Adrien Depeursinge<\/a>\u00a0will be a speaker at the MIT CSAIL Seminar Series: Biomedical Imaging and Analysis 2015\/2016. The topic of his talk is ‘Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification’. The presentation will take place Thursday, July 28, 2016.<\/p>\nSUMMARY
We present texture operators encoding class-specific local\u00a0organizations of image directions (LOID) in a rotation-invariant\u00a0fashion. The LOIDs are key for visual understanding, and are at the\u00a0origin of the success of the popular approaches such as local binary\u00a0patterns (LBP) and the scale-invariant feature transform\u00a0(SIFT). Whereas LBPs and SIFT yield handcrafted image representations,\u00a0we propose to learn data-specific representations of the LOIDs in a\u00a0rotation-invariant fashion. The image operators are based on steerable\u00a0circular harmonic wavelets (CHW), offering a rich and yet compact\u00a0initial representation for characterizing natural textures. The joint\u00a0location and orientation required to encode the LOIDs is preserved by\u00a0using moving frames (MF) texture representations built from\u00a0locally-steered multi-order CHWs. In a second step, we use support\u00a0vector machines (SVM) to learn a multi-class shaping matrix of the\u00a0initial CHW representation, yielding data-driven MFs that are\u00a0invariant to rigid motions. We experimentally demonstrate the\u00a0effectiveness of the proposed operators for classifying natural\u00a0textures.<\/p>\n
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Adrien Depeursinge\u00a0will be a speaker at the MIT CSAIL Seminar Series: Biomedical Imaging and Analysis 2015\/2016. The topic of his talk is ‘Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification’. The presentation will take place Thursday, July 28, 2016. SUMMARYWe present texture operators encoding class-specific local\u00a0organizations of image directions (LOID) in a rotation-invariant\u00a0fashion. … <\/p>\n
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