演講公告

新聞標題：　( 2014-10-29 )

• 演講主題：Re-weighting the morphological diversity

• 主講人：彭冠舉博士 (中央研究院資訊科學所)

• 演講日期：2014年11月6日(Thursday)上午10:00-11:00

• 演講地點：NCTU SA307(交通大學 科學一館307室)

• 摘要內容：
  

  2014 NCTS/CMMSC Seminar on Probability and Statistics with Applications
  
  Abstract：
  Morphological component analysis (MCA) for signal separation decomposes a signal into a superposition of morphological subcomponents, each of which is approximately sparse in a certain dictionary. Some of the dictionaries can also be modified to make them adaptive to local structure in images. We show that signal separation performance can be improved over the previous MCA approaches by replacing L1 norm optimization with “weighted” L1 norm optimization and replacing their dictionary adaptation with regularized dictionary adaptation. The weight on an atom for sparse coding is commonly derived from the corresponding coefficient’s value. In contrast, the weight of an atom in a dictionary for signal separation is derived from the mutual coherence between the atom and the atoms in the other dictionaries. The proposed solution for regularized dictionary adaptation is an extension of the K-SVD method, where the dictionary and “weighted” sparse coefficients are estimated simultaneously. We present a series of experiments demonstrating the significant performance improvement of the proposed algorithm over the previous approaches for signal separation.

go back