Semantic based image retrieval: a probabilistic approach
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
On the Choice of Band-Pass Quadrature Filters
Journal of Mathematical Imaging and Vision
Detecting urbanization changes using SPOT5
Pattern Recognition Letters - Special issue: Pattern recognition in remote sensing (PRRS 2004)
Attribute-space connectivity and connected filters
Image and Vision Computing
Rotation-invariant and scale-invariant Gabor features for texture image retrieval
Image and Vision Computing
Evaluation of the effects of Gabor filter parameters on texture classification
Pattern Recognition
Semantic content analysis and annotation of histological images
Computers in Biology and Medicine
Foliage Recognition Based on Local Edge Information
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
IEICE - Transactions on Information and Systems
Non-rigid Image Registration with Uniform Spherical Structure Patterns
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
Novel approach for rotation invariant texture recognition
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
An efficient combination of texture and color information for watershed segmentation
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
A computational model for boundary detection
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A new fuzzy segmentation approach based on S-FCM type 2 using LBP-GCO features
Image Communication
A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images
Journal of Systems and Software
Pattern Recognition Letters
| Hi-index | 0.01 |
Texture is an important part of the visual world of animals and humans and their visual systems successfully detect, discriminate, and segment texture. Relatively recently progress was made concerning structures in the brain that are presumably responsible for texture processing. Neurophysiologists reported on the discovery of a new type of orientation selective neuron in areas V1 and V2 of the visual cortex of monkeys which they called grating cells. Such cells respond vigorously to a grating of bars of appropriate orientation, position and periodicity. In contrast to other orientation selective cells, grating cells respond very weakly or not at all to single bars which do not make part of a grating. Elsewhere we proposed a nonlinear model of this type of cell and demonstrated the advantages of grating cells with respect to the separation of texture and form information. In this paper, we use grating cell operators to obtain features and compare these operators in texture analysis tasks with commonly used feature extracting operators such as Gabor-energy and co-occurrence matrix operators. For a quantitative comparison of the discrimination properties of the concerned operators a new method is proposed which is based on the Fisher (1923) linear discriminant and the Fisher criterion. The operators are also qualitatively compared with respect to their ability to separate texture from form information and their suitability for texture segmentation