Hemalatha Thiagarajan

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Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. This paper uses gray level cooccurrence matrix method(More)
Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. Many different approaches and feature extraction methodologies concerning scene classification have been proposed and applied in the last few years. In real time environments, we prefer a(More)
Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. This paper is classifying the scenes using support(More)
Thousands of images are generated every day, which implies the necessity to classify, organize and access them using an easy, faster and efficient way. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. Many different approaches(More)
Insect behavioral studies are time consuming but essential in pest management. Tracking different kinds of insects is an integral part of these studies. In this paper, we present a new approach to track multiple insects especially the larva which is elastomeric in nature. This study demonstrates the use of image processing techniques for segmenting insects,(More)
Data Mining approaches have been widely applied in the field of healthcare to facilitate the quality diagnosis. The physiologic state and the complexity of disease of a patient is monitored through diverse variety of symptoms and laboratory test measurements. Data acquisition in healthcare systems is voluminous; they come from many different sources, not(More)
In the present study, attempts are made to capture and track coconut black headed caterpillar, Opisina arenosella and its parasitoid, Goniozus nephantidis with respect to their path and orientation. We devised an automatic tracking system using Artificial Neural Network for tracking both insects. The tracking system is based on the extracted features of the(More)