| 4. | |
Kanan, C. & Cottrell, G. W. (2012). Color-to-Grayscale: Does the Method Matter in Image Recognition? PLoS ONE: 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]
|