M Tech Dissertations

Now showing 1 - 4 of 4

Open Access
Multiresolution fusion using compressive sensing and graph cuts
(Dhirubhai Ambani Institute of Information and Communication Technology, 2012) Harikumar, V.; Joshi, Manjunath V.
Multiresolution fusion refers to the enhancement of low spatial resolution (LR) of Multispectral (MS) images to that of Panchromatic (Pan ) image without compro- mising on the spectral details. Many of the present day methods for multiresolution fusion require that the Pan and MS images are registered. In this thesis we propose a new approach for multiresolution fusion which is based on the theory of compressive sensing and graph cuts. We rst estimate a close approximation to the fused image by using the sparseness in the given Pan and MS images. Assuming that the Pan and LR MS image have the same sparseness, the initial estimate of the fused image is obtained as the linear combination of the Pan blocks. The weights in the linear combination are estimated using the l1 minimization by making use of MS and the down sampled Pan image. The nal solution is obtained by using a model based approach. The low resolution MS image is modeled as the degraded and noisy version of the fused image in which the degradation matrix entries are estimated by using the initial estimate and the MS image. Since the MS fusion is an ill-posed inverse problem, we use a regularization based approach to obtain the nal solution. We use the truncated quadratic prior for the preservation of the discontinuities in the fused image. A suitable energy function is then formed which consists of data tting term and the prior term and is minimized using a graph cuts based approach in order to obtain the fused image. The advantage of the proposed method is that it does not require the registration of Pan and MS data. Also the spectral characteristics are well preserved in the fused image since we are not directly operating on the Pan digital numbers. Effectiveness of the proposed method is illustrated by conducting experiments on synthetic as well as on real satellite images. Quantitative comparison of the proposed method in terms of Erreur Relative Globale Adimensionnelle de Synthse (ERGAS), Correlation Coecient(CC) , Relative Average Spectral Error(RASE) and Spectral Aangle Mapper(SAM) with the state of the art approaches indicate superiority of our approach
Open Access
Moment based image segmentation
(Dhirubhai Ambani Institute of Information and Communication Technology, 2010) Chawla, Charu; Mitra, Suman K.
Usually, digital image of scene is not same as actual; it may degrade because of environment, camera focus, lightening conditions, etc. Segmentation is the key step before performing other operations like description, recognition, scene understanding, indexing, etc. Image segmentation is the identification of homogeneous regions in the image. This is accomplished by segmenting an image into subsets and later assigning the individual pixels to classes. There are various approaches for segmentation to identify the object and its spatial information. These approaches employ some features of the input image(s). The concept of feature is used to denote a piece of information which is relevant for solving the computational task related to a certain application. The moment is an invariant feature used in the pattern recognition field to recognize the test object from the database. The key point of using moment is to provide a unique identification for each object irrespective of its transformations. The moment is the weighted average intensity of pixels. It is used for object recognition so far. Now the idea is to use moment in object classification field. The propose method is to compute Set of Moments as a feature for each pixel to get information of the image. This information can be used further in its detail analysis or decision making systems by classification techniques. Moment requires an area to compute it. Hence, window based method is used for each pixel in the image. All possible windows have been defined in which current pixel is placed at different positions and moment is computed for each window representation. The moments define a relationship of that pixel with its neighbors. The set of moments computed will be feature vector of that pixel. After obtaining the feature vector of pixels, k-means classification technique is used to classify these vectors in k number of classes. The different types of moments are used to classify the images namely: Statistical, Geometric, Legendre moments. Experiments are performed using moments with different window sizes to analyze their effect on execution time and other features. The comparative study is performed on various moments using different window sizes. The comparison is done using mismatching between moments, window sizes and their computation time. The implementation is also performed on noisy images. The results conclude that the proposed method probably gives better result than pixel based classification. The Statistical moment gives better result as compared to Geometric and Legendry moment. Its computation time is also less because it does not involve polynomial function in computation. The window size also affects the segmentation. The small window size preserves edge information in segmented image. The computation time and noise tolerance of proposed algorithm also increases as window size increases. Hence, the selections of window size have trade between computation time and image quality. All the experiments have been performed on both gray and colour scale images in MATLAB(R).
Open Access
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.
Open Access
Fractal based approach for image segmentation
(Dhirubhai Ambani Institute of Information and Communication Technology, 2004) Londhe, Tushar; Banerjee, Asim
In this thesis, we have proposed an algorithm for image segmentation, using the fractal codes. The basic idea behind this algorithm is to use fractal codes for the image segmentation. This method uses compressed codes instead of the gray levels of the image. Therefore it is cost effective in the sense of storage space and time as no decoding is performed before using the segmentation algorithm. Moreover, the proposed scheme can directly use on the images accessed from the image database where images are kept in fractal-compressed code.

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