M Tech Dissertations

Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3

Browse

Subject: search.filters.subject.deep learning

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    Shadow Detection and Removal from video using Deep Learning
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Dodiya, Krutika; Khare, Manish; Gohel, Bakul
    The removal of shadow from images is crucial in computer vision as it can enhancethe interpretability and visual quality of images. This research work proposesa cascade U-Net architecture for the shadow removal, consisting of twostages of U-Net Architecture. In the first stage, a U-Net is trained using theshadow images and their corresponding ground truth to predict the shadow freeimages. The second stage uses the predicted shadow free images and groundtruth as input to another U-Net, which further refines the shadow removal results.This cascade U-Net architecture enables the model to learn and refine theshadow removal progressively, leveraging both the initial predictions and groundtruth.Experimental evaluations on benchmark datasets demonstrate that our approachachieves notably good performance in both qualitative and quantitative evaluations.By using both objective metrics such as Structural Similarity Index(SSIM),and Root mean Square Error (RMSE), and subjective evaluations where humanobservers rate the quality of the shadow removal results, our approach was foundto outperform other state-of-the-art methods. Overall, our proposed cascade UNetarchitecture offers a promising solution for the shadow removal that canimprove image quality and interpretability
  • Thumbnail Image
    ItemOpen Access
    Time Series Forecasting using various Machine Learning Models
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2022) Shah, Varun; Joshi, Manjunath V.
    Analysis of time series data is a challenging task in recent times. Statistical analysis of time series data and forecasting with the help of past data is a requirement in current times. The industry is looking forward to accomplishing complete effectiveness in forecasting. There are several established techniques such as auto regressing (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) for univariate time series forecasting. For multivariate time series forecasting, the vector autoregression (VAR) model was used. With recent advances in deep learning techniques, prediction tasks can be effectively performed by a neural network and deep learning models can give better results than these established models. This study analyses and compares various established models with deep learning techniques on different datasets and explores whether transformers can be used for time series forecasting to get highly accurate results.
  • Thumbnail Image
    ItemOpen Access
    On the efficacy of deep image denoising for computer vision applications
    (2021) Shah, Manan Dharmendra; Kumar, Pankaj
    Image denoising is a process of inverse reconstruction where the original image is reconstructed from its noisy observations. Several deep learning models have been developed for image denoising. Usually the performance of image denoising is measured by metrics like peak signal to noise ratio (PSNR), structural similarity index (SSIM), however in this research we take a more pragmatic approach. We design and conduct experiments to evaluate the performance of deep image denoising methods in terms of improving the performance of some popular computer vision (CV) algorithms after image denoising. In this paper we have comparatively analysed: Fast and flexible denoising convolution neural network (CNN) (FFDNet), Feed forward denoising CNN (DnCNN) and Deep image prior (DIP) based image denoising. CV algorithms experimented with are face detection, face recognition and object detection. Standard and augmented datasets were used in our experiments. Raw images from standard datasets (BSDS500, LFW, FDDB and WGSID) were augmented with various kinds and levels of noise. From the results we obtained it can be concluded that image denoising is not effective in improving the performance of CV algorithms when denoising is applied to raw images of the datasets. But image denoising is very effective in improving the performance of the CV methods when denoising is applied to Gaussian noise corrupted images of the datasets. In our experiments we found results where the improvements were upto 11.70 percentage in terms of accuracy for face detection experiment.
  • Thumbnail Image
    ItemOpen Access
    Monkey detection using deep learning
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2019) Shingala, Mitalee; Kumar, Pankaj
    NA