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pro vyhledávání: '"Gangeh, Mehrdad J."'
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
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
In this paper, we propose supervised dictionary learning (SDL) by incorporating information on class labels into the learning of the dictionary. To this end, we propose to learn the dictionary in a space where the dependency between the signals and t
Externí odkaz:
http://arxiv.org/abs/1207.2488
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