M Tech Dissertations

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

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Subject: search.filters.subject.Image Segmentation

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    ItemOpen Access
    Learning based approach for image compression
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Kumar, Dheeraj; Joshi, Manjunath V.
    Data compression is a process of storing same information with less data or space in computer memory. There are many image compression techniques that are available for storing images with less storage space. Minimizing storage space minimizes the bandwidth required for transmission. In the proposed algorithm first level Discrete Wavelet Transformation (DWT, with Daubechies wavelets db4 as a mother wavelet) is applied on original image after which only low resolution coefficients are retained. Further Embedded Zero Tree Wavelet based algorithm (EZW) [10] is applied for best image quality at the given bit rate. We are using a set of images as database. For every input image Content Based Image Retrieval (CBIR) [7] technique is applied on database which results in some images, having similar content. At the receiver a learning based approach is used to decompress from resulted database images. Structure Similarity Index Measurement (SSIM) [15] an image quality assessment is used for similarity check. Inverse DWT is applied to get the estimate of the original. This is a lossy compression and results are compared with JPEG [13] and JPEG2000 [8] compression.
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    ItemOpen Access
    Study of fuzzy clustering algorithms and enhanced fuzzy reasoning application to texture based image segmentation
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Gupta, Dhruv; Banerjee, Asim; Raval, Mehul S.; Shah, Pratik
    c-means (k-means) is a popular algorithm for cluster analysis. Many variants of c-means algorithms are available. All these models are studied in depth and convergence of iterative solutions are verified, in this thesis. An example of texture based image segmentation is used to support this study of various clustering algorithms. In context of clustering points in a space, a cluster represents a set of elements. The set is created by studying the membership of each element within it. Conventionally there are two types of set theories: crisp and its extension fuzzy set theory. The extension of crisp sets to fuzzy sets in terms of membership functions, is alike to extension of the set of integers to the set of real numbers. But the development does not end here, the membership can be extended to a vector value. Clustering is significantly affected by the data dimensionality and the distance metric used during cluster formation. Distance between points and distance between clusters are the key attributes for an accurate cluster analysis. During analysis of fuzzy based clustering a need for a new distance metric was felt. This metric defines distance between fuzzy sets and also between elements and fuzzy sets. As a step to fulfil this requirement, in this work the fuzzy sets with vector memberships are defined and proposed. Basic set theoretic operations, such as complement, union and intersection are defined and discussed in axiomatic manner. This work also proposes a new distance function defined for points and sets, and the new function is proved to be a metric through systematic proofs.
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    ItemOpen Access
    Manifold valued image segmentation
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2013) Bansal, Sumukh; Tatu, Aditya
    Image segmentation is the process of partitioning a image into different regions or groups based on some characteristics like color, texture, motion or shape etc. Segmentation is an intermediate process for a large number of applications including object recognition and detection. Active contour is a popular variational model for object segmentation in images, in which the user initializes a contour which evolves in order to optimize an objective function designed such that the desired object boundary is the optimal solution. Recently, imaging modalities that produce Manifold valued images have come up, for example, DT-MRI images, vector fields. The traditional active contour model does not work on such images. In the work presented here we generalize the active contour model to work on Manifold valued images. Since usual gray-scale images are just an specific example of Manifold valued images, our method produce expected results on gray-scale images. As an application of proposed active contour model we we perform texture segmentation on gray-scale images by first creating an appropriate Manifold valued image. We demonstrate segmentation results for manifold valued images and texture images. Diversity of the texture segmentation problem Inspired us to propose a new active contour model for texture segmentation where we find the background/foreground texture regions in a given image by maximizing the geodesic distance between the interior and exterior covariance matrices. We also provide results using proposed method.