Mohammad Mahoor - Profile on Academia.edu (original) (raw)
Papers by Mohammad Mahoor
Microscopic medical image classification framework via deep learning and shearlet transform
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 ...
Long-Term Task- and Dopamine-Dependent Dynamics of Subthalamic Local Field Potentials in Parkinson's Disease
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...
Children with Autism Spectrum Disorders rely on head rotation to perceive gaze direction
Journal of Vision, 2016
SIFT-Motion Estimation (SIFT-ME): A New Feature for Human Activity Recognition
Ipcv, 2010
Proceedings Icip International Conference on Image Processing, 2009
In this paper we describe a multi-modal ear and face biometric system. The system is comprised of... more In this paper we describe a multi-modal ear and face biometric system. The system is comprised of two components: a 3D ear recognition component and a 2D face recognition component. For the 3D ear recognition, a series of frames is extracted from a video clip and the region of interest (i.e., ear) in each frame is independently reconstructed in 3D using Shape From Shading. The resulting 3D models are then registered using the iterative closest point algorithm. We iteratively consider each model in the series as a reference model and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. For the 2D face recognition, a set of facial landmarks is extracted from frontal facial images using the Active Shape Model. Then, the response of facial images to a series of Gabor filters at the locations of facial landmarks are calculated. The Gabor features (attributes) are stored in the database as the face model for recognition. The similarity between the Gabor features of a probe facial image and the reference models are utilized to determine the best match. The match scores of the ear recognition and face recognition modalities are fused to boost the overall recognition rate of the system. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips (images). As a result, a rank-one identification rate of 100% was achieved using the weighted sum technique for fusion.
of a dissertation at the University of Miami. Dissertation supervised by Dr. Mohamed Abdel-Mottal... more of a dissertation at the University of Miami. Dissertation supervised by Dr. Mohamed Abdel-Mottaleb. No. of pages in text. (169)
Affective Computing, Emotional Development, and Autism
2D Human Skeleton Model from Monocular Video for Human Activity Recognition
Ipcv, 2010
Bmvc, 2006
This paper presents a novel approach for combining a set of registered images into a composite mo... more This paper presents a novel approach for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion. To promote execution speed in building large area mosaics, the mosaic space is divided into disjoint regions of image intersection based on a geometric criterion. Pair-wise image blending is performed independently in each region by means of watershed segmentation and graph cut optimization. A contribution of this work -use of watershed segmentation to find possible cuts over areas of low photometric difference -allows for searching over a much smaller set of watershed segments, instead of over the entire set of pixels in the intersection zone.
Modeling the Dynamics of Spontaneous Facial Action Units
Fitting distal limb segments for accurate skeletonization in human action recognition
Journal of Ambient Intelligence and Smart Environments, Apr 1, 2012
Disparity-Based 3D Face Modeling for 3D Face Recognition
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 ...
ExpressionBot: An emotive lifelike robotic face for face-to-face communication
2014 Ieee Ras International Conference on Humanoid Robots, Nov 1, 2014
2006 Ieee International Conference on Multimedia and Expo, 2006
We present an automatic disparity-based approach for 3D face modeling, from two frontal and one p... more 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 one of the frontal views and computes a dense disparity map from the two frontal images. Using the extracted 2D features plus their corresponding disparities in the disparity map, we compute their 3D coordinates. We next align a low resolution 3D mesh model to the 3D features, re-project it's vertices on the frontal 2D image and adjust its profile line vertices using the profile view. We increase the resolutions of the resulting 2D model only at its center region to obtain a facial mask model covering distinctive features of the face. The computation of the 2D vertices coordinates with their disparities results in a deformed 3D model mask specific to a give subject's face. Application of the model in 3D face recognition validates the algorithm and shows a high recognition rate.
Multi-modal (2-D and 3-D) face modeling and recognition using Attributed Relational Graph
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 ...
2010 Aaai Fall Symposium Series, Mar 11, 2010
We present our position on developing an open source platform for human-robot dialog research in ... more We present our position on developing an open source platform for human-robot dialog research in home healthcare. We discuss the need for perceptive, emotive, spoken-dialog robotic companions, describe an architecture for this research, and call on others to participate in making it a successful community resource.
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009
This paper presents a framework to automatically measure the intensity of naturally occurring fac... more This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The Facial Action Coding System (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping Action Units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (Lip Corner Puller) and AU6 (Cheek Raising) in videos captured from infant-mother live faceto-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train Support Vector Machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units.
Mobile robot connectivity maintenance based on RF mapping
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013
Measuring the intensity of spontaneous facial action units with dynamic Bayesian network
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.
Facial expression recognition using HessianMKL based multiclass-SVM
2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013
Microscopic medical image classification framework via deep learning and shearlet transform
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 ...
Long-Term Task- and Dopamine-Dependent Dynamics of Subthalamic Local Field Potentials in Parkinson's Disease
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...
Children with Autism Spectrum Disorders rely on head rotation to perceive gaze direction
Journal of Vision, 2016
SIFT-Motion Estimation (SIFT-ME): A New Feature for Human Activity Recognition
Ipcv, 2010
Proceedings Icip International Conference on Image Processing, 2009
In this paper we describe a multi-modal ear and face biometric system. The system is comprised of... more In this paper we describe a multi-modal ear and face biometric system. The system is comprised of two components: a 3D ear recognition component and a 2D face recognition component. For the 3D ear recognition, a series of frames is extracted from a video clip and the region of interest (i.e., ear) in each frame is independently reconstructed in 3D using Shape From Shading. The resulting 3D models are then registered using the iterative closest point algorithm. We iteratively consider each model in the series as a reference model and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. For the 2D face recognition, a set of facial landmarks is extracted from frontal facial images using the Active Shape Model. Then, the response of facial images to a series of Gabor filters at the locations of facial landmarks are calculated. The Gabor features (attributes) are stored in the database as the face model for recognition. The similarity between the Gabor features of a probe facial image and the reference models are utilized to determine the best match. The match scores of the ear recognition and face recognition modalities are fused to boost the overall recognition rate of the system. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips (images). As a result, a rank-one identification rate of 100% was achieved using the weighted sum technique for fusion.
of a dissertation at the University of Miami. Dissertation supervised by Dr. Mohamed Abdel-Mottal... more of a dissertation at the University of Miami. Dissertation supervised by Dr. Mohamed Abdel-Mottaleb. No. of pages in text. (169)
Affective Computing, Emotional Development, and Autism
2D Human Skeleton Model from Monocular Video for Human Activity Recognition
Ipcv, 2010
Bmvc, 2006
This paper presents a novel approach for combining a set of registered images into a composite mo... more This paper presents a novel approach for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion. To promote execution speed in building large area mosaics, the mosaic space is divided into disjoint regions of image intersection based on a geometric criterion. Pair-wise image blending is performed independently in each region by means of watershed segmentation and graph cut optimization. A contribution of this work -use of watershed segmentation to find possible cuts over areas of low photometric difference -allows for searching over a much smaller set of watershed segments, instead of over the entire set of pixels in the intersection zone.
Modeling the Dynamics of Spontaneous Facial Action Units
Fitting distal limb segments for accurate skeletonization in human action recognition
Journal of Ambient Intelligence and Smart Environments, Apr 1, 2012
Disparity-Based 3D Face Modeling for 3D Face Recognition
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 ...
ExpressionBot: An emotive lifelike robotic face for face-to-face communication
2014 Ieee Ras International Conference on Humanoid Robots, Nov 1, 2014
2006 Ieee International Conference on Multimedia and Expo, 2006
We present an automatic disparity-based approach for 3D face modeling, from two frontal and one p... more 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 one of the frontal views and computes a dense disparity map from the two frontal images. Using the extracted 2D features plus their corresponding disparities in the disparity map, we compute their 3D coordinates. We next align a low resolution 3D mesh model to the 3D features, re-project it's vertices on the frontal 2D image and adjust its profile line vertices using the profile view. We increase the resolutions of the resulting 2D model only at its center region to obtain a facial mask model covering distinctive features of the face. The computation of the 2D vertices coordinates with their disparities results in a deformed 3D model mask specific to a give subject's face. Application of the model in 3D face recognition validates the algorithm and shows a high recognition rate.
Multi-modal (2-D and 3-D) face modeling and recognition using Attributed Relational Graph
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 ...
2010 Aaai Fall Symposium Series, Mar 11, 2010
We present our position on developing an open source platform for human-robot dialog research in ... more We present our position on developing an open source platform for human-robot dialog research in home healthcare. We discuss the need for perceptive, emotive, spoken-dialog robotic companions, describe an architecture for this research, and call on others to participate in making it a successful community resource.
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009
This paper presents a framework to automatically measure the intensity of naturally occurring fac... more This paper presents a framework to automatically measure the intensity of naturally occurring facial actions. Naturalistic expressions are non-posed spontaneous actions. The Facial Action Coding System (FACS) is the gold standard technique for describing facial expressions, which are parsed as comprehensive, nonoverlapping Action Units (Aus). AUs have intensities ranging from absent to maximal on a six-point metric (i.e., 0 to 5). Despite the efforts in recognizing the presence of non-posed action units, measuring their intensity has not been studied comprehensively. In this paper, we develop a framework to measure the intensity of AU12 (Lip Corner Puller) and AU6 (Cheek Raising) in videos captured from infant-mother live faceto-face communications. The AU12 and AU6 are the most challenging case of infant's expressions (e.g., low facial texture in infant's face). One of the problems in facial image analysis is the large dimensionality of the visual data. Our approach for solving this problem is to utilize the spectral regression technique to project high dimensionality facial images into a low dimensionality space. Represented facial images in the low dimensional space are utilized to train Support Vector Machine classifiers to predict the intensity of action units. Analysis of 18 minutes of captured video of non-posed facial expressions of several infants and mothers shows significant agreement between a human FACS coder and our approach, which makes it an efficient approach for automated measurement of the intensity of non-posed facial action units.
Mobile robot connectivity maintenance based on RF mapping
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013
Measuring the intensity of spontaneous facial action units with dynamic Bayesian network
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.
Facial expression recognition using HessianMKL based multiclass-SVM
2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), 2013