Conference Papers

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    Image processing approach to diagnose eye diseases
    (Springer Verlag service@springer.de, 2017) Prashasthi, P.; Shravya, K.S.; Deepak, A.; Mulimani, M.; Shashidhar, K.G.
    Image processing and machine learning techniques are used for automatic detection of abnormalities in eye. The proposed methodology requires a clear photograph of eye (not necessarily a fundoscopic image) from which the chromatic and spatial property of the sclera and iris is extracted. These features are used in the diagnosis of various diseases considered. The changes in the colour of iris is a symptom for corneal infections and cataract, the spatial distribution of different colours distinguishes diseases like subconjunctival haemorrhage and conjunctivitis, and the spatial arrangement of iris and sclera is an indicator of palsy. We used various classifiers of which adaboost classifier which was found to give a substantially high accuracy i.e., about 95% accuracy when compared to others (k-NN and naive-Bayes). To enumerate the accuracy of the method proposed, we used 150 samples in which 23% were used for testing and 77% were used for training. © Springer International Publishing AG 2017.
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    Gender Detection using Handwritten Signatures
    (Institute of Electrical and Electronics Engineers Inc., 2018) Mohit Reddy, J.; Guru Pradeep Reddy, T.; Mishra, S.; Mulimani, M.; Koolagudi, S.G.
    In this paper, a method is proposed which uses both Image Processing and Machine Learning techniques which detects the gender of a person using handwritten signature. A photograph of a handwritten signature is given as input to the model which then extracts different features like pen pressure, slant angle, count external and internal contours etc. The features extracted from multiple images in the dataset are used to train the model, which then predicts the output of a new input given to it. Our objective is to collect unbiased datasets from a set of people and feed those signatures to the model, carrying out the statistical analysis and calculating the accuracy of the algorithm after every signature classification. We have used Adaboost classifier which gave a cross-validation accuracy of 73.2% compared to other classifiers like Gradient Boosting Classifier, Random Forest Trees and Multi-Layer Perceptron which gave 73.2%, 63.2% and 59.6% accuracies respectively. Copy Right © INDIACom-2018.