Sum and Difference Histograms for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Measures for Carpet Wear Assessment
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks
Machine Learning
Machine Learning
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-Organizing Maps
Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Experiments in colour texture analysis
Pattern Recognition Letters
Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Classification of honeybee pollen using a multiscale texture filtering scheme
Machine Vision and Applications
Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics
Computer Vision and Image Understanding
Supervised texture classification by integration of multiple texture methods and evaluation windows
Image and Vision Computing
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Overview of advanced computer vision systems for skin lesions characterization
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Invariant texture classification for biomedical cell specimens via non-linear polar map filtering
Computer Vision and Image Understanding
Dominant local binary patterns for texture classification
IEEE Transactions on Image Processing
Comparison and fusion of multiresolution features for texture classification
Pattern Recognition Letters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Modern Applied Statistics with S
Modern Applied Statistics with S
Review article: Automated fabric defect detection-A review
Image and Vision Computing
Statistical and wavelet based texture features for fish oocytes classification
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A review on automatic image annotation techniques
Pattern Recognition
Multiple Resolution Texture Analysis and Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic system for quality-based classification of marble textures
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatic detection and classification of grains of pollen based on shape and texture
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Texture information in run-length matrices
IEEE Transactions on Image Processing
Fractal Dimension of Color Fractal Images
IEEE Transactions on Image Processing
Hi-index | 0.01 |
The estimation of fecundity and reproductive cells (oocytes) development dynamic is essential for an accurate study of biology and population dynamics of fish species. This estimation can be developed using the stereometric method to analyse histological images of fish ovary. However, this method still requires specialised technicians and much time and effort to make routinary fecundity studies commonly used in fish stock assessment, because the available software does not allow an automatic analysis. The automatic fecundity estimation requires both the classification of cells depending on their stage of development and the measurement of their diameters, based on those cells that are cut through the nucleous within the histological slide. Human experts seem to use colour and texture properties of the image to classify cells, i.e., colour texture analysis from the computer vision point of view. In the current work, we provide an exhaustive statistical evaluation of a very wide variety of parallel and integrative texture analysis strategies, giving a total of 126 different feature vectors. Besides, a selection of 17 classifiers, representative of the currently available classification techniques, was used to classify the cells according to the presence/absence of nucleous and their stage of development. The Support Vector Machine (SVM) achieves the best results for nucleous (99.0% of accuracy using colour Local Binary Patterns (LPB) feature vector, integrative strategy) and for stages of development (99.6% using First Order Statistics and grey level LPB, parallel strategy) with the species Merluccius merluccius, and similar accuracies for Trisopterus luscus. These results provide a high reliability for an automatic fecundity estimation from histological images of fish ovary.