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Vision Lab

BIOMEDICAL IMAGE ANALYSIS AND COMPUTATIONAL PATHOLOGY
3-Dimensional Lung Segmentation Using Deep Convolutional Neural Networks
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Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis
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Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network
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Multi-Organ Segmentation using Deep Neural Networks
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Quaternion Temporal Convolutional Networks
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Recurrent Residual U-Net (R2U-Net) for Medical Image Segmentation
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Skin Cancer Segmentation and Classification with Improved Deep Convolutional Neural Network
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DEEP LEARNING USING AERIAL VISION
Active Recall Networks for Multiperspectivity Learning through Shared Latent Space Optimization
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Deep Learning Based Automated Right-of-Way Surveillance
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GlacierNet: A Novel Deep Learning Approach for Debris-Covered Glacier Mapping
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Inceptive Event Time-Surfaces for Object Classification using Neuromorphic Cameras
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WIDE AREA MOTION IMAGERY ANALYSIS
When processing WAMI (Wide Area Motion Imagery) data several preprocessing techniques can be applied to significantly improve the visibility of the imagery.
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In order to perform tracking on Wide Area Motion Imagery (WAMI) data, the initial steps of image registration and moving object detection are required.
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The challenge of tracking vehicles and pedestrians in Wide Area Motion Imagery (WAMI) data is the limited resolution of the objects being tracked.
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Classification in Wide Area Motion Imagery (WAMI) is a difficult problem because of the low resolution of the objects.
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SCENE ANALYSIS AND UNDERSTANDING
Scene reconstruction systems are developed to assist autonomous machines with self awareness, navigation and completion of tasks.
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Video stabilization is a video processing technique used to eliminate the shakiness in video.
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The objective is to automatically detect changes between the buildings in a given region at two different states in time for protecting pipeline infrastructures.
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The main goal of Pipeline Intrusion Detection program is to develop a real-time application system that has capability of detecting and localizing threat objects on pipeline to prevent human-caused damages to surface pipelines.
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PERCEPTION BEYOND THE VISIBLE SPECTRUM
The science of hyperspectral remote sensing is based on taking a fraction of the electromagnetic spectrum and breaking it into numerous bands for theoretical analysis and computations.
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LiDAR is a remote sensing technology which uses a set of 3D geo-referenced points in order to describe a scene.
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We intend to develop advanced algorithms to extract and enhance distinguishable features of various explosives by analyzing the Fido data and employ novel classifiers to categorize various types of explosives.
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Synthetic aperture radar (SAR) is a coherent radar system that operates in the microwave portion of the electromagnetic spectrum and provides high resolution remote sensing imagery of terrain reflectivity.
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BIOMETRICS FOR HUMAN IDENTIFICATION
This research deals with a human surveillance system with integrated face understanding technologies to recognize personal identities in real time.
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This research develops an efficient face recognition algorithm based on a modular PCA approach that has an improved recognition rate for large variations in pose, lighting direction and facial expression.
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Face sketch recognition aims at recognizing faces from hand drawn sketches. A typical application of face sketch recognition is in criminal investigation to locate criminals involved. 
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The human iris, a thin circular diaphragm lying between the cornea and the lens, has an intricate structure with may minute characteristics such as furrows, freckles, crypts, and coronas.
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HUMAN ACTIVITY RECOGNITION
Face pose estimation from standard imagery remains a complex computer vision problem that requires identifying the primary modes of variance directly corresponding to pose variation, while ignoring variance due to face identity and other noise factors. 
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This approach considers human actions as space time shapes which is a concatenation of silhouettes, and uses a combination of shape descriptors to extract features suitable for recognition.
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The objective of our research is to determine Automatic 3D facial Expression Recognition using live video sequences captured by a camera.
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Face tracking is a very complex problem in computer vision. Attempts have been made to create a perfect object tracker. 
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VIDEO PROCESSING
The objective of this research is to develop an algorithm which is capable of simultaneous compression of bright regions and enhancement of dark regions in an image frames captured by a video surveillance system.
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Super-Resolution enhancement refers to the process of obtaining high resolution image from a single low resolution image. 
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Visibility improvement, contrast enhancement and features enhancement of images/video captured in bad weather environment is very useful for many outdoor computer vision applications like video surveillance, long range object detection, recognition and tracking, self navigating ground and air-based vision systems etc., 
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In this project, a method based on phase congruency is used to remove rain from videos. This method makes use of the spatial, temporal and chromatic properties of the rain streaks in order to detect and remove them.
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BRAIN ACTIVITY ANALYSIS
In this project, a method based on phase congruency is used to remove rain from videos. This method makes use of the spatial, temporal and chromatic properties of the rain streaks in order to detect and remove them.
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A major debate in the field of medical research is which data acquisition tool is more effective: Electroencephalography (EEG) or functional Magnetic Resonance Imaging (fMRI). 
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This project uses an Electroencephalogram (EEG) to collect signals from the brain as a person thinks of actions. These signals are then compared and categorized into simple actions such as lift, turn, grab, pull, push, or drop, among others. 
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ROBOTICS AND VISION GUIDED NAVIGATION
Many projects in the Vision Lab utilize aerial data, and the hexacopter drone is an effective way to obtain this aerial data. 
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The UD Vision Lab has provided the platform and sensors required for a real-time prototype system.  We will refer to this system as the Robust Artificial Intelligence-based Defense Electro Robot or simply RAIDER.
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The patterns generated by certain thoughts can be classified and used as control signals for a BMI system. The completed system allows a user to control a computer, robot, or other device by using thought as the only input mechanism.
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The Segway RMP 220 is a power, highly maneuverable, two-wheeled platform featuring an advanced stabilization mode that provides dynamic balancing.  The Vision Lab will utilize the high center of gravity and unique mobility characterizes to perform human tracking/following.
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SEMANTIC SEGMENTATION
This research presents optimizing Active Contour Model using recurrent architecture for automatic object region and boundary extraction in human activity video sequences. 
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Touch screens interfaces have become the quicker, more intuitive way to interact with surround technologies. We present an interactive object region segmentation technique that leverages the touch screen technology to the detection and identification of objects in images captured in real environments.
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CONTACT

Vision Lab, Dr. Vijayan Asari, Director

Kettering Lab
300 College Park
Dayton, Ohio 45469 - 0232
937-229-1779
Email
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