MARC 主機 00000nam  2200325   4500 
001    AAI1494257 
005    20120426105730.5 
008    120426s2011    ||||||||||||||||| ||eng d 
020    9781124684321 
035    (UMI)AAI1494257 
040    UMI|cUMI 
100 1  Kulkarni, Naveen 
245 10 Compressive Sensing for Computer Vision and Image 
300    96 p 
500    Source: Masters Abstracts International, Volume: 49-06, 
       page: 3890 
500    Adviser: Baoxin Li 
502    Thesis (M.S.)--Arizona State University, 2011 
520    With the introduction of compressed sensing and sparse 
       representation,many image processing and computer vision 
       problems have been looked at in a new way. Recent trends 
       indicate that many challenging computer vision and image 
       processing problems are being solved using compressive 
       sensing and sparse representation algorithms. This thesis 
       assays some applications of compressive sensing and sparse
       representation with regards to image enhancement, 
       restoration and classication. The first application deals 
       with image Super-Resolution through compressive sensing 
       based sparse representation. A novel framework is 
       developed for understanding and analyzing some of the 
       implications of compressive sensing in reconstruction and 
       recovery of an image through raw-sampled and trained 
       dictionaries. Properties of the projection operator and 
       the dictionary are examined and the corresponding results 
       presented. In the second application a novel technique for
       representing image classes uniquely in a high-dimensional 
       space for image classification is presented. In this 
       method, design and implementation strategy of the image 
       classification system through unique affine sparse codes 
       is presented, which leads to state of the art results. 
       This further leads to analysis of some of the properties 
       attributed to these unique sparse codes. In addition to 
       obtaining these codes, a strong classier is designed and 
       implemented to boost the results obtained. Evaluation with
       publicly available datasets shows that the proposed method
       outperforms other state of the art results in image 
       classication. The final part of the thesis deals with 
       image denoising with a novel approach towards obtaining 
       high quality denoised image patches using only a single 
       image. A new technique is proposed to obtain highly 
       correlated image patches through sparse representation, 
       which are then subjected to matrix completion to obtain 
       high quality image patches. Experiments suggest that there
       may exist a structure within a noisy image which can be 
       exploited for denoising through a low-rank constraint 
590    School code: 0010 
650  4 Computer Science 
690    0984 
710 2  Arizona State University.|bComputer Science 
773 0  |tMasters Abstracts International|g49-06 
856 40 |u