Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Koolagudi, S.G.Subject: search.filters.subject.Convolutional neural networks

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    Food classification from images using convolutional neural networks
    (Institute of Electrical and Electronics Engineers Inc., 2017) Attokaren, D.J.; Fernandes, I.G.; Sriram, A.; Vishnu Srinivasa Murthy, Y.V.; Koolagudi, S.G.
    The process of identifying food items from an image is quite an interesting field with various applications. Since food monitoring plays a leading role in health-related problems, it is becoming more essential in our day-to-day lives. In this paper, an approach has been presented to classify images of food using convolutional neural networks. Unlike the traditional artificial neural networks, convolutional neural networks have the capability of estimating the score function directly from image pixels. A 2D convolution layer has been utilised which creates a convolution kernel that is convolved with the layer input to produce a tensor of outputs. There are multiple such layers, and the outputs are concatenated at parts to form the final tensor of outputs. We also use the Max-Pooling function for the data, and the features extracted from this function are used to train the network. An accuracy of 86.97% for the classes of the FOOD-101 dataset is recognised using the proposed implementation. © 2017 IEEE.
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    Efficient Traffic Signboard Recognition System Using Convolutional Networks
    (Springer, 2020) Mothukuri, S.K.P.; Tejas, R.; Patil, S.; Darshan, V.; Koolagudi, S.G.
    In this paper, a smart automatic traffic sign recognition system is proposed. This signboard recognition system plays a vital role in the automated driving system of transport vehicles. The model is built based on convolutional neural network. The German Traffic Sign Detection Benchmark (GTSDB), a standard open-source segmented image dataset with forty-three different signboard classes is considered for experimentation. Implementation of the system is highly focused on processing speed and classification accuracy. These aspects are concentrated, such that the built model is suitable for real-time automated driving systems. Similar experiments are carried in comparison with the pre-trained convolution models. The performance of the proposed model is better in the aspects of fast responsive time. © Springer Nature Singapore Pte Ltd. 2020.
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    Kannada Dialect Classification Using CNN
    (Springer Science and Business Media Deutschland GmbH, 2020) Hegde, P.; Chittaragi, N.B.; Mothukuri, S.K.P.; Koolagudi, S.G.
    Kannada is one of the prominent languages spoken in southern India. Since the Kannada is a lingua franca and spoken by more than 70 million people, it is evident to have dialects. In this paper, we identified five major dialectal regions in Karnataka state. An attempt is made to classify these five dialects from sentence-level utterances. Sentences are segmented from continuous speech automatically by using spectral centroid and short term energy features. Mel frequency cepstral coefficient (MFCC) features are extracted from these sentence units. These features are used to train the convolutional neural networks (CNN). Along with MFCCs, shifted delta and double delta coefficients are also attempted to train the CNN model. The proposed CNN based dialect recognition system is also tested with internationally known standard Intonation Variation in English (IViE) dataset. The CNN model has resulted in better performance. It is observed that the use of one convolution layer and three fully connected layers balances computational complexity and results in better accuracy with both Kannada and English datasets. © 2020, Springer Nature Switzerland AG.