Conference Papers

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Author: search.filters.author.Gupta, S.K.

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    CVUCAMS: Computer vision based unobtrusive classroom attendance management system
    (Institute of Electrical and Electronics Engineers Inc., 2018) Gupta, S.K.; Ashwin, T.S.; Guddeti, R.M.
    One of the major challenges in a smart classroom environment is to develop a computer vision based unobtrusive classroom attendance management system. Traditional classroom environment follows a manual attendance marking system either by calling the student's names or by forwarding an attendance sheet; both interrupts the teaching-learning process and also consume a lot of time. Further, it can be erroneous due to factors such as students' proxy etc. In this paper, we propose an unobtrusive face recognition based smart classroom attendance management system using the high definition rotating camera for capturing the faces of students. The proposed system uses Max-Margin Face Detection (MMFD) technique for the face detection and the model is trained using the Inception-V3 CNN technique for the students' identification. The proposed smart classroom system was tested for a classroom with 20 students at National Institute of Technology Karnataka Surathkal, Mangalore, India and we got the experimental results demonstrate the train and test accuracy of 97.67% and 96.66%, respectively. © 2018 IEEE.
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    Optimal Selection of Bands for Hyperspectral Images Using Spectral Clustering
    (Springer Verlag service@springer.de, 2019) Gupta, V.; Gupta, S.K.; Shukla, D.P.
    High spectral resolution of hyperspectral images comes hand in hand with high data redundancy (i.e. multiple bands carrying similar information), which further contributes to high computational costs, complexity and data storage. Hence, in this work, we aim at performing dimensionality reduction by selection of non-redundant bands from hyperspectral image of Indian Pines using spectral clustering. We represent the dataset in the form of similarity graphs computed from metrics such as Euclidean, and Tanimoto Similarity using K-Nearest neighbor method. The optimum k for our dataset is identified using methods like Distribution Compactness (DC) algorithm, elbow plot, histogram and visual inspection of the similarity graphs. These methods give us a range for the optimum value of k. The exact value of clusters k is estimated using Silhouette, Calinski-Harbasz, Dunn’s and Davies-Bouldin Index. The value indicated by majority of indices is chosen as value of k. Finally, we have selected the bands closest to the centroids of the clusters, computed by using K-means algorithm. Tanimoto similarity suggests 17 bands out of 220 bands, whereas the Euclidean metric suggests 15 bands for the same. The accuracy of classified image before band selection using support vector machine (SVM) classifier is 76.94% and after band selection is 75.21% & 75.56% for Tanimoto and Euclidean matrices respectively. © 2019, Springer Nature Singapore Pte Ltd.