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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 specialized 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 1 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.

Parallel perceptrons (PPs) are very simple and efficient committee machines (a single layer of perceptrons with threshold activation functions and binary outputs, and a majority voting decision scheme), which nevertheless behave as universal approximators. The parallel delta (P-Delta) rule is an effective training algorithm, which, following the ideas of statistical learning theory used by the support vector machine (SVM), raises its generalization ability by maximizing the difference between the perceptron activations for the training patterns and the activation threshold (which corresponds to the separating hyperplane). In this paper, we propose an analytical closed-form expression to calculate the PPs' weights for classification tasks. Our method, called Direct Parallel Perceptrons (DPPs), directly calculates (without iterations) the weights using the training patterns and their desired outputs, without any search or numeric function optimization. The calculated weights globally minimize an error function which simultaneously takes into account the training error and the classification margin. Given its analytical and noniterative nature, DPPs are computationally much more efficient than other related approaches (P-Delta and SVM), and its computational complexity is linear in the input dimensionality. Therefore, DPPs are very appealing, in terms of time complexity and memory consumption, and are very easy to use for high-dimensional classification tasks. On real benchmark datasets with two and multiple classes, DPPs are competitive with SVM and other approaches but they also allow online learning and, as opposed to most of them, have no tunable parameters.

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