Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organized Feature Extraction Achieved with a Parameterized Filterbank
Neural Processing Letters
Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
A Neural 3-D Object Recognition Architecture Using Optimized Gabor Filters
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Gabor Filter Analysis for Texture Segmentation
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Eye Tracking Methodology: Theory and Practice
Eye Tracking Methodology: Theory and Practice
Plant Leaf Identification Using Multi-scale Fractal Dimension
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Match-moving for area-based analysis of eye movements in natural tasks
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Robust face detection using Gabor filter features
Pattern Recognition Letters
Growing self-reconstruction maps
IEEE Transactions on Neural Networks
Modification of the growing neural gas algorithm for cluster analysis
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Leaf recognition based on the combination of wavelet transform and gaussian interpolation
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Comparison of texture features based on Gabor filters
IEEE Transactions on Image Processing
Fast anisotropic Gauss filtering
IEEE Transactions on Image Processing
Hi-index | 12.05 |
Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves.