Theses and Dissertations

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    ItemOpen Access
    Dhows: the traditional art of wooden boat making
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Sanghani, Palak; Desai, Binita
    Boat building forms a very important part of the economy along the coast of Gujarat. Salaya, Veraval and Mandvi are towns of fishermen, boat makers and traders. In these regions boat building is an integral part of their daily lives. There is a unique relationship between traders, boat makers and fishermen. Traders invest, boat makers build the Dhows and fishermen use them. Dhows are a central aspect both socio-economically and culturally in the port towns of Gujarat. Every individual is directly or indirectly connected to the boat makers, repairers, helpers and carpenters. Some members of the community are engaged in the transportation of timber, fishing, and net making or in the supply of timber. Some members are associated with the fisheries industry. There are others who are engaged in recycling wood to build bridges or doors and in selling wood from broken down ships. Dhow builders neither require a large industrial set up nor hundreds of people but the methodology and precision with which these craftsmen operate is comparable to any large industry with huge investments. Most of the traditional boats are built in temporary yards with sheds that are erected by carpenters, on land at the port, rented to them by the Government. The ports are busy places with the sound of carpenters at work. Dhows stand tall on the coastal wetland, the deck swarms with carpenters, labourers and errand boys going about their work. It takes an average of six months for a Dhow to take shape with ten to twelve carpenters working continuously. Carpenters arrive purposefully at the site at 9am and work till dusk making full use of daylight hours. They are adept at handling wood almost like clay or paper, curving and bending wood to get the desired shape. It is pleasurable experience to see how they create Dhows from a single plank of wood that could have a load carrying capacity ranging from five hundred to fifteen hundred tonnes and keep sailing for about thirty years. A well-built Dhow could last for fifty years if well maintained. All year round at the port there are several boats at various stages of construction. During monsoons when the fleets return from sea repair work increases and waters are crammed with Dhows under repair and streets are abuzz with sailors. Dhows are pulled up on spare ground, allowed to dry, repaired, oiled and set afloat again. During the four months of monsoons Indian Government bans any vehicle from going into the sea as waters are too dangerous and rough. Near every Dhow there is a modest shack made with wood waste and gunny bags where the watchman lives. A small cabin built with recycled planks serves as a shelter from the scorching sun and a place to relax for the contractor and other workers. Small Paan cabins and tea stalls serve as addas for the workers, primarily for conversation and playing cards. Buzz word ‘sustainability’ finds its true meaning in these clusters where still their system of making and living has been well integrated. It examines how people, places and things are constituted through every day work practices and all the other relational, embodied, habitual activities of life. The wooden ship-building industry should get a boost as there is much potential. There were some plans to encourage Vahanvatti a few years ago but nothing came out of it. Government doesn’t provide any subsidies or benefits to the boat making community.
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    Integrating semantics into biomedical information retrieval
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Thakrar, Fenny; Majumder, Prasenjit
    Integrating semantics into Biomedical Information Retrieval is concerned with studying the meaning of concepts and focusing on their relationships. We have used semantic document representation approach to applying domain-specific knowledge into the information retrieval system. Single and multi word concepts are extracted from the document using an external semantic structure UMLS Metathesaurus. Word sense disambiguation is performed on the extracted concepts to disambiguate different concept senses. And, the document is represented in the form of UMLS concepts. The documents and queries are represented in semantic space and fed to an information retrieval system to rank those documents, according to the given query. We have performed experiments on TREC 2014 CDS Task data and its 30 queries. Two types of retrieval techniques namely single word and multi word retrieval are experimented. The results obtained using conceptual information retrieval are compared with the results obtained using traditional term based retrieval. The conceptual IR approach proved better compared to term based IR system for the evaluation metrics MAP, P10 and RPrec. And, single word retrieval proved better compared to multi word retrieval technique for conceptual IR. Also, query expansion in conceptual IR system proved better compared to non query expanded conceptual IR system.
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    Text retrieval from the degraded document images
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Vasani, Hiral; Mitra, Suman K.
    Image binarization is used to obtain a black and white text document from a colored one. Basically, it can be taken as an image segmentation task that segments the text part from the background. Such a black and white document can be used in many applications, namely Optical Character Recognition (OCR). Text documents suffer from various types of degradations that make image binarization a challenging task. This thesis presents the work done to design a technique that segments text from the background. In this method, the document image is first darkened in order to enhance the text (foreground) in it. The text image is again processed separately so as to suppress the background. The two images so obtained are combined in such a way that the suppressed background is retained from the last image and enhanced text is used from the first image. Then this pre-processed image is binarized using an existing thresholding technique. The first binarized image is subjected to some post-processing in order to remove unwanted smaller components and other noise. The output image so obtained is compared to the ground truth results using some evaluation parameters. The results of the algorithm are compared to the existing Binarization techniques.
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    Summarizing medical texts for effective retrieval
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Iyer, Ganesh R; Majumder, Prasenjit
    User centered health information retrieval is a challenging and important problem in information retrieval. In this work, we apply medical resources to bridge the vocabulary mismatch between lay-users and medical documents. We also applied text summarization techniques to reduce the document to relevant information while pruning irrelevant information. We provide a survey of medical resources and application of text summarization in information retrieval. The primary research goals were to investigate the use of medical resources in query expansion and text summarization in indexing. The experiments were performed as a part of a CLEF eHealth Task, overview of which is provided. From our experiments we observed that a summarized index can be used to replace a full collection index. Also a compression rate of 40-80% outperformed the baseline indicating that retrieval on the summarized collection can indeed improve performance. Using MeSH(Medical Subject Headings) as a thesaurus to supplement the query terms improved retrieval for certain queries. We obtained the best MAP score of 0.415, for all teams, using query expansion with discharge summaries.
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    Techniques for denoising brain magnetic resonance images
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2016) Phophalia, Ashish; Mitra, Suman K.
    Advances in the computational science joined medical imaging domain to help humanity. It offers great support in clinical practices where automatic Computer Added Systems (CAD) help in identification and localization of abnormal tissues. In recent decades, a lot of research tuned non-invasive techniques have been devised to serve mankind. One of them is Magnetic Resonance Imaging (MRI) which provides structural information at higher resolution even in presence of bone structures in the body. Although it is free from ionizing ingredient, factors like electronic circuitry, patient movement etc. provoke some artifacts in imaging system considered as noise. One needs to get rid of these artifacts by means of software processing to enhance the performance of diagnostic process. This thesis is also an attempt to deal with noisy part of MRI and comply with preserving image structures such as boundary details and preventing over-smoothing. It has been observed that, in case of MR data, noise follows Rician distribution. As opposed to additive Gaussian noise, Rician noise is signal dependent in nature due to MR image acquisition process. The thesis constitutes a relationship between MRI denoising and uncertainty model defined by Rough Set Theory (RST). RST already has shown some promising outcomes in image processing problems including segmentation, clustering whereas not much attention has been paid in image restoration task. The first part of the thesis proposes a novel method for object based segmentation and edge derivation given the noisy MR image. The edges are closed and continuous in nature and segmentation accuracy turns out to be better than well-known methods. The prior information is used as cues in various image denoising frameworks. In Bilateral filter framework along with spatial and intensity cues, a new weighing factor is derived using prior segmentation and edge information. This further extends to non local framework where waiver in spatial relation conceded to access similar information from far of neighbors. Under non locality paradigm, a clustering based method is proposed which clubs together similar patches based on similarity criteria. The proposed clustering method uniquely defines clusters of patches under multiple class set up. These clusters are then used to define the basis vectors using Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) method followed by hard thresholding shrinkage procedure. Afterwards, multiple estimations of a pixel are averaged by number of estimations. In total, number of PCA or KPCA operations are far less than other contemporary methods which repeat the same process over chunks of patches in the image space. The concept is then extended for 3D MRI data. The 3D imaging provides better view of objects from three directions as compared to 2D imaging where only one face of object can be viewed. It involves a complex relationship as compared to 2D imaging and hence is computationally expensive. But it also includes more information which helps in visualizing the object, its shape, boundary etc. similar to real world phenomenon. We extended the segmentation and edge derivation mechanism to 3D data in last part of the thesis. Clustering process is also extended by converting each voxel to one dimensional vector. This part explores various kernels over Rician noise distributed MR data. The results are promising in terms of structure measures even with some simple kernels.