M Tech Dissertations

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

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    Microwave imaging of a 36-cell human body by moment method formulation
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Chandarana, Mahek Harshad; Ghodgaonkar, Deepak
    Estimation of a complex permittivity is achieved using the moment method formulation of electric field integral equation. In addition to numerical results for three dimensional biological bodies, thesis also describes formulation of inverse problem, forward problem and the evaluation of matrix element. For excitation of the 36-cell human body with electric field, short dipole is used as transmitter, which is located in front and back side of the body. Thus, electric field at cell centroid locations are calculated using near field equation and electric field at N receiving dipole locations are stimulated using MAT LAB and thus complex permittivity are estimated from the inverse problem. This procedure is an ideal approach. But, in practical scenario, random and systematic errors occur which cannot be calculated in stimulation. Hence, it is taken into consideration by addition of Gaussian error to the received electric field at the receiver locations. Due to this process, error in the complex permittivity of a buried cells occurs in a large amount. Hence, multiple view technique is introduced in order to obtain the reduction in the error of complex permittivity of buried cells.
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    Depth from defocus
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Khatri, Nilay; Banerjee, Asim
    With the recent innovations in 3D technology accurate estimation of depth is very fascinating and challenging problem. In this thesis, a depth estimation algorithm, utilizing Singular Value Decomposition to compute orthogonal operators, has been implemented to test the algorithm on a variety of image database. Due to the difficulty in obtaining the database, an algorithm is implemented, that attempts to generate various synthetic image database of a scene from two defocused images by varying camera parameters. Thus, providing a researcher with more databases to work upon.
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    ItemOpen Access
    Classification of 3D volume data using finite mixture models
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Phophalia, Ashish; Mitra, Suman K.
    The 3D imaging provides better view of objects from three directions as compared to 2D imaging where front face of object can be viewed only. It involves a complex relationship as compared to 2D imaging and hence computationally expensive also. But it also includes more information which helps in visualizing the object, its shape, boundary similar to real world phenomenon. The segmentation method should take care of 3D relationship that exists between voxels. The multi channel 3D imaging provides exibility in changing voxel size by changing echo pulse signals which helps in analysis of soft tissues. The application of 3D imaging in MRI brain images help in understanding more clearly the brain anatomy and function. Mixture model based image segmentation methods provide platform to many real life segmentation problems. Finite Mixture Model (FMM) segmentation techniques have been applied in 2D imaging successfully. But these methods do not involve spatial relationship among neighboring pixels. To overcome this drawback, Spatially Variant Finite Mixture Model (SVFMM) was given for classification purpose. In the medical imaging, the probability of noise is high due to environment, technician expertise level, etc. So, a robust method is required which can reduce the noise effect of the images. The Gaussian Distribution is more preferred in the literature but it is not robust against the noisy data. The Student's t Distribution uses Mahalanobis squared distance to reduce the effect of outlier data. A comparative study has been presented between these two distribution functions. In Medical Imaging, segmentation procedures provide facility to separate out different type of tissues instead of manual processing which requires time and efforts. The segmentation methods automate this classification procedure. To reduce the computation time in 3D medical imaging, a sampling based approach called Column Sampling is used. The variance of a column is taken as a measure in sample selection. A comparison is presented for time taken in sample selection from whole volume with Random Sampling. The selected samples are provided to the estimation technique. The parameters of mixture model are estimated using Maximum Likelihood Estimation and Bayesian Learning Estimation in the presented work. The method for estimating parameters of SVFMM using Bayesian Learning is proposed. The Misclassification Rate (MCR) is used for quantitative measure among these methods. This work analyzes FMM and SVFMM models with different probability distribution over two different estimation techniques. The MCR and computational time are considered as quantitative measures for performance evaluation. The different sampling percentage is tried out to estimate the parameters and their MCR and computational time are presented. In conclusion, Bayesian learning estimation SVFMM using Student's t distribution gives comparatively better results.