A Compact Sift-Based Strategy for Visual Information Retrieval in Large Image Databases

Abstract

This paper applies the Standard Scale Invariant Feature Transform (S-SIFT) algorithm to accomplish the image descriptors of an eye region for a set of human eyes images from the UBIRIS database despite photometric transformations. The core assumption is that textured regions are locally planar and stationary. A descriptor with this type of invariance is sufficient to discern and describe a textured area regardless of the viewpoint and lighting in a perspective image, and it permits the identification of similar types of texture in a figure, such as an iris texture on an eye. It also enables to establish the correspondence between texture regions from distinct images acquired from different viewpoints (as, for example, two views of the front of a house), scales and/or subjected to linear transformations such as translation. Experiments have confirmed that the S-SIFT algorithm is a potent tool for a variety of problems in image identification.

Breast cancer classification using machine learning techniques: a comparative study

“…It uses the maximum likelihood method for parameter estimation and is appropriate for high dimensionality inputs. Equation (2) gives the probability of a class given predictor ( | ), where ( | ) is posterior probability, ( ) is the class prior probability, ( | ) is the likelihood, and ( ) is the probability of predictor [ 4]:…”

“…In the literature, several algorithms for breast cancer diagnosis and prognosis are proposed. In this paper we provide a practical comparison between kernel and linear support vector machines (K-SVM, L-SVM respectively), random forest(RF), decision tree (DTs), multi-layer perceptron (MLP), logistic regression (LR), and k-nearest neighbors (k-NN) which are the most used algorithms in several researches [2][3][4]. The goal of this study is to evaluate the performance of these algorithms in terms of effectiveness, efficiency and accuracy,.…”

The Axis “Human Papillomavirus - Anal Squamous Cell Carcinoma”: A Review

“…Robotics will also help advances in 3D and 4D imaging with different types of cameras, augmented reality options, image processing techniques, and 3D printing [65][66][67][68]. Advances in databases will also impact ASCC theragnostic [69].…”