The goal of multi-frame Super Resolution (SR) is to fuse multiple Low Resolution (LR) images to produce one High Resolution (HR) image. The major challenge of classic SR approaches is accurate motion estimation between the frames. To handle this challenge, fuzzy motion estimation method has been proposed that replaces value of each pixel using the weighted averaging all its neighboring pixels i...

Introduction: The limited resolution of the imaging system causes partial volume effects (PVEs). These results in spreading of image counts to the neighboring pixels. This phenomenon is called spill-out effect. This study aimed at quantifying PVEs using ImageJ. Methods:Technetium-99m solution of concentration of 74 kBq/ml was filled into spheres A, B<...

In this paper, a new learning method is proposed to build Support Vector Machines (SVM) Binary Decision Function (BDF) of reduced complexity, efficient generalization and using an adapted hybrid color space. The aim is to build a fast and efficient SVM classifier of pixels. The Vector Quantization (VQ) is used in our learning method to simplify the training set. This simplification step maps pi...

The rational design of photonic nanostructures consists of anticipating their optical response from systematic variations of simple models. This strategy, however, has limited success when multiple objectives are simultaneously targeted, because it requires demanding computational schemes. To this end, evolutionary algorithms can drive the morphology of a nano-object towards an optimum through ...

In this paper, we describe a new approach for color texture feature extraction and selection. We define color texture features as texture features which are computed by taking into account the color components of the pixels. We determine the most discriminating color texture features among a multidimensional set of color texture features by means of an iterative feature selection procedure asso...

Modern precision agriculture applications increasingly rely on stable computer vision outputs. An important task is to discriminate between soil and plant pixels, which called segmentation. For this task, supervised learning techniques, such as decision tree classifiers (DTC), support vector machines (SVM), or artificial neural networks (ANN) are increasing in popularity. The selection of train...