Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Comparing CNNs and GANs for Image Completion(Institute of Electrical and Electronics Engineers Inc., 2021) Saji, R.; Anand, S.K.; Chandavarkar, B.R.Imperfections or defects inevitably occur in images due to inexperienced photographers, inadequate methods of preservation, or even some deliberate hacking. Image restoration or completion has been performed using various manual methods in the past, be it being drawn by artists based on their creativity or deleting noise and blur effects using software like Photo-shop. On a large scale, manual image completion is infeasible and has quite a lot of limitations. Modern advancements in Computer Vision and Deep Learning have allowed man to automate such tasks with high efficiency. Manual restoration usually relies on prior experience in the subject and sometimes even creativity to reconstruct the image based on the artist's imagination. At the same time, deep learning produces excellent results given enough training data. Deep learning methods can improvise and generalize better too and hence outperform the traditional manual methods. In this project, image completion is performed using 2 Deep Learning models - Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN). Adversarial Networks have been proven to be very handy in image to image translation tasks and image reconstruction and hence this is explored widely. Both Deep Convolutional GANs as well as Conditional GANs are used for this task and their respective performances are compared for the above task. © 2021 IEEE.Item A Survey on Semantic Segmentation Models for Underwater Images(Springer, 2023) Anand, S.K.; Kumar, P.V.; Saji, R.; Gadagkar, A.V.; Chandavarkar, B.R.Semantic segmentation remains a key research field in modern day computer vision and has been used in a myriad of applications across various fields. It can be extremely beneficial in the study of underwater scenes. Various underwater applications, such as unmanned explorations and autonomous underwater vehicles, require accurate object classification and detection to allow the probes to avoid malicious objects. However, the models that work well for terrestrial images rarely work just as well for underwater images. This is because underwater images suffer from high blue light intensity as well as other ill effects such as poor lighting and contrast. This can be fixed using preprocessing techniques to manually improve the image characteristics. Trying to improve the model to account for bad image quality is not a great method as the model may misidentify noise as an image characteristic. In this chapter, 6 different deep learning semantic segmentation models—SegNet, Pyramid Scene Parsing Network (PSP-Net), U-Net, DNN-VGG (Deep Neural Network-VGG), DeepLabv3+, and SUIM-Net—are explored. Their architectures, technical aspects with respect to underwater images, advantages, and disadvantages are all investigated. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Semantic Segmentation of Underwater Images with CNN Based Adaptive Thresholding(Springer Science and Business Media Deutschland GmbH, 2025) Anand, S.K.; Kumar, P.V.; Saji, R.; Gadagkar, A.V.; Chandavarkar, B.R.Semantic segmentation remains a key research field in modern day computer vision and has been used in a myriad of applications across various fields. It can be extremely beneficial in the study of underwater scenes. Various underwater applications, like unmanned explorations and autonomous underwater vehicles, require accurate object classification and detection to allow the probes to avoid malicious objects. However, the models which work well for terrestrial images rarely work just as well for underwater images. This is because underwater images suffer from high blue light intensity as well as other ill-effects such as poor lighting and contrast. Trying to improve the model to account for bad image quality is not a great method as the model may misidentify noise as an image characteristic. In this paper, a unique CNN-based approach for post-processing image thresholding is proposed, on top of 3 models used for the semantic segmentation itself–Segnet, U-Net, and Deeplabv3+. The models’ outputs are then subject to the CNN-based post-processing technique to binarize the outputs into masks, and provides improved segmentation results compared to the base models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.