A Fast Method to Segment Images with Additive Intensity Value

Master'sMASTER OF SCIENC

Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation

Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset – a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation quality of the proposed method are better than ones of the compared methods.This research was funded by University of Economics Ho Chi Minh City, Vietnam

A tight frame algorithm in image inpainting.

Cheng, Kei Tsi Daniel.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 45-49).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 2 --- Background Knowledge --- p.6Chapter 2.1 --- Image Restoration using Total Variation Norm --- p.6Chapter 2.2 --- An Example of Tight Frame system --- p.10Chapter 2.3 --- Sparse and compressed representation --- p.13Chapter 2.4 --- Existence of minimizer in convex analysis --- p.16Chapter 3 --- Tight Frame Based Minimization --- p.18Chapter 3.1 --- Tight Frames --- p.18Chapter 3.2 --- Minimization Problems and Algorithms --- p.19Chapter 3.3 --- Other Minimization Problems --- p.22Chapter 4 --- Algorithm from minimization problem 3 --- p.24Chapter 5 --- Algorithm from minimization problem 4 --- p.28Chapter 6 --- Convergence of Algorithm 2 --- p.31Chapter 6.1 --- Inner Iteration --- p.31Chapter 6.2 --- Outer Iteration --- p.33Chapter 6.2.1 --- Existence of minimizer --- p.33Chapter 7 --- Numerical Results --- p.37Chapter 8 --- Conclusion --- p.4

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Deep learning y big data en cartografía digital. Creación de inteligencias artificiales para el tratamiento de ortofotografías y sistemas de información geográfica tridimensionales

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Filosofía y Letras. Departamento de Geografía. Fecha de Lectura: 16-07-202