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Designing and Implementing an Enhanced Classification Algorithm in Image Processing

Baghery Daneshvar, Mohammad | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 45195 (55)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Babaie-zadeh, Massoud; Ghorshi, Alireza
  7. Abstract:
  8. Statistical learning plays a key role in many areas of science [38]. An example of learning problems is image matching, image matching plays an important role in many aspects of computer vision.Computers can be used in intelligent tasks, which are followed by logical inference, for example, visual scenes (images or videos) or speech (audios). For humans visual system of such task are performed hundreds of times every day so easily sometimes without any awareness. In this thesis we focus on the image matching phase which is the first phase of the classification process. One of the popular image matching methods is Scale Invariant Feature Transform (SIFT) which our proposed method is based on it. The main idea behind our method is adding the following steps to SIFT:
    1.In the keypoint localization phase we added some steps to remove the excess keypoints.
    2.In feature description phase in addition to orientation histograms we added oriented patterns.
    3.In descriptor formation phase we decreased the size of the descriptors.
    By adding these changes to SIFT, we would have oriented patterns of keypoints. In addition, the number of keypoints have been reduced and the places of them would be selected more accurately, and the size of the descriptors have been reduced
  9. Keywords:
  10. Feature Extraction ; Image Matching ; Descriptor ; Oriented Pattern ; Image Processing

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