Mohammad Mahoor - Academia.edu (original) (raw)
Papers by Mohammad Mahoor
Journal of medical imaging (Bellingham, Wash.), 2016
Cancer is the second leading cause of death in US after cardiovascular disease. Image-based compu... more Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computer-aided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape. An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features. A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented. Particularly, we apply shearlet transform on images and extract the ...
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Brain sciences, Jan 29, 2016
Subthalamic nucleus (STN) local field potentials (LFP) are neural signals that have been shown to... more Subthalamic nucleus (STN) local field potentials (LFP) are neural signals that have been shown to reveal motor and language behavior, as well as pathological parkinsonian states. We use a research-grade implantable neurostimulator (INS) with data collection capabilities to record STN-LFP outside the operating room to determine the reliability of the signals over time and assess their dynamics with respect to behavior and dopaminergic medication. Seven subjects were implanted with the recording augmented deep brain stimulation (DBS) system, and bilateral STN-LFP recordings were collected in the clinic over twelve months. Subjects were cued to perform voluntary motor and language behaviors in on and off medication states. The STN-LFP recorded with the INS demonstrated behavior-modulated desynchronization of beta frequency (13-30 Hz) and synchronization of low gamma frequency (35-70 Hz) oscillations. Dopaminergic medication did not diminish the relative beta frequency oscillatory desyn...
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Journal of Vision, 2016
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Ipcv, 2010
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Proceedings Icip International Conference on Image Processing, 2009
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Ipcv, 2010
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Bmvc, 2006
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Journal of Ambient Intelligence and Smart Environments, Apr 1, 2012
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2006 International Conference on Image Processing, Oct 1, 2006
Abstract We present an automatic disparity-based approach for 3D face modeling, from two frontal ... more Abstract We present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from ...
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2014 Ieee Ras International Conference on Humanoid Robots, Nov 1, 2014
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2006 Ieee International Conference on Multimedia and Expo, 2006
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Proceedings Icip International Conference on Image Processing, 2008
... 25, no. 6, pp. 583 594, 1992. [20] Arun A. Ross, Karthik Nandakumar, and Anil K. Jain, Hand-... more ... 25, no. 6, pp. 583 594, 1992. [20] Arun A. Ross, Karthik Nandakumar, and Anil K. Jain, Hand-book of Multibiometrics (International Series on Biometrics), Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006. [21] A-Nansser Ansari, M. Abdel-Mottaleb, and Mohammad H ...
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2010 Aaai Fall Symposium Series, Mar 11, 2010
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2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009
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2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013
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Pattern Recognition, 2015
ABSTRACT Automatic facial expression analysis has received great attention in different applicati... more ABSTRACT Automatic facial expression analysis has received great attention in different applications over the last two decades. Facial Action Coding System (FACS), which describes all possible facial expressions based on a set of facial muscle movements called Action Unit (AU), has been used extensively to model and analyze facial expressions. FACS describes methods for coding the intensity of AUs, and AU intensity measurement is important in some studies in behavioral science and developmental psychology. However, in majority of the existing studies in the area of facial expression recognition, the focus has been on basic expression recognition or facial action unit detection. There are very few investigations on measuring the intensity of spontaneous facial actions. In addition, the few studies on AU intensity recognition usually try to measure the intensity of facial actions statically and individually, ignoring the dependencies among multilevel AU intensities as well as the temporal information. However, these spatiotemporal interactions among facial actions are crucial for understanding and analyzing spontaneous facial expressions, since these coherent, coordinated, and synchronized interactions are that produce a meaningful facial display. In this paper, we propose a framework based on Dynamic Bayesian Network (DBN) to systematically model the dynamic and semantic relationships among multilevel AU intensities. Given the extracted image observations, the AU intensity recognition is accomplished through probabilistic inference by systematically integrating the image observations with the proposed DBN model. Experiments on Denver Intensity of Spontaneous Facial Action (DISFA) database demonstrate the superiority of our method over single image-driven methods in AU intensity measurement.
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2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013
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Journal of medical imaging (Bellingham, Wash.), 2016
Cancer is the second leading cause of death in US after cardiovascular disease. Image-based compu... more Cancer is the second leading cause of death in US after cardiovascular disease. Image-based computer-aided diagnosis can assist physicians to efficiently diagnose cancers in early stages. Existing computer-aided algorithms use hand-crafted features such as wavelet coefficients, co-occurrence matrix features, and recently, histogram of shearlet coefficients for classification of cancerous tissues and cells in images. These hand-crafted features often lack generalizability since every cancerous tissue and cell has a specific texture, structure, and shape. An alternative approach is to use convolutional neural networks (CNNs) to learn the most appropriate feature abstractions directly from the data and handle the limitations of hand-crafted features. A framework for breast cancer detection and prostate Gleason grading using CNN trained on images along with the magnitude and phase of shearlet coefficients is presented. Particularly, we apply shearlet transform on images and extract the ...
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Brain sciences, Jan 29, 2016
Subthalamic nucleus (STN) local field potentials (LFP) are neural signals that have been shown to... more Subthalamic nucleus (STN) local field potentials (LFP) are neural signals that have been shown to reveal motor and language behavior, as well as pathological parkinsonian states. We use a research-grade implantable neurostimulator (INS) with data collection capabilities to record STN-LFP outside the operating room to determine the reliability of the signals over time and assess their dynamics with respect to behavior and dopaminergic medication. Seven subjects were implanted with the recording augmented deep brain stimulation (DBS) system, and bilateral STN-LFP recordings were collected in the clinic over twelve months. Subjects were cued to perform voluntary motor and language behaviors in on and off medication states. The STN-LFP recorded with the INS demonstrated behavior-modulated desynchronization of beta frequency (13-30 Hz) and synchronization of low gamma frequency (35-70 Hz) oscillations. Dopaminergic medication did not diminish the relative beta frequency oscillatory desyn...
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Journal of Vision, 2016
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Ipcv, 2010
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Proceedings Icip International Conference on Image Processing, 2009
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Ipcv, 2010
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Bmvc, 2006
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Journal of Ambient Intelligence and Smart Environments, Apr 1, 2012
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2006 International Conference on Image Processing, Oct 1, 2006
Abstract We present an automatic disparity-based approach for 3D face modeling, from two frontal ... more Abstract We present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from ...
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2014 Ieee Ras International Conference on Humanoid Robots, Nov 1, 2014
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2006 Ieee International Conference on Multimedia and Expo, 2006
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Proceedings Icip International Conference on Image Processing, 2008
... 25, no. 6, pp. 583 594, 1992. [20] Arun A. Ross, Karthik Nandakumar, and Anil K. Jain, Hand-... more ... 25, no. 6, pp. 583 594, 1992. [20] Arun A. Ross, Karthik Nandakumar, and Anil K. Jain, Hand-book of Multibiometrics (International Series on Biometrics), Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006. [21] A-Nansser Ansari, M. Abdel-Mottaleb, and Mohammad H ...
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2010 Aaai Fall Symposium Series, Mar 11, 2010
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2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009
Bookmarks Related papers MentionsView impact
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013
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Pattern Recognition, 2015
ABSTRACT Automatic facial expression analysis has received great attention in different applicati... more ABSTRACT Automatic facial expression analysis has received great attention in different applications over the last two decades. Facial Action Coding System (FACS), which describes all possible facial expressions based on a set of facial muscle movements called Action Unit (AU), has been used extensively to model and analyze facial expressions. FACS describes methods for coding the intensity of AUs, and AU intensity measurement is important in some studies in behavioral science and developmental psychology. However, in majority of the existing studies in the area of facial expression recognition, the focus has been on basic expression recognition or facial action unit detection. There are very few investigations on measuring the intensity of spontaneous facial actions. In addition, the few studies on AU intensity recognition usually try to measure the intensity of facial actions statically and individually, ignoring the dependencies among multilevel AU intensities as well as the temporal information. However, these spatiotemporal interactions among facial actions are crucial for understanding and analyzing spontaneous facial expressions, since these coherent, coordinated, and synchronized interactions are that produce a meaningful facial display. In this paper, we propose a framework based on Dynamic Bayesian Network (DBN) to systematically model the dynamic and semantic relationships among multilevel AU intensities. Given the extracted image observations, the AU intensity recognition is accomplished through probabilistic inference by systematically integrating the image observations with the proposed DBN model. Experiments on Denver Intensity of Spontaneous Facial Action (DISFA) database demonstrate the superiority of our method over single image-driven methods in AU intensity measurement.
Bookmarks Related papers MentionsView impact
2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013
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