Mission

The Machine and Neuromorphic Perception Laboratory (a.k.a. kLab) in the Chester F. Carlson Center for Imaging Science at the Rochester Institute of Technology (RIT) applies machine learning techniques to solve problems in computer vision. The lab also studies vision in humans and other primates as a source of principles that can be used to create brain-inspired algorithms. kLab is part of RIT's Multidisciplinary Vision Research Laboratory (MVRL).

Recent projects have included algorithms for visual question answering, Gnostic Fields for classification, deep learning, new methods for eye movement analysis, saliency algorithms, perception systems for autonomous ships, algorithms for top-down and bottom-up saliency, tracking in video using neural networks, active vision algorithms, and feature learning in hyperspectral imagery.

 

Research Topics & Selected Publications

Deep & Self-Taught Learning - Our lab members were early pioneers in studying self-taught feature learning, and we heavily use deep learning.

  • Kanan, C. and Kafle, K. (2016) Answer-Type Prediction for Visual Question Answering. In: Proc IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2016).
  • Wang, P., Cottrell, G., and Kanan, C. (2015) Modeling the Object Recognition Pathway: A Deep Hierarchical Model Using Gnostic Fields. In: Proc. 36th Annual Conference of the Cognitive Science Society (CogSci-2015).
  • Kanan, C. (2013) Active Object Recognition with a Space-Variant Retina. ISRN Machine Vision, 2013: 138057. doi:10.1155/2013/138057
  • Kanan, C. and Cottrell, G.W. (2010) Robust classification of objects, faces, and flowers using natural image statistics. In: Proc IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2010).
  • Kanan, C., Tong, M.H., Zhang, L. and Cottrell, G.W. (2009) SUN: Top-down saliency using natural statistics. Visual Cognition, 17:979-1003.

Brain-Inspired Computer Vision - Much of our work is inspired by the primate visual system. Our lab was the first to implement a computational model for Gnostic Fields, a theory in neuroscience, and they achieved state-of-the-art accuracy on object recognition, odor classification, and music classification tasks.

  • Yousefhussien, M., Browning, N.A., and Kanan, C. (2016) Online Tracking using Saliency. In: Proc. IEEE Winter Applications of Computer Vision Conference (WACV-2016).
  • Wang, P., Cottrell, G., and Kanan, C. (2015) Modeling the Object Recognition Pathway: A Deep Hierarchical Model Using Gnostic Fields. In: Proc. 36th Annual Conference of the Cognitive Science Society (CogSci-2015).
  • Kanan, C. (2014) Fine-Grained Object Recognition with Gnostic Fields. IEEE Winter Applications of Computer Vision Conference (WACV-2014). doi:10.1109/WACV.2014.6836122
  • Khosla, D., Huber, D.J., and Kanan, C. (2014) A Neuromorphic System for Visual Object Recognition. Biologically Inspired Cognitive Architectures, 8: 33-45.
  • Kanan, C. (2013) Recognizing Sights, Smells, and Sounds With Gnostic Fields. PLoS ONE, 8(1): e54088. doi:10.1371/journal.pone.0054088

Active Computer Vision - Motivated by human eye movements, we built computer vision algorithms that sequentially sample images with simulated eye movements to recognize objects in scenes.

Human Eye Movements - People make 180,000 eye movements per day. We have developed algorithms for predicting what a person is doing from their eye movements and saliency models for predicting where a person will look in an image.

  • Kanan, C., Bseiso, D., Ray, N., Hsiao, J., and Cottrell, G. (2015) Humans Have Idiosyncratic and Task-specific Scanpaths for Judging Faces. Vision Research. doi:10.1016/j.visres.2015.01.013
  • Kanan, C., Ray, N.A., Bseiso, D.N.F., Hsiao, J.H., Cottrell, G.W. (2014) Predicting an observer's task using Multi-Fixation Pattern Analysis. In Proceedings of the Annual Eye Tracking Research & Applications Symposium (ETRA 2014), March, 24-26, Safety Harbor, FL.
  • Kanan, C., Tong, M.H., Zhang, L. and Cottrell, G.W. (2009) SUN: Top-down saliency using natural statistics. Visual Cognition, 17:979-1003.
 

Current Lab Members

Dr. Christopher Kanan

Lab Director & Principal Investigator

Machine Learning, Computer Vision, Theoretical Neuroscience/Psychology

Mohammed Yousefhussien

Imaging Science Ph.D. Student

Deep Learning, Tracking

Kushal Kafle

Imaging Science Ph.D. Student

Deep Learning, Visual Question Answering

Ronald Kemker

Imaging Science Ph.D. Student

Feature Learning, Hyperspectral Imagery, Remote Sensing

Megan Iafrati

Imaging Science Ph.D. Student

Feature Learning, Saliency,
Hyperspectral Imagery, Remote Sensing

Arjun Raj Rajanna

Electrical Engineering M.S. Student

Deep Learning, Image Recognition,
Image Popularity Prediction

Justin Namba

CIT B.S. Student

Computer Vision, Machine Learning

Ramesh Nair

Electrical Engineering M.S. Student

Deep Learning

Utkarsh Deshmukh

Electrical Engineering M.S. Student

Visual Question Answering, Deep Learning