2,942 research outputs found
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
This paper presents a novel unsupervised domain adaptation method for
cross-domain visual recognition. We propose a unified framework that reduces
the shift between domains both statistically and geometrically, referred to as
Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two
coupled projections that project the source domain and target domain data into
low dimensional subspaces where the geometrical shift and distribution shift
are reduced simultaneously. The objective function can be solved efficiently in
a closed form. Extensive experiments have verified that the proposed method
significantly outperforms several state-of-the-art domain adaptation methods on
a synthetic dataset and three different real world cross-domain visual
recognition tasks
Flame Detection for Video-based Early Fire Warning Systems and 3D Visualization of Fire Propagation
Early and accurate detection and localization of flame is an essential requirement of modern early fire warning systems. Video-based systems can be used for this purpose; however, flame detection remains a challenging issue due to the fact that many natural objects have similar characteristics with fire. In this paper, we present a new algorithm for video based flame detection, which employs various spatio-temporal features such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. Various background subtraction algorithms are tested and comparative results in terms of computational efficiency and accuracy are presented. Experimental results with two classification methods show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio. Finally, a 3D visualization tool for the estimation of the fire propagation is outlined and simulation results are presented and discussed.The original article was published by ACTAPRESS and is available here: http://www.actapress.com/Content_of_Proceeding.aspx?proceedingid=73
Multi-View Face Recognition From Single RGBD Models of the Faces
This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks
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