Christopher Kanan's Web Page

Refereed Publications

6.  Kanan, C. (2013). Recognizing Sights, Smells, and Sounds Using Gnostic Fields. PLOS ONE: e54088. doi:10.1371/journal.pone.0054088  

Key Words: Stimulus Classification, Sound Classification, electronic nose, Image Recognition, Computer Vision
Summary: I develop a new kind of "localist" neural network called a gnostic field that is easy to implement as well as being fast to train and run. The model is tested on its ability to classify images (Caltech-256 and CUB-200), musical artists, and odors. Gnostic fields exceeded the best methods across modalities and datasets.
5.  Birmingham, E., Meixner, T., Iarocci, G., Kanan, C., Smilek, D., & Tanaka, J. (2012) The Moving Window Technique: A Window into Age-Related Changes in Attention to Facial Expressions of Emotion. Child Development. doi:10.1111/cdev.12039

Key Words: Face Processing, Emotion Recognition
Summary: We develop a new computer mouse-driven technique for assessing attention, and the approach is used in a developmental study of facial expression recognition.
4.  Kanan, C. & Cottrell, G. W. (2012). Color-to-Grayscale: Does the Method Matter in Image Recognition? PLOS ONE, 7(1): e29740. doi:10.1371/journal.pone.0029740. 

Key Words: Color-to-grayscale, Image Recognition, Computer Vision
Summary: We tested 13 color-to-grayscale algorithms in a modern descriptor based image recognition framework with 4 feature types: SIFT, SURF, Geometric Blur, and Local Binary Patterns (LBP). We discovered that the method can have a significant influence on performance, even when using robust features.
3.  Kanan, C. & Cottrell, G. W. (2010). Robust Classification of Objects, Faces, and Flowers Using Natural Image Statistics. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010

Key Words: Object Recognition, Active Vision, Eye Movements, Computational Neuroscience
Summary: We used simulated eye movements with a model of V1 to achieve state-of-the-art results as of early 2010 on a number of challenging datasets in computer vision.
[Project Webpage] [MATLAB Demo] [MATLAB Code for Experiments] [Supplementary Materials]
2.  Kanan, C., Flores, A., & Cottrell, G. W. (2010). Color Constancy Algorithms for Object and Face Recognition. International Symposium on Visual Computing (ISVC 2010)

Key Words: Object Recognition, Computer Vision
Summary: We examine the performance of color constancy algorithms in this paper. Our later work on color-to-grayscale algorithms is substantially more rigorous.
1.  Kanan, C., Tong, M. H., Zhang, L., & Cottrell, G. W. (2009). SUN: Top-down saliency using natural statistics. Visual Cognition, 17: 979-1003.

Key Words: Attention, Active Vision, Eye Movements, Computational Psychology
Summary: We modeled task-driven visual search, and demonstrated that appearance is predictive of human fixation locations.
[Project Webpage]


1.  Khosla, D., Kanan, C., Huber, D., Chelian, S., & Srinivasa, N. (2012) Visual Attention and Object Recognition System. U.S. Patent No. 8,165,407. Washington, DC: U.S.

Key Words: Image Recognition, Brain-Inspired Computer Vision
Summary: At HRL Laboratories, my colleagues and I invented a system that combines a model of visual attention with a brain-inspired model of object recognition, which sequentially recognized objects in images.