Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Gangeh, Mehrdad"'
Removing noise from scanned pages is a vital step before their submission to the optical character recognition (OCR) system. Most available image denoising methods are supervised where the pairs of noisy/clean pages are required. However, this assump
Externí odkaz:
http://arxiv.org/abs/2105.09437
Autor:
Gangeh, Mehrdad J., Tizhoosh, Hamid R., Wu, Kan, Huang, Dun, Tadayyon, Hadi, Czarnota, Gregory J.
Recent advances in using quantitative ultrasound (QUS) methods have provided a promising framework to non-invasively and inexpensively monitor or predict the effectiveness of therapeutic cancer responses. One of the earliest steps in using QUS method
Externí odkaz:
http://arxiv.org/abs/1701.03779
In this paper, a novel semi-supervised dictionary learning and sparse representation (SS-DLSR) is proposed. The proposed method benefits from the supervisory information by learning the dictionary in a space where the dependency between the data and
Externí odkaz:
http://arxiv.org/abs/1604.07319
Quantitative ultrasound (QUS) methods provide a promising framework that can non-invasively and inexpensively be used to predict or assess the tumour response to cancer treatment. The first step in using the QUS methods is to select a region of inter
Externí odkaz:
http://arxiv.org/abs/1602.02586
Autor:
Gangeh, Mehrdad J., Ghodsi, Ali
In this paper, it is proved that dictionary learning and sparse representation is invariant to a linear transformation. It subsumes the special case of transforming/projecting the data into a discriminative space. This is important because recently,
Externí odkaz:
http://arxiv.org/abs/1503.02041
Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-of-the-art performance in various fields such as pattern recognition, machine learning, computer vision, an
Externí odkaz:
http://arxiv.org/abs/1502.05928
Autor:
Sannachi, Lakshmanan *, Gangeh, Mehrdad *, Naini, Ali-Sadeghi *, Bhargava, Priya, Jain, Aparna, Tran, William Tyler, Czarnota, Gregory Jan *
Publikováno v:
In Ultrasound in Medicine & Biology May 2020 46(5):1142-1157
Autor:
Sannachi, Lakshmanan *, Gangeh, Mehrdad *, Tadayyon, Hadi *, Gandhi, Sonal, Wright, Frances C., Slodkowska, Elzbieta, Curpen, Belinda, Sadeghi-Naini, Ali, Tran, William *, Czarnota, Gregory J. *
Publikováno v:
In Translational Oncology October 2019 12(10):1271-1281
Textures often show multiscale properties and hence multiscale techniques are considered useful for texture analysis. Scale-space theory as a biologically motivated approach may be used to construct multiscale textures. In this paper various ways are
Externí odkaz:
http://arxiv.org/abs/1207.4089
Supervised pixel-based texture classification is usually performed in the feature space. We propose to perform this task in (dis)similarity space by introducing a new compression-based (dis)similarity measure. The proposed measure utilizes two dimens
Externí odkaz:
http://arxiv.org/abs/1207.3071