Antonio Valerio Netto

Machine learning techniques for ocular errors analysis

The conventional techniques for refractive error measurements (myopia, hypermetropia, and astigmatism) have been considered inadequate for several optometry researches. In this context, they have investigated alternative methodologies for refractive error measurement. A new strategy is the determination of refractive errors from images of the globe of the eye. A process named Hartmann-Shack can obtain these images. The HS images should be analysed in order to extract relevant information for identification of refractive errors. The present paper investigates a technique based on radial basis functions (RBFs), an artificial neural network (ANN), and on support vector machines (SVMs), which automatically performs analysis of images from the globe of the eye and identifies refractive errors. The most relevant data of these images are extracted using Gabor wavelets transform, and then these machine learning techniques carry out the image analysis

Using neural nets to measure ocular refractive errors: a proposal

We propose the development of a functional system for diagnosing and measuring ocular refractive errors in the human eye (astigmatism, hypermetropia and myopia) by automatically analyzing images of the human ocular globe acquired with the Hartmann-Schack (HS) technique. HS images are to be input into a system capable of recognizing the presence of a refractive error and outputting a measure of such an error. The system should pre-process and image supplied by the acquisition technique and then use artificial neural networks combined with fuzzy logic to extract the necessary information and output an automated diagnosis of the refractive errors that may be present in the ocular globe under exam.

Virtual Reality for Machine Tool Prototyping

We outline a procedure for implementing a virtual CNC lathe prototype using software for creating virtual environments. The prototype constructed focus on the lathe’s interlocking system (its functionality) and on its geometric model (its physical design). This project allowed us to identify the possibilities and limitations of applying current Virtual Reality technology for virtual prototyping of manufacturing machines, and evaluate the complexity associated to product prototyping in manufacturing or assembly. In additional, we suggest the possibility that this new approach can use for three areas: Training, Marketing/Sale and Product development.Copyright

DETERMINAÇÃO DE VÍCIOS REFRATIVOS OCULARES UTILIZANDO SUPPORT VECTOR MACHINES

The article introduces a new image analysis approach for measuring refractive errors in the human eye (myopia, hypermetropia and astigmatism) using Machine Learning techniques. These refractive errors are identified through the analysis of images of the eye obtained with a specific technique known as Hartmann-Shack (or Shack-Hartmann), which are preprocessed with histogram analysis considering spatial and geometrical information on the application domain. Afterwards, feature vectors are extracted using two techniques: Principal Component Analysis and Gabor Wavelets Transform. Finally, the dataset with the extracted feature vectors is analyzed using Support Vector Machines. In spite of the limitations of the image dataset, encouraging results were obtained, suggesting the potential of the proposed approach in Optometry/Ophthalmology.

CLASSIFIER COMBINATION APPLIED FOR UNDERSTANDING OF EYES IMAGES

The article presents the development of a Classifiers Combination based on machine learning techniques (Artificial Neural Networks, Support Vector Machines and C4.5 algorithm) which were able to increase the performance achieved by the Refractive Errors Measurement System (REMS) that analyzes Hartmann-Shack (HS) images from human eyes. The HS images are analyzed in order to extract relevant data for identification of refractive errors (myopia, hypermetropia and astigmatism). Those data are extracted using Gabor wavelets transform and afterwards, machine learning techniques are employed to carry out the image analysis.

Image processing and analysis using neural networks for optometry area

In this work we describe the framework of a functional system for processing and analyzing images of the human eye acquired by the Hartmann-Shack technique (HS), in order to extract information to formulate a diagnosis of eye refractive errors (astigmatism, hypermetropia and myopia). The analysis is to be carried out using an Artificial Intelligence system based on Neural Nets, Fuzzy Logic and Classifier Combination. The major goal is to establish the basis of a new technology to effectively measure ocular refractive errors that is based on methods alternative those adopted in current patented systems. Moreover, analysis of images acquired with the Hartmann-Shack technique may enable the extraction of additional information on the health of an eye under exam from the same image used to detect refraction errors.