G. Thippeswamy

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Every year farmers experience huge losses due to pest infestation in crops & this inturn impacts his livelihood. In this paper we discuss a novel approach to solve this problem by constantly monitoring crops using video processing, cloud computing and robotics. The paper concentrates in methodologies to detect pests in one of the most popular fruits in(More)
In this paper, we propose a robust and an accurate face recognition model which uses the feature extraction capabilities of fractional discrete cosine transform (FDCT). We apply FDCT on the face image database, and use transform coefficients as features. The proposed model is tested on publicly available standard AT&T face database to demonstrate the(More)
In this paper, we have presented a new face recognition algorithm based on region covariance matrix (RCM) descriptor computed in monogenic scale space. In the proposed model, energy information obtained using monogenic filter is used to represent a pixel at different scales to form region covariance matrix descriptor for each face image during training(More)
This research work focuses on to the development of neural network based detection and characterization of electrocardiogram (ECG) and electroencephalogram (EEG) signal. ECG and EEG signals have prime importance for patients under critical care. These signals have to be continuously monitored and processed as they are inter dependent. In this research(More)
There are countless plant species available globally. To manage massive content, development of a fast and effective categorization methods has turned into a territory of dynamic research. As trees and plants are very important to ecology, accurate Identification and classification becomes necessary. Classification procedure is carried out through number of(More)
In this paper, we investigate whether the rule based system derived from edges are effective and efficient for face representation and recognition when the number of classes is fixed and known. The characteristics of edges namely straightness and crookedness are used to derive rules. During training, a representative feature value is calculated for each(More)
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