We demonstrate a novel approach to modelling arbitrary temporally-deforming objects using spatio-temporal Fourier descriptors. This is a continuous boundary descriptor, which can ...
Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome fo...
Anelia Angelova, Yaser S. Abu-Mostafa, Pietro Pero...
In this paper we propose and evaluate an algorithm that learns a similarity measure for comparing never seen objects. The measure is learned from pairs of training images labeled ...
Abstract. This paper describes a color region-based approach to motion estimation in color image sequences. The system is intended for robotic and vehicle guidance applications whe...
In this paper, we proposed a neural network based scheme for performing unsupervised video object segmentation, especially for videophone or videoconferencing applications. The pr...
Anastasios D. Doulamis, Nikolaos D. Doulamis, Stef...