M Tech Dissertations
Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3
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Item Open Access Manifold valued image segmentation(Dhirubhai Ambani Institute of Information and Communication Technology, 2013) Bansal, Sumukh; Tatu, AdityaImage 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.Item Open Access Image ranking based on clustering(Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Sharma, Monika; Mitra, Suman K.In a typical content-based image retrieval (CBIR) system, query results are a set of images sorted by feature similarities with respect to the query. However, images with high feature similarities to the query may be very different from the query. We introduced a novel scheme to rank images, cluster based image ranking, which tackle this difference in query image and retrieved images based on hypothesis: semantically similar images tends to clustered in same cluster. Clustering approach attempts to capture the difference in query and retrieved images by learning the way that similar images belongs to same cluster. For clustering color moments based clustering approach is used. The moment is the weighted average intensity of pixels. The proposed method is to compute color Moments of separated R,G,B components of images as a feature to get information of the image. This information can be used further in its detail analysis or decision making systems by classification techniques. The moments define a relationship of that pixel with its neighbors. The set of moments computed will be feature vector of that image. After obtaining the feature vector of images, k-means classification technique is used to classify these vectors in k number of classes. Initial assignment of data to the cluster is not random, it is based on maximum connected components of images. The two types of features are used to cluster the images namely: block median based clustering and color moment based clustering. Experiments are performed using these features to analyze their effect on results. To demonstrate the effectiveness of the proposed method, a test database from retrieval result of LIRE search engine is used and result of Lire is used as base line. The results conclude that the proposed methods probably give better result than Lire result. All the experiments have been performed on in MATLAB(R). Wang database of 10000 images is used for retrieval. It can be downloaded from http://wang.ist.psu.edu/iwang/test1.tarItem 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).