management of its fishery

Offline Handwritten Malayalam Character Recognition Based on Chain Code Histogram

Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification

Content Based Image Retrieval System for Malayalam Handwritten Characters

Content Based Image Retrieval is one of the prominent areas in Computer Vision and Image Processing. Recognition of handwritten characters has been a popular area of research for many years and still remains an open problem. The proposed system uses visual image queries for retrieving similar images from database of Malayalam handwritten characters. Local Binary Pattern (LBP) descriptors of the query images are extracted and those features are compared with the features of the images in database for retrieving desired characters. This system with local binary pattern gives excellent retrieval performance