Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item 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.Item Extending Denoising AutoEncoders for Feature Recognition(Institute of Electrical and Electronics Engineers Inc., 2018) Jeppu, N.; Chandrasekaran, K.Image rendering techniques involve the addition of noise on the rendered samples. The characteristics of Monte Carlo rendered noisy images makes it difficult to denoise using conventional methods. The noise induced in this case is spatially sparse. Using traditional methods of denoising and feature recognition is time consuming for such images as they use the entire image space as their search space. This results in unnecessary calculations that can be avoided and therefore reduce the processing time significantly. A recurrent convolutional neural network that operates on varying spatial resolutions, also known as the auto encoder decoder structure perform very well on these rendered images. The partitioning into encoder and decoder phases lets the network operate on continuously decreasing and increasing spatial domains respectively. This can be coupled with classification techniques to incorporate feature recognition of noisy images. In this paper the pretrained autoencoder layers are supported with additional softmax and sigmoid layers to enable feature recognition capabilities. © 2018 IEEE.Item Performance evaluation of deep learning frameworks on computer vision problems(Institute of Electrical and Electronics Engineers Inc., 2019) Nara, M.; Mukesh, B.R.; Padala, P.; Kinnal, B.Deep Learning (DL) applications have skyrocketed in recent years and are being applied in various domains. There has been a tremendous surge in the development of DL frameworks to make implementation easier. In this paper, we aim to make a comparative study of GPU-accelerated deep learning software frameworks such as Torch and TenserFlow (with Keras API). We attempt to benchmark the performance of these frameworks by implementing three different neural networks, each designed for a popular Computer Vision problem (MNIST, CIFAR10, Fashion MNIST). We performed this experiment on both CPU and GPU(Nvidia GeForce GTX 960M) settings. The performance metrics used here include evaluation time, training time, and accuracy. This paper aims to act as a guide to selecting the most suitable framework for a particular problem. The special interest of the paper is to evaluate the performance lost due to the utility of an API like Keras and a comparative study of the performance over a user-defined neural network and a standard network. Our interest also lies in their performance when subjected to networks of different sizes. ©2019 IEEE.Item Depthwise Separable Convolutional Neural Network Model for Intra-Retinal Cyst Segmentation(Institute of Electrical and Electronics Engineers Inc., 2019) Girish, G.N.; Saikumar, B.; Roychowdhury, S.; Kothari, A.R.; Rajan, J.Intra-retinal cysts (IRCs) are significant in detecting several ocular and retinal pathologies. Segmentation and quantification of IRCs from optical coherence tomography (OCT) scans is a challenging task due to present of speckle noise and scan intensity variations across the vendors. This work proposes a convolutional neural network (CNN) model with an encoder-decoder pair architecture for IRC segmentation across different cross-vendor OCT scans. Since deep CNN models have high computational complexity due to a large number of parameters, the proposed method of depthwise separable convolutional filters aids model generalizability and prevents model over-fitting. Also, the swish activation function is employed to prevent the vanishing gradient problem. The optima cyst segmentation challenge (OCSC) dataset with four different vendor OCT device scans is used to evaluate the proposed model. Our model achieves a mean Dice score of 0.74 and mean recall/precision rate of 0.72/0.82 across different imaging vendors and it outperforms existing algorithms on the OCSC dataset. © 2019 IEEE.Item Automated Traffic Light Signal Violation Detection System Using Convolutional Neural Network(Springer, 2020) Bordia, B.; Nishanth, N.; Patel, S.; Anand Kumar, M.; Rudra, B.Automated traffic light violation detection system relies on the detection of traffic light color from the video captured with the CCTV camera, detection of the white safety line before the traffic signal and vehicles. Detection of the vehicles crossing traffic signals is generally done with the help of sensors which get triggered when the traffic signal turns red or yellow. Sometimes, these sensors get triggered even when the person crosses the line or some animal crossover or because of some bad weather that gives false results. In this paper, we present a software which will work on image processing and convolutional neural network to detect the traffic signals, vehicles and the white safety line present in front of the traffic signals. We present an efficient way to detect the white safety line in this paper combined with the detection of traffic lights trained on the Bosch dataset and vehicle detection using the TensorFlow object detection SSD model. © 2020, Springer Nature Singapore Pte Ltd.Item 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.Item 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.Item Cross Task Temporal Consistency for Semi-supervised Medical Image Segmentation(Springer Science and Business Media Deutschland GmbH, 2022) Jeevan, G.; Pawan, S.J.; Rajan, J.Semi-supervised deep learning for medical image segmentation is an intriguing area of research as far as the requirement for an adequate amount of labeled data is concerned. In this context, we propose Cross Task Temporal Consistency, a novel Semi-Supervised Learning framework that combines a self-ensembled learning strategy with cross-consistency constraints derived from the implicit perturbations between the incongruous tasks of multi-headed architectures. More specifically, the Signed Distance Map output of a teacher model is transformed to an approximate segmentation map which acts as a pseudo target for the student model. Simultaneously, the teacher’s segmentation task output is utilized as the objective for the student’s Signed Distance Map derived segmentation output. Our proposed framework is intuitively simple and can be plugged into existing segmentation architectures with minimal computational overhead. Our work focuses on improving the segmentation performance in very low-labeled data proportions and has demonstrated marked superiority in performance and stability over existing SSL techniques, as evidenced through extensive evaluations on two standard datasets: ACDC and LA. © 2022, Springer Nature Switzerland AG.Item Soil Type Identification via Deep Learning and Machine Learning Methods(Springer Science and Business Media Deutschland GmbH, 2024) Jalapur, S.; Patil, N.Soil type identification stands as a crucial concern in numerous countries, to ensure optimal crop yield, farmers need to accurately identify the suitable soil type for specific crops, which plays a significant role in meeting the heightened global food demand. The objective of this survey paper is to present a thorough and up-to-date overview of prevailing methodologies in soil identification, primarily focusing on image analysis, machine learning, and deep learning techniques. The paper initiates by highlighting the significance of soil identification and the limitations inherent in traditional methods. It then delves into the fundamental principles of image processing, deep learning, and spectroscopy, explaining how these techniques can be applied to soil identification. The survey presents an in-depth analysis of various image processing techniques employed for soil identification, including image segmentation, feature extraction, and classification algorithms. Furthermore, it discusses the application of deep learning models for soil classification based on image data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.