Gupta, S., Girshick, R., Arbeláez, P. and Malik, J. (2014) Learning Rich Features from RGB-D Images for Object Detection and Segmentation. European Conference on Computer Vision 2014, Zurich, 6-12 September 2014, 345-360. https://doi.org/10.1007/978-3-319-10584-0_23

has been cited by the following article:

• TITLE: Shape Descriptor Based on Curvature

  AUTHORS: Julio-Alejandro Romero-González, Marcela Herrera-Navarro, Hugo Jiménez-Hernández

  KEYWORDS: Curvature, Smooth Curves, Shape Descriptor, Clustering

  JOURNAL NAME: Open Access Library Journal, Vol.9 No.3, March 28, 2022

  ABSTRACT: Nowadays, the applications developed in the imaging area have focused on image measurement and compression, facial recognition, blurring, or image enhancement, to name a few. Among these activities, shape descriptors play an important role in describing the topology of an object and determining its general and local characteristics. Despite the large number of studies carried out, the idea of a robust shape descriptor remains a difficult problem, the challenges that hinder shape recognition are due to transformations such as rotations, scaling, deformations caused by noise or occlusions. This article presents the development of a descriptor based on the use of curvature, with this procedure, the dimensionality of the data is reduced so that the variability of the shapes can be described or explained by means of characteristics, and through neural networks associates these features and thus discriminates one type of object from another.