The computational intelligence and machine vision laboratory (Vision Lab) aims to develop novel theory, state-of-art algorithms and architectures for learning and real-time applications in the areas of:

Human and Machine-Centric Interaction, Learning and Recognition

	Data-limited domain adaptation and transfer learning for learning latent expression labels of child facial expression images
	Gender Classification Using Gait Kinematics
	Generalized object recognition using deep recurrent models
	Analysis and Recognition of 3D Facial Expressions in Pose Variations
	Non-intrusive optical imaging of face to probe physiological traits in Autism Spectrum Disorder
	Robot navigation and object detection/tracking
	CSRN Maze Traversal with EKF

 

Biomedical Imaging and Signal Analysis

	Joint Modeling of RNASEQ and Radiomics Data for Glioma Molecular Characterization and Prediction
	Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features
	Glioma Outcome Prediction And Survival Analysis
	Neurophysiologic Analysis of Human Visual Processing and Interfacing for Automated Robot Navigation
	Improved Brain Tumor Growth Prediction and Segmentation in Longitudinal Brain MRI
	Modeling and Fusion of Atlas-Based Tumor Growth and Feature-Based Models for Normal and Abnormal Brain Tissue Segmentation
	Automatic Segmentation of Brain Tumor and Lesions in MR Images
	Deep Recurrent Neural Network for Seizure Detection

 

Environmental and Geosciences Applications

	Efficient Segmentation of Real-Time Traffic Video for Gas Emissions Estimation
	Sensing and Classification of Aerosols in LiDAR Scattering Plots in the Atmosphere
	Shoreline Detection and Mapping Using DEMs Data and Aerial Photos