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Front Matter: Proceedings Of The Maics 2016 Conference, University Of Dayton
Front Matter: Proceedings Of The Maics 2016 Conference, University Of Dayton
Content presented at the MAICS conference
Front matter contains:
- A list of program chairs and committee members
- Foreword to the proceedings by James P. Buckley, conference chair; Saverio Perugini, general chair
Editors: Phu H. Phung, University of Dayton; Ju Shen, University of Dayton; Michael Glass, Valparaiso University
Histogram Of Oriented Phase And Gradient (Hopg) Descriptor For Improved Pedestrian Detection, Hussin Ragb, Vijayan K. Asari
Histogram Of Oriented Phase And Gradient (Hopg) Descriptor For Improved Pedestrian Detection, Hussin Ragb, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
This paper presents a new pedestrian detection descriptor named Histogram of Oriented Phase and Gradient (HOPG) based on a combination of the Histogram of Oriented Phase (HOP) features and the Histogram of Oriented Gradient features (HOG).
The proposed descriptor extracts the image information using both the gradient and phase congruency concepts. Although the HOG based method has been widely used in the human detection systems, it lacks to deal effectively with the images impacted by the illumination variations and cluttered background. By fusing HOP and HOG features, more structural information can be identified and localized in order to obtain more …
Gaussian Nonlinear Line Attractor For Learning Multidimensional Data, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla
Gaussian Nonlinear Line Attractor For Learning Multidimensional Data, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla
Electrical and Computer Engineering Faculty Publications
The human brain’s ability to extract information from multidimensional data modeled by the Nonlinear Line Attractor (NLA), where nodes are connected by polynomial weight sets. Neuron connections in this architecture assumes complete connectivity with all other neurons, thus creating a huge web of connections. We envision that each neuron should be connected to a group of surrounding neurons with weighted connection strengths that reduces with proximity to the neuron. To develop the weighted NLA architecture, we use a Gaussian weighting strategy to model the proximity, which will also reduce the computation times significantly.
Once all data has been trained in …
State Preserving Extreme Learning Machine For Face Recognition, Md. Zahangir Alom, Paheding Sidike, Vijayan K. Asari, Tarek M. Taha
State Preserving Extreme Learning Machine For Face Recognition, Md. Zahangir Alom, Paheding Sidike, Vijayan K. Asari, Tarek M. Taha
Electrical and Computer Engineering Faculty Publications
Extreme Learning Machine (ELM) has been introduced as a new algorithm for training single hidden layer feed-forward neural networks (SLFNs) instead of the classical gradient-based algorithms. Based on the consistency property of data, which enforce similar samples to share similar properties, ELM is a biologically inspired learning algorithm with SLFNs that learns much faster with good generalization and performs well in classification applications. However, the random generation of the weight matrix in current ELM based techniques leads to the possibility of unstable outputs in the learning and testing phases. Therefore, we present a novel approach for computing the weight matrix …
Brain Machine Interface Using Emotiv Epoc To Control Robai Cyton Robotic Arm, Daniel P. Prince, Mark J. Edmonds, Andrew J. Sutter, Matthew Thomas Cusumano, Wenjie Lu, Vijayan K. Asari
Brain Machine Interface Using Emotiv Epoc To Control Robai Cyton Robotic Arm, Daniel P. Prince, Mark J. Edmonds, Andrew J. Sutter, Matthew Thomas Cusumano, Wenjie Lu, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
The initial framework for an electroencephalography (EEG) thought recognition software suite is developed, built, and tested. This suite is designed to recognize human thoughts and pair them to actions for controlling a robotic arm.
Raw EEG brain activity data is collected using an Emotiv EPOC headset. The EEG data is processed through linear discriminant analysis (LDA), where an intended action is identified. The EEG classification suite is being developed to increase the number of distinct actions that can be identified compared to the Emotiv recognition software. The EEG classifier was able to correctly distinguish between two separate physical movements.
Future …
A Modular Approach For Key-Frame Selection In Wide Area Surveillance Video Analysis, Almabrok Essa, Paheding Sidike, Vijayan K. Asari
A Modular Approach For Key-Frame Selection In Wide Area Surveillance Video Analysis, Almabrok Essa, Paheding Sidike, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
This paper presents an efficient preprocessing algorithm for big data analysis. Our proposed key-frame selection method utilizes the statistical differences among subsequent frames to automatically select only the frames that contain the desired contextual information and discard the rest of the insignificant frames.
We anticipate that such key frame selection technique will have significant impact on wide area surveillance applications such as automatic object detection and recognition in aerial imagery. Three real-world datasets are used for evaluation and testing and the observed results are encouraging.
Efficient Thermal Image Segmentation Through Integration Of Nonlinear Enhancement With Unsupervised Active Contour Model, Fatema Albalooshi, Evan Krieger, Paheding Sidike, Vijayan K. Asari
Efficient Thermal Image Segmentation Through Integration Of Nonlinear Enhancement With Unsupervised Active Contour Model, Fatema Albalooshi, Evan Krieger, Paheding Sidike, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions.
Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity …
Gaussian Weighted Neighborhood Connectivity Of Nonlinear Line Attractor For Learning Complex Manifolds, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla
Gaussian Weighted Neighborhood Connectivity Of Nonlinear Line Attractor For Learning Complex Manifolds, Theus H. Aspiras, Vijayan K. Asari, Wesam Sakla
Electrical and Computer Engineering Faculty Publications
The human brain has the capability to process high quantities of data quickly for detection and recognition tasks. These tasks are made simpler by the understanding of data, which intentionally removes redundancies found in higher dimensional data and maps the data onto a lower dimensional space. The brain then encodes manifolds created in these spaces, which reveal a specific state of the system. We propose to use a recurrent neural network, the nonlinear line attractor (NLA) network, for the encoding of these manifolds as specific states, which will draw untrained data towards one of the specific states that the NLA …
Intensity And Resolution Enhancement Of Local Regions For Object Detection And Tracking In Wide Area Surveillance, Evan Krieger, Vijayan K. Asari, Saibabu Arigela, Theus H. Aspiras
Intensity And Resolution Enhancement Of Local Regions For Object Detection And Tracking In Wide Area Surveillance, Evan Krieger, Vijayan K. Asari, Saibabu Arigela, Theus H. Aspiras
Electrical and Computer Engineering Faculty Publications
Object tracking in wide area motion imagery is a complex problem that consists of object detection and target tracking over time. This challenge can be solved by human analysts who naturally have the ability to keep track of an object in a scene. A computer vision solution for object tracking has the potential to be a much faster and efficient solution. However, a computer vision solution faces certain challenges that do not affect a human analyst. To overcome these challenges, a tracking process is proposed that is inspired by the known advantages of a human analyst.
First, the focus of …
Video-To-Video Pose And Expression Invariant Face Recognition Using Volumetric Directional Pattern, Vijayan K. Asari, Almabrok Essa
Video-To-Video Pose And Expression Invariant Face Recognition Using Volumetric Directional Pattern, Vijayan K. Asari, Almabrok Essa
Electrical and Computer Engineering Faculty Publications
Face recognition in video has attracted attention as a cryptic method of human identification in surveillance systems. In this paper, we propose an end-to-end video face recognition system, addressing a difficult problem of identifying human faces in video due to the presence of large variations in facial pose and expression, and poor video resolution. The proposed descriptor, named Volumetric Directional Pattern (VDP), is an oriented and multi-scale volumetric descriptor that is able to extract and fuse the information of multi frames, temporal (dynamic) information, and multiple poses and expressions of faces in input video to produce feature vectors, which are …
Person Identification From Streaming Surveillance Video Using Mid-Level Features From Joint Action-Pose Distribution, Binu M. Nair, Vijayan K. Asari
Person Identification From Streaming Surveillance Video Using Mid-Level Features From Joint Action-Pose Distribution, Binu M. Nair, Vijayan K. Asari
Electrical and Computer Engineering Faculty Publications
We propose a real time person identification algorithm for surveillance based scenarios from low-resolution streaming video, based on mid-level features extracted from the joint distribution of various types of human actions and human poses.
The proposed algorithm uses the combination of an auto-encoder based action association framework which produces per-frame probability estimates of the action being performed, and a pose recognition framework which gives per-frame body part locations.
The main focus in this manuscript is to effectively combine these per-frame action probability estimates and pose trajectories from a short temporal window to obtain mid-level features. We demonstrate that these mid-level …