A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking.

This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search.

A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification.

Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder-decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures.

Fast Deep Stacked Networks based on Extreme Learning Machine applied to regression problems.

Deep learning techniques are commonly used to process large amounts of data, and good results are obtained in many applications. Those methods, however, can lead to long training times. An alternative to simultaneously tune all parameters of a large network is to stack smaller modules, improving the model efficiency.

A Soft-Threshold Filtering Approach for Tomography Reconstruction from a Limited Number of Projections with Bilateral Edge Preservation.

In X-ray tomography image reconstruction, one of the most successful approaches involves a statistical approach with l 2 norm for fidelity function and some regularization function with l p norm, 1 < p < 2 . Among them stands out, both for its results and the computational performance, a technique that involves the alternating minimization of an objective function with l 2 norm for fidelity and a regularization term that uses discrete gradient transform (DGT) sparse transformation minimized by total variation (TV). This work proposes an improvement to the reconstruction process by adding a bilateral edge-preserving (BEP) regularization term to the objective function.

An incremental neural network with a reduced architecture.

This paper proposes a technique, called Evolving Probabilistic Neural Network (ePNN), that presents many interesting features, including incremental learning, evolving architecture, the capacity to learn continually throughout its existence and requiring that each training sample be used only once in the training phase without reprocessing. A series of experiments was performed on data sets in the public domain; the results indicate that ePNN is superior or equal to the other incremental neural networks evaluated in this paper. These results also demonstrate the advantage of the small ePNN architecture and show that its architecture is more stable than the other incremental neural networks evaluated.