M Tech Dissertations

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

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Subject: search.filters.subject.Imaging systems

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Now showing 1 - 4 of 4
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    Practical approach for depth estimation and image restoration using defocus cue
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Ranipa, Keyur R.; Joshi, Manjunath V.
    Reconstruction of depth from 2D images is an important research issue in computer vision. Depth from defocus (DFD) technique uses space varying blurring of an image as a cue in reconstructing the 3D structure of a scene. In this thesis we explore the regularization based approach for simultaneous estimation of depth and image restoration from defocused observations. We are given two defocused observations of a scene that are captured with di erent camera parameters. Our method consists of two steps. First we obtain the initial estimates for the depth as well as for the focused image. In the second step we re ne the solution by using a fast optimization technique. Here we use the classic depth recovery method due to Subbarao for ob- taining the initial depth map and Wiener lter approach for initial image restoration. Since the problem we are solving is ill-posed and does not yield unique solution, it is necessary to regularize the solution by imposing additional constraint to restrict the solution space. The regularization is performed by imposing smoothness constraint only. However, for preserving the depth and image intensity discontinuities, they are indenti ed prior to the minimization process from initial estimates of the depth map and the restored image. The nal solution is obtained by using computationally e client gradient descent algorithm, thus avoiding the need for computationally taxing algorithms. The depth as well as intensity edge details of the nal solution correspond to those obtained using the initial estimates. The experimental results indicate that the quality of the restored image is found to be satisfactory even under severe space-varying blur conditions.
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    Single frame superresolution
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2008) Sattaru, Annamnaidu; Joshi, Manjunath V.
    Super-resolving an image from single frame observation image. In many cases more than one low resolution observations may not be available, need high spatial resolution images e.g. medical imaging, remote sensing etc.. We obtain the estimate of the high frequency (edges) contents by learning the wavelet coefficients from a database of similar or arbitrary high resolution images. We then employ a suitable regularization approach for edge preservation as well as for ensuring spatial continuity among pixels. The learnt wavelet coefficients are used as edge prior. An Markov Random Field (MRF) model is used for spatial dependence. The final cost function consists of data fitting term and two regularization terms, which is minimized by global optimizing (Gradient Decent) method. The experiments conducted on real images show considerable improvement both perceptually and quantitatively when compared to conventional interpolation (Bicubic Interpolation images) methods. The advantage of the proposed technique is that unlike many other super-resolution techniques, a number of low resolution observations are not required. Finally instead of MRF we used Inhomogeneous markov random field(IGMRF) for maintain the spatial dependency effectively in super-resolved image, the results show that its better than MRF prior.
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    Active contours in action
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2005) Shah, Pratik P.; Banerjee, Asim
    There was considerable success in converting images into something like line drawings without resorting to any but the most general prior knowledge about smoothness and continuity. That led to the problem of “grouping” together the lines belonging to each object which is difficult in principle and very demanding of computing. Two terms that describes this problem in image processing tasks are edge detection and segmentation. Active contour models are proven to be very effective tools for image segmentation. The popularity of this semiautomatic approach may be attributed to its ability to aid segmentation process with apriori knowledge and user interaction. For more detailed application domain study for active contours, problem of converting a frontal photograph into a line drawing is taken up along with lip tracking based on Gradient Vector Flow force field (GVF) active contours. In images with gaussian and salt-pepper noise, segmentation process becomes difficult for gradient based methods. This work gives a solution to this problem. A novel break n’ join technique is presented and simulated for various images ranging from synthetic to real with convex and concave regions. And as an outcome, encouraging results are observed.
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    Automatic car license plate recognition
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2005) Suryanarayana, P. V.; Mitra, Suman K.; Banerjee, Asim
    In most traffic applications, someone gathers information manually at the scene or watching the video of a scene. With automatic car license plate recognition using image processing techniques, new ways of capturing traffic data are evolved. It works by processing a sequence of images captured by a video camera and identifying the vehicle’s license plate. Locating the car license plate in an image or video frame of a car is an important step in car license plate recognition/identification applications.

    In this thesis, we propose a morphology based method for license plate extraction from car images. The algorithm uses morphological operations on the preprocessed, edge images of the vehicles. Characteristic features such as license plate width and height, character height and spacing are considered for defining structural elements for morphological operations. The localised license plates are segmented into individual character images by connected component labeling algorithm and the segmented characters of license plate are recognized by template matching approach. Entire system is implemented in Matlab and is tested over a large database of car images of various countries.