Semi-Automated Stereo Image Patches Generation and Labeling Method Based on Perspective Transformations

• Autores: Durante, Diego Patricio; Verrastro, Ramiro; Gómez, Juan Carlos; Verrastro, Claudio Abel
• Año de publicación: 2022
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Ciencias Informáticas; Computer Vision; Machine Learning; Wide Baseline Stereo; Labeling Tool; Siamese Convolutional Neural Networks
• Institución: Universidad Nacional de La Plata

In computer vision, Wide Baseline Stereo (WxBS) refers to Vision System configurations on which their images come from cameras with non parallel and widely separated views. One common task in reconstruction algorithms of WxBS consists of subvididing the stereo images in multiple image patches and then associate homologous patches between homologous images. Multiple approaches can be used to associate homologous patches. To train and test supervised learning algorithms for this tasks, a labeled dataset is required. In this work, a semi-automated method to generate patches and their labels from WxBS images is presented. It allows to calculate thousands of positive and negative pairs of patches with a score of correspondence between a pair of potentially homologous image patches. This method largely solves the problems of traditional approach, which requires a lot of hand labeled work and time. To apply the method, images from different viewpoints of objects with planar faces and their corner locations are required.
Sociedad Argentina de Informática e Investigación Operativa